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The population structure of differential evolution (DE) algorithm cannot maintain the diversity of the population to the greatest extent and help the population avoid to fall into the local optima in time. In this paper, a co-evolutionary multi-swarm adaptive differential evolution algorithm, namely ECMADE is proposed to solve the premature convergence and search stagnation. First of all, in terms of population structure, based on the parallel distributed framework, ECMADE randomly and evenly divides the population into exploration subpopulation, development subpopulation, and auxiliary subpopulation, and introduces an adaptive information exchange mechanism so that subpopulations can escape local optima in time. Then, a multi-operator parallel search strategy is proposed to keep population diversity and meet the optimization needs of different problems. Finally, an adaptive adjustment mechanism of control parameters is developed, through recent elite parameter archive and weight distribution to fully mine successful parameter information, and generate control parameters with a high success rate for the current evolutionary stage. In order to prove the effectiveness of the ECMADE, 10 test functions and portfolio optimization problem are selected in here. The experiment results show that the ECMADE can effectively solve these test functions, the accuracy and efficiency is superior to those of two classical DE algorithms. The actual application results show that the ECMADE can significantly improve the ability of portfolio to resist extreme losses, which proves the effectiveness and feasibility of the ECMADE once again. The ECMADE has better optimization performance by comparing with some well-known algorithms in term of the solution quality, robustness and space distribution. It provides a new algorithm for solving complex optimization problems. [ABSTRACT FROM AUTHOR]

The shape optimization of interpolation curves is a crucial and intractable technique in scientific visualization, data analysis, manufacturing and computer-aided design and is potentially used in many engineering fields involving geometric modeling. In this paper, an enhanced hybrid tunicate swarm algorithm (TSA) is used to optimize the shape of complex rational quartic interpolation spline (RQI-spline, for short) curves. Firstly, to modify the shape of the interpolation curve more flexibly, a novel multi-parameter RQI-spline curve with local and global shape parameters is presented. Then the corresponding combined RQI-spline curves with 2th-order parametric continuity denoted C 2 are constructed. In addition, considering that the optimization of shape parameters for RQI-spline curves is a complex optimization problem, an enhanced hybrid version of TSA called DQTSA is developed. The introduction of differential evolution and quadratic interpolation strategies enhances the ability of the TSA to jump out of local minima, thus effectively solving the shape parameter optimization of RQI-spline curves. DQTSA and some popular comparison algorithms are used to solve the set of 23 benchmark functions and CEC2019 test functions. The experimental results show that DQTSA performs the best in 7 (30.43%) of the 23 test problems, while DQTSA performs the best in 3 of the 10 CEC2019 suites. Meanwhile, applying DQTSA to three engineering optimization problems, the best value, mean and significantly better than other search algorithms are obtained from 20 optimized designs, which verifies the applicability and stability of DQTSA in solving engineering optimization problems. Finally, the proposed DQTSA is used to solve the shape optimization model for the combined C 2 RQI-spline curve. Numerical results find that DQTSA obtains the optimal RQI-spline curve with minimum energy. Some representative numerical examples illustrate that DQTSA is a potentially excellent algorithm for solving curve optimization. • A novel C2 rational quartic interpolation spline (RQI-spline, for short) curves are constructed. • An enhanced tunicate swarm algorithm called DQTSA is presented. • The superiority of DQTSA is verified by comparing with popular intelligent algorithms. • Shape optimization model of C2 RQI-spline curves is established. • The DQTSA algorithm is applied to the solutions of the established optimization model. [ABSTRACT FROM AUTHOR]

• High dimensional complex distribution system increases the amount of calculation and computational complexity of in the process of economic dispatch. • A large number of microgrids connected to the distribution network system increases the complexity of the system. • The computational dimension and complexity can be reduced by layering or decomposition system. • The efficiency of network loss calculation can be improved by utilizing the network loss sensitivity to calculate network loss. With the development of the active distribution system with multi-microgrids, increasingly numbers of distributed energy resources are integrated into the distribution network through multi-microgrids. As a result, the economic dispatch problem presents high dimensional complexity. Hence, this paper proposes a decomposition and coordination calculation method to reduce the computational dimension and improve the calculation accuracy. In this paper, the active distribution system with multi-microgrids is divided into two parts: one is the outer power grid and the other is the inner power grid, and the optimal value is obtained by applying the differential evolution algorithm. To improve the efficiency of the traditional network loss calculation, the network loss sensitivity is adopted to calculate the network loss. In order to verify the effectiveness of the proposed method, the test model is constructed based on the IEEE 33 node and 69 node distribution systems. The simulation results show that the proposed method can obtain the global optimal solution, and its economy is better than the overall centralized calculation method. This method is conducive to the subsequent application in the actual power grid system. [ABSTRACT FROM AUTHOR]

Multimodal multiobjective optimization problems (MMOPs) possess multiple equivalent Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). Numerous multimodal multiobjective evolutionary algorithms (MMEAs) have been developed to solve MMOPs. However, how to locate multiple high quality equivalent PSs and obtain a uniformly distributed PF simultaneously remains a challenging task. This paper explores a novel multimodal multiobjective differential evolution algorithm, termed MMO_CDE, for solving MMOPs. In MMO_CDE, a neighborhood-based dual mutation strategy is proposed by developing two novel mutation operators. The two novel mutation operators provide a better tradeoff between exploration and exploitation in locating multiple equivalent PSs. In addition, a clustering-based environmental selection mechanism is designed by integrating an affinity propagation method. The affinity propagation method improves the distribution of solutions in decision and objective spaces, and a harmonic averaged distance is adopted to promote the diversity of a population. The proposed MMO_CDE is compared against state-of-the-art MMEAs on a test suite of CEC 2019 benchmark functions and a map-based practical application. Empirical results validate the superiority of the proposed MMO_CDE regarding locating multiple equivalent PSs and obtaining a well-distributed PF. • Propose a clustering differential evolution with neighborhood-based dual mutation. • Develop a neighborhood-based dual mutation for the proposed algorithm. • Develop a clustering-based environmental selection for the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved version of JADE by h ybridizing the JADE algorithm with a G aussian t ail, a modified hunger games search (HGS) algorithm, and a distance-based multi-population (DbMP) approach named as HJADE-GT. In the proposed algorithm, two sets of operators (modified HGS operator and JADE operator with Gaussian tail) are utilized to generate offspring to further enhance the exploration and exploitation abilities. DbMP approach is proposed to make full use of feedback information from the whole population. In HJADE-GT, the main population is divided into three fixed-size subpopulations: exploration subpopulation, balanced subpopulation, and exploitation subpopulation. Secondly, a modified HGS operator is incorporated into the exploration subpopulation to improve global searchability. Thirdly, the JADE operator with a Gaussian tail is utilized to enhance the ability of exploitation subpopulation. Finally, the DbMP approach is utilized for the balanced subpopulation to choose an appropriate operator for current individuals to make full use of feedback information from the exploration subpopulation and exploitation subpopulation. In the experimental studies, it is demonstrated that the proposed algorithm presents competitive performance with 13 well-known algorithms, including jDE, SaDE, JADE, MPEDE, SHADE, CoDE, SaJADE, iLSHADE, jSO, CMAES, MGFPA, ESSA, and PPSO on CEC2017 benchmark functions. Four engineering problems and three dynamic economic emission dispatch (DEED) problems were utilized to verify the performance of HJADE-GT, and the experiments on DEED problems confirm that HJADE-GT is an efficient algorithm to solve engineering and large-scale constrained DEED problems. [ABSTRACT FROM AUTHOR]

Driver pathways have been acknowledged to play critical roles in the initiation and progression of cancers►hence it is essential for precision medicine related studies to develop accurate and efficient methods to identify them. Although previous approaches have shown promising results by integrating multi-omics data, their preset artificial parameters may decrease the convenience to use and limit the application scalability. In this paper, a novel integration approach is proposed to incorporate four omics data, i.e., construct a weighted non-binary mutation matrix without presetting artificial parameters. A parameter-free identification model is put forward based on it. It takes advantage of the association between genes in the PPI network as well as balances the contribution of coverage and mutual exclusivity with the harmonic mean. Furthermore, a cooperative co-evolution algorithm is proposed for solving this model. In the algorithm, a particle swarm optimization algorithm suitable for solving combinatorial problems is presented. Three cooperative operators are devised to construct the cooperation among the populations and the swarm to increase the population diversity. Both real biological datasets and simulated ones were exerted to perform experimental comparisons among the proposed method and six other state-of-the-art ones. The gene sets identified by the presented method generally contain more genes involved in known signaling pathways than those obtained by the other methods. Simultaneously, both high accuracy and high efficiency of the proposed method were verified in experiments, making it practical in realistic applications and an effective supplementary tool to identify driver pathways. • A non-binary mutation matrix is constructed without artificial adjustable parameters. • The model incorporates gene association and balances coverage and mutual exclusivity. • A PSO algorithm suitable for solving combinatorial problems is presented. • A co-operative co-evolution algorithm is put forward for solving the presented model. • The performance is compared among the proposed method and seven state of the art ones. [ABSTRACT FROM AUTHOR]

• The optimal mass design of the nonlinear energy sink is proposed. • The relations between damping and optimal mass of NESs is revealed. • The advantages of mass design of the bistable nonlinear energy sink are found. Nonlinear energy sink (NES) is attracting more attention because of its promising in the control of engineering structure vibration. This paper thoroughly reviews the selection of NES mass ratios in existing studies and finds the big difference. So there is still a lack of a unified and profound understanding of the mass design of the NES. To achieve decent vibration mitigation and reduce costs, the mass design of the NES is essential. This paper pioneers the optimal mass design of NESs. The non-dominated sorting differential evolution algorithm and parameter sensitivity analysis are adopted. The relations between the optimal mass and the vibration mitigation effects are discussed in different damping and harmonic excitation strength. The results show that different from the monostable NES, the bistable NES with a small mass ratio (no more than 0.7%) and small damping can reduce 30% of the resonance peak of the primary system in most situations. However, both these two NESs with large damping need larger mass ratios to achieve decent vibration mitigation. Smaller damping of the primary system will contribute to wider effective ranges of the mass ratio of the two NESs, while larger excitation will contribute to those of the monostable NES. To ensure the vibration mitigation effects in small mass ratio, the monostable and bistable NESs with small damping need large and small cubic stiffness, respectively. Besides, the bistable NES with small damping prefers small negative linear stiffness. In summary, this paper presents a detailed study of the mass design of the nonlinear energy sink. It will help researchers and engineers find the suitable mass and design NES in the vibration reduction of engineering structures. [ABSTRACT FROM AUTHOR]

Clustering has grown to be a research focus in recent years owing to the challenges of labeling massive collected data. Recent advances such as the emotional preference and migratory behavior clustering model jointly utilized the biogeography-based idea and inertia theorem for handling data clustering tasks. However, as with most clustering methods, the initialization for the solution is often purely random. Moreover, the search results vulnerably converge to local optima due to favorable exploration but short exploitation, which requires an additional process to balance them effectively. Inspired by the differential evolution (DE) strategy and the opposition-based learning (OBL) method, the paper proposed a novelty opposition-based differential evolution clustering algorithm for emotional preference and migratory behavior optimization named OBDE-EPMC. Specifically, we first utilize the OBL method to initialize the population to obtain a closer initial individual to the optimal solution. Thereafter, to ensure population diversity during the optimization, we applied the differential evolution (DE) strategy to expand the scope of the solution and the opposition-based learning method to generate an opposite one. Lastly, the historical solution scheme is added to retain the excellent solution within the population. In addition, the theoretical analysis and convergence property proof of OBDE-EPMC are presented. Numerous experiments were performed to compare the proposed OBDE-EPMC with the other seven clustering algorithms on a host of standard datasets. And several standards demonstrate the rationality and effectiveness of our proposed OBDE-EPMC algorithm. [ABSTRACT FROM AUTHOR]

In imperialist competition algorithm (ICA), the assimilation is an important step, which is responsible for exploring potential better position or solution for an optimization problem. In this paper, an improved ICA equipped with an adaptive differential mutation (ADM) assimilation strategy was proposed inspired by differential evolution (DE). The proposed algorithm is called ICA-ADM. The ADM assimilation strategy generates new positions for colonies in an empire using the "DE/best/1" mutation operator and crossover operator with adaptively varied mutation and crossover rate. Since the "DE/best/1" mutation and crossover can produce high quality solutions, ICA-ADM possesses good exploration capability. Meanwhile, since the mutation and crossover rates are adaptively varied, ICA-ADM also preserves a good population diversity. Thus, the performance of ICA-ADM is greatly increased. Optimization results on 16 well-known benchmark functions and comparisons verify that the proposed ICA-ADM exceeds the initial ICA and its several variants. • A new assimilation method was proposed for imperialist competitive algorithm. • The method generates new position using "DE/best/1" mutation and crossover operator. • The method has better global searching capability. • The method can maintain a good population diversity. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) algorithm is popular to tackle real-world optimization problems. The standard DE, however, is not able to solve constrained optimization problems (COPs) for their complicated linear or nonlinear constraints. The transformation of constraints, which is realized via constraint handling technique, has a great impact on dealing with constrained algorithms. In this paper, a lightweight and efficient variant of DE named C ε -LDE is proposed to solve constrained single-objective optimization problems. This study firstly introduces a combined constraint handling method, which is designed based on the difference and relationship between two different ε constrained methods and balances infeasible solution and feasible solution with rules from the perspective of probability allocation. In addition, an extra control parameter is redefined in terms of different initial ε level in consideration of the complexity and diversity of COPs. On the other hand, DE evolves by opposition-based learning initialization and modifications on mutation operator and crossover rate selection, respectively. Mutation strategies frequently named rand/1 and best/1 are adopted and Lévy flight is added to the mutation strategy as a multiplier with scale factor F for a long jump. The crossover rate CR tends to be a larger or smaller stochastic value according to the pros and cons of the selected vector. A set of 28 problems under different dimension settings (D = 10, 30, 50 and 100) used for the CEC 2017 Competition is employed to evaluate the performance of the proposed C ε -LDE. The computational results demonstrate the effectiveness and superiority of the proposed combined ε constrained method. A comparative study of C ε -LDE with state-of-art algorithms is conducted on the obtained experimental results under the rules of CEC 2017 Competition. The ranking outcomes reveal that C ε -LDE is equipped with high competitiveness. Furthermore, the applications in several standard real-life engineering problems verify the effectiveness and practicability of the proposed scheme. [ABSTRACT FROM AUTHOR]

• A hybrid Machine Learning (ML) approach for an efficient wrapper feature selection is proposed for enhancing the performance and reducing the computation time of IDS while increasing the accuracy to detect the intruder in IoT networks. • The hybrid ML method consists of two techniques: Differential Evolution (DE) and Extreme Learning Machine (ELM). The DE is responsible for choosing the beneficial features while the ELM is employed to assess the selected features. • Extensive experiments are implemented to evaluate the proposed approach using different performance metrics. In addition, to verify the efficiency of proposed model, the comparison is achieved with some related existing methods such as CFS + SVM [20] , CFS + ANN [19] , GA + Bagging [18] , CFS + RF [30] , and Chi-squared + RF [30]. The intrusion detection system (IDS) has gained a rapid increase of interest due to its widely recognized potential in various security fields, however, it suffers from several challenges. Different network datasets have several redundant and irrelevant features that affect the decision of the IDS classifier. Therefore, it is essential to decrease these features to improve the system performance. In this paper, an efficient wrapper feature selection method is proposed for improving the performance and decreasing the processing time of the IDS. The proposed approach employs a differential evaluation algorithm to select the useful features whilst the extreme learning machine classifier is applied after feature selection to evaluate the selected features. Many experiments are performed using the full NSL-KDD dataset to evaluate the performance of the proposed method. The results prove that the proposed approach can efficiently reduce the features, increase the accuracy, reduce the false alarm rates, and improve the processing time of the IDS in comparison to other recent related works. [ABSTRACT FROM AUTHOR]

• Optimal foraging algorithm with direction prediction & Gaussian oscillation is built. • New algorithm includes two update methods: prediction model and random model. • Prediction model mends exploitation ability by predicting the evolutionary direction. • Random model mends exploration by exploring the space with Gaussian distribution. • The superiority of algorithm is verified on benchmark set and engineering problems. Optimal foraging algorithm (OFA) is a newly stochastic optimization technique and is famous for its computational accuracy. However, the high computational accuracy leads to slow convergence speed. Experimental results demonstrate that OFA is good at unimodal functions but poor at multimodal functions. To improve these drawbacks, in this paper a novel modified OFA with direction prediction and Gaussian oscillation, named OFA/P&G is introduced. In OFA/P&G, a transition matrix is constructed when a new global optimum is found to generate the candidate individuals. If the current global optimum does not change, the Gaussian oscillation is employed in a low probability and OFA update method is used in a high probability to generate the candidate individuals. The superior performance of OFA/P&G is verified on the 12 CEC2017 benchmark functions, 13 constrained benchmark functions and 5 engineering problems. Experimental results demonstrate that OFA/P&G outperforms other comparative algorithms. Finally, a real-world problem, drilling path optimization, is solved by OFA/P&G. [ABSTRACT FROM AUTHOR]

• Multi-objective truss structural optimization problems are proposed and solved. • Three and four conflicting Objective Functions (OF) are considered. • Weight, frequency of vibration, global stability and nodal displacements are the OF. • Surface Pareto fronts are obtained. • Solutions are extracted from the Pareto fronts by a Multi-criteria decision-making. Multi-objective structural optimization problems (MOSOPs) with two objectives are widely discussed in the literature. Most MOSOPs that refer to trusses are formulated to minimize the weight and the maximum nodal displacement. This paper formulates MOSOPs with several objective functions combined with various formulations. The objective functions are the weight, the natural frequencies of vibration, the maximum nodal displacement, and the critical load factor concerning the structure's global stability. The design variables are the cross-sectional areas of the bars, the nodal coordinates, and the presence or absence of bars in the final optimized structure. The third evolution step of generalized differential evolution (GDE3), the success history–based adaptive multi-objective differential evolution (SHAMODE) and the success history–based adaptive multi-objective differential evolution with whale optimization (SHAMODE-WO), and the multi-objective meta-heuristic with iterative parameter distribution estimation (MM-IPDE) are the differential evolution algorithms used in this paper. The experiments refer to the 10-, 25-, 56-, 72-, 120-, and 582-bar trusses and a 33-bar ground-structure system. Multi-criteria decision-making (MCDM) is adopted to extract solutions from the Pareto front according to preferences of the decision-maker (DM) used in the ground-structure system. The complete data for each extracted solution are provided, including its optimized topology. [ABSTRACT FROM AUTHOR]

Differential evolution is a classical and effective evolutionary algorithm. In recent years, many differential evolution variants have been proposed and achieved good results on many problems. To investigate their inherent characteristics, this paper uses the population interaction network. Six representative differential evolution algorithms including DE, JADE, CJADE, SHADE, L-SHADE, and EBLSHADE are analyzed from the perspective of information interaction among individuals. The cumulative distribution function of degrees of nodes obtained from the population interaction network on thirty IEEE CEC2017 benchmark functions is fitted by seven distribution models. Results show that the cumulative distribution function of differential evolution is the Poisson distribution whereas the other variants meet the Power-law distribution. The Power-law distribution influences their performance and depends on the population size. These remarkable findings suggest that the Power-law distribution widely exists in best-performing differential evolution algorithms, which gives empirical evidence for designing Power-law distribution-based differential evolution algorithms. • Population interaction networks of six differential evolution algorithms are established. • Seven distribution models are used to fit the frequency of degrees of nodes. • The frequency of degrees of nodes in classic differential evolution algorithm obeys the Poisson distribution. • The frequencies of degrees of nodes in state-of-the-art differential evolution algorithms obey the Power-law distribution. [ABSTRACT FROM AUTHOR]

The emergence of services such as high-definition video, smart home, and virtual reality has led to stringent requirements for delay jitter in 5G fronthaul network. In this paper, a novel differentiated traffic scheduling algorithm is proposed in 5G fronthaul hybrid network. It can not only eliminate the delay jitter, but also simultaneously transmit CPRI traffic and eCPRI traffic for the first time. To CPRI traffic, zero-jitter of delay is achieved by innovatively mapping the scheduling timeslots of the flow's packets into an arithmetic progression. Furthermore, the optimal flow scheduling with minimizing the delay and minimizing the number of conflicting time slots is realized by combining differential evolution (DE) with non-dominated sorting genetic algorithm II (NSGA-II). To eCPRI traffic, zero-jitter flow scheduling is performed by innovatively proposing a fuzzy time slot method. In this method, by implementing different caching policies for marked and not marked flows, zero-jitter is achieved. By controlling the upper limit of packet sending time, the delay performance is guaranteed. Finally, by the simulation, the effectiveness of the proposed algorithm is demonstrated. The simulation results indicate that the optimal flow scheduling based on this algorithm has good performance in jitter, delay and bandwidth utilization. [ABSTRACT FROM AUTHOR]

When solving constrained multiobjective optimization problems, it is crucial to deal with several conflicting objectives and various constraints simultaneously. To address these two issues, a constrained multiobjective differential evolution algorithm with an infeasible-proportion control mechanism is presented in this paper. Specifically, two populations are cooperatively employed in the evolution process. The first population is used to solve the original problem, that is, to explore the constrained Pareto front, while the second population is created to search for high-quality objective function information. Furthermore, differential evolution with two specific mutation strategies is employed to update each population. Cooperative strategies can not only balance diversity and convergence but also realize information exchange between the two populations provide new evolutionary directions. In addition, an infeasible-proportion control mechanism is used to gradually decrease the proportion of infeasible solutions in the second population. This enables the main population to pass through the infeasible region barrier in the early evolution stage and facilitates the search for a constrained Pareto front in the later evolution stage. Systematic experiments on 65 benchmark test functions show that the proposed algorithm is superior to or at least comparable to five well-established constrained multiobjective optimization methods. [ABSTRACT FROM AUTHOR]

• The proposed method can be applied for any non-linear ECM without pre-assumptions. • Parameters identification of battery was transformed into a LSOP. • A new m -decomposition method allots R 0 , R P and C P overlapped to each group. Parameters identification of battery is a significant task for lithium-ion batteries. Some widely used techniques usually simplify the electrical circuit model (ECM) with non-linearity to a linear model or local linear model. However, by using such a methodology, the parameters in ECMs are not globally optimal, since the parameters may be not consistent at different linearized points. To address this issue, this paper proposed a cooperative co-evolution differential evolution (CCDE) algorithm to identify parameters of lithium-ion battery, without any linearization or pre-assumption. First, to describe the dynamic behaviors of battery, we presented a first-order RC equivalent circuit model ECM. Without making any approximation, improved Euler's numerical method was utilized to solve the differential equations directly. Second, an optimizing objective function was built to minimize errors between the true and optimized terminal voltages. In that optimization model, parameters of battery (R 0 , R P and C P) and v OCV (t) at each sampling point were considered as variables to be optimized, resulting in a very high-dimension problem. Third, such an optimization problem was transformed into a large scale optimization problem (LSOP). Based on the character of parameters identification, we proposed a new m -decomposition method which is different from general grouping methods for benchmark functions and its corresponding differential evolution (DE) algorithm to solve this LSOP. Comprehensive experimental results demonstrated effectiveness of the proposed framework and methodology, compared with seven state-of-the-art cooperative co-evolution methods. [ABSTRACT FROM AUTHOR]

Search-based algorithms are a recent research hotspot for solving path coverage (PC), which is the most critical and challenging problem in the field of automated test case generation (ATCG). There remains a large research space for achieving ATCG-PC's goal of finding a set of test cases covering all possible paths with as little computational overhead as possible. In contrast to two existing research approaches of testing different search-based algorithms and developing different fitness functions, this paper proposes two learning strategies based on a hypercube (termed HBL and THBL), which are inspired by a problem-specific knowledge expressed by the mathematical formulas of different fitness functions for ATCG-PC. The hypercubes of HBL and THBL are developed via an opposition-based learning strategy around the current best solution. The two learning strategies can guide search-based algorithms to search for test cases that cover uncovered paths. Two improved differential evolutionary algorithms based on HBL and THBL are then proposed to solve ATCG-PC. Experimental studies on thirty instances generated by eight classical benchmark programs and six fog computing benchmark programs show that the proposed algorithms achieve highest path coverage with fewer test cases and less running time than some compared state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Land readjustment and redistribution (LR) is an important approach used to realize development plans by converting rural lands to urban land and also providing urban infrastructure. The LR problem, which is a complex challenging real-world problem, is a discrete optimization problem because its structure is similar to TSP (Traveling Salesman Problem) and scheduling problems which are combinatorial optimization problems. Since classical mathematical methods are insufficient for solving NP (Nondeterministic Polynomial) optimization problems due to time limitations, meta-heuristic optimization algorithms are commonly utilized for solving these kinds of problems. In this paper, meta-heuristic algorithms including genetic, particle swarm, differential evolution, artificial bee, and tree seed algorithms are utilized for solving LR problems. The stated meta-heuristic algorithms are used by applying spatial-based crossover and mutation operators depending upon the LR problem on each algorithm. Moreover, a synthetic dataset is used to ensure that the quality of the solution obtained is acceptable to everyone, to prove an optimal solution easily. By utilizing the suggested spatial-based crossover and mutation operators, finding the ideal solution is aimed using the synthetic dataset. In addition, five different modifications on TSA (Tree-Seed Algorithm) are performed and used to solve LR problems. All the modified versions of TSA are carried out only by changing the mechanism of seed reproduction. The novel TSA approaches are respectively named as tcTSA (tournament current), tbTSA (tournament best), pbTSA (personal-best based), t2TSA (double tournament), and elTSA (elitism based). In the experimental studies, the hybrid approach, which includes the crossover and mutation operators, is successfully applied in all of the algorithms under equal conditions for a fair comparison. According to experimental results performed using the dataset, it can be clearly stated that especially t2TSA outperforms all the algorithms in terms of performance and time. [ABSTRACT FROM AUTHOR]

The performance of photovoltaic (PV) cell is affected by the model structure and corresponding parameters. However, these parameters are adjustable and variable, which play an available role in regarding to the efficiency and effectiveness of PV generation. Due to strong non-linear characteristics, existing PV model parameters identification methods cannot easily obtain accurate solutions. To tackle this, this paper proposes an adaptive differential evolution algorithm with the dynamic opposite learning strategy (DOL), named DOLADE. In DOLADE, the opposite learning method expands the current elite population and the population of poor performance, improving the particles' exploration capability. In the process of particles work, the searching area of particles is adjusting dynamically so that the particles' exploitation capability is enhanced. The experimental data of different types of PV are tested, respectively. Three PV models are used to verify the new strategy's accuracy and effectiveness. The proposed DOLADE is compared with several general advanced algorithms, and comprehensive experimental results are demonstrated. The results illustrate that DOLADE well extracts optimal parameters for each PV cell model and brought great competition in terms of accuracy, reliability, and computational efficiency in solving the problem. • Differential evolution variant is proposed to estimate the photovoltaic models' parameter. • Dynamic opposite learning strategy aims to improve exploration and exploitation abilities. • The proposed method is tested under various experimental data and cases. • A series of experimental results indicate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Two metaheuristic algorithms for improvement of the MPPT technique are presented. • The first MPPT method is based on WOA and DE, called the WOADE-MPPT technique. • The second MPPT method, IWOADE-MPPT, is an improvement of the WOADE. • IWOADE-MPPT technique outperforms all traditional MPPT techniques. • Five various shading patterns are studied to analyze the proposed MPPT technique. The photovoltaic output power varies according to the solar radiation and ambient temperature because the operation of the photovoltaic arrays depends on them. Therefore, achieving the maximum photovoltaic power under different shading patterns is a key factor in the performance improvement of photovoltaic systems. An efficient Maximum Power Point Tracking technique is needed to distinguish the global maximum power point from the local ones, as the traditional techniques are prone to fail. This paper proposes two hybrid meta-heuristic algorithms to improve the maximum power point tracking technique of partially shaded photovoltaic systems. The first proposed maximum power point tracking technique is based on the Whale Optimization Algorithm and Differential Evolution algorithms. The second proposed maximum power point tracking technique is an improved version of the first proposed technique. Both proposed techniques are highly proficient in enhancing the photovoltaic system's efficiency in shaded and unshaded conditions. The maximum power point tracking technique is studied for evaluating the performance based on two traditional algorithms, Whale Optimization Algorithm and Differential Evolution, and two hybrid proposed algorithms. The simulation results show the second proposed maximum power point tracking technique finds the global power points faster and offers better performance than the first proposed technique, which itself outperforms two traditional maximum power point tracking techniques concerning a faster rate of convergence and higher efficiency. [ABSTRACT FROM AUTHOR]

The extremely sensitive and highly non-linear search space of interplanetary transfer trajectory design brings about big challenges on global optimization. As a representative, the current known best solution (fuel consumption) of the global trajectory optimization problem designed by the European space agency is very hard to be found. To deal with this difficulty, a powerful differential evolution with the guided movement for population, named G_DE is proposed in this paper. G_DE employs a two-stage evolutionary process, which concentrates on learning global structure in the earlier process, and tends to self-adaptively probe the structures of numerous local spaces at a later stage. During these two stages, four guidance strategies related to the learning from the population distribution are proposed. The experimental test results show that G_DE can find the current known best solutions of Cassini1 and Sagas directly. For the newly proposed GTOP-X problem, G_DE has found state-of-the-art solutions for four encounter sequences, and using five gravity assists, G_DE found encounter sequences with fuel consumption as low as 1.5676 km/s. The experimental test on 10D CEC2017 problems proves G_DE has a comparable performance with recently proposed differential evolution variant. [ABSTRACT FROM AUTHOR]

• Presents a simple and efficient black-box adversarial attack strategy. • The adversarial perturbation can be dense or sparse. • Both universal and image dependent adversarial attack can be performed. • Employs a simple variant of Differential Evolution capable of optimizing the high dimensional problem under concern. Constructing adversarial perturbations for deep neural networks is an important direction of research. Crafting image-dependent adversarial perturbations using white-box feedback has hitherto been the norm for such adversarial attacks. However, black-box attacks are much more practical for real-world applications. Universal perturbations applicable across multiple images are gaining popularity due to their innate generalizability. There have also been efforts to restrict the perturbations to a few pixels in the image. This helps to retain visual similarity with the original images making such attacks hard to detect. This paper marks an important step that combines all these directions of research. We propose the DEceit algorithm for constructing effective universal pixel-restricted perturbations using only black-box feedback from the target network. We conduct empirical investigations using the ImageNet validation set on the state-of-the-art deep neural classifiers by varying the number of pixels to be perturbed from a meager 10 pixels to as high as all pixels in the image. We find that perturbing only about 10% of the pixels in an image using DEceit achieves a commendable and highly transferable Fooling Rate while retaining the visual quality. We further demonstrate that DEceit can be successfully applied to image-dependent attacks as well. In both sets of experiments, we outperform several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Bridges are one of the fundamental infrastructure assets that are vital for economic growth and public welfare. Over the past few decades, the numbers of deteriorating bridges have drastically escalated raising concerns for serviceable, safe and functional transportation networks. This state of affairs poses a paramount challenge especially when coupled with the need to address social and environmental constraints. Accordingly, this current research paper proposes an automated three-component model for bridge maintenance optimization at both project and network levels. The first component aims at identifying the physical characteristics of the tackled bridge inventory. The second component encompasses designing a multi-objective optimization model to determine the optimal set of maintenance plans through four principal objective functions. These functions comprise maximization of performance condition of bridge elements, minimization of agency and user costs, minimization of duration of traffic disruption and minimization of environmental impact. In the multi-objective optimization model, an exponential chaotic differential evolution (ECDE) algorithm is introduced in an attempt to circumvent the drawbacks of convergence speed and search behavior of classical meta-heuristics. The third component combines criteria importance through inter-criteria correlation (CRITIC), complex proportional assessment (COPRAS) and grey relational analysis (GRA) to select the most optimum maintenance plan for each study period. Comparison results revealed that ECDE-based Sinusoidal algorithm managed to improve the performance diagnostics of classical meta-heuristics by values ranged from 49.2% to 73.1% over the multi-year maintenance plans. The results of benchmark test functions exemplified that ECDE-based Sinusoidal algorithm performed better than genetic and differential evolution algorithms by 114.2% and 79.5%, respectively. The developed integrated model is expected to assist infrastructure managers in executing optimized and sustainable maintenance budget plans within various planning scenarios. • Bibliometric map is presented to derive the key shortcomings of previous research. • A chaotic-based optimization model is formulated for bridge maintenance planning in both project and network levels. • A hybrid multi-criteria decision-making algorithm is designed for prioritizing the Pareto optimum maintenance plans. • A computer aided application is developed using C#.net and Matlab. • The developed multi-objective optimization model is validated using actual case studies and benchmark test functions. [ABSTRACT FROM AUTHOR]

Dynamic economic dispatch with valve-point effects (DED_ vpe) is a high-dimensional constrained optimization problem with non-convex and non-smooth characteristics. Hybrid methods are one of the most advanced methods to solve the problem. However, most of these methods improve the solution accuracy at the expense of algorithm robustness. This paper proposes an adaptive hybrid backtracking search optimization algorithm (AHBSA) for solving the DED_ vpe. The core idea of AHBSA lies in designing a suitable coupling structure based on the current best individual (called optimal partial coupling). The structure hybridizes an improved BSA mutation operator and the DE/best/1 operator with equal probability. The improved BSA mutation operator uses the current best individual and the historical population to update individual position, called BSA/best/old. It is also the first research work of extending BSA to the problem. In addition, an adaptive parameter control mechanism is proposed to select an appropriate 'mixrate' value for achieving better coupling. The performance of AHBSA is validated on six DED test cases of three systems. Experimental results demonstrate that, compared with some representative methods, AHBSA not only reduces the fuel cost but also ensures the robustness of the algorithm. • A hybrid method AHBSA is proposed to solve DED problem with valve-point effects. • An optimal partial coupling structure is designed in AHBSA. • BSA is the first attempt at solving DED problem. • Results on six benchmark DED test cases show AHBSA is competitive. [ABSTRACT FROM AUTHOR]

Underwater glider (UG) is a new type of ocean observation platform. An accurate hydrodynamic analysis is necessary for motion performance analysis and motion control purposes. This paper proposes a novel data-driven method based on a differential evolution (DE) algorithm for the UG hydrodynamic. The gliding hydrodynamic identification model is derived first from the dynamic model under stable gliding conditions. Then, based on the trial data, the DE is used to identify gliding hydrodynamic. Finally, the towing tank test (Tank) and computational fluid dynamics (CFD) simulation are carried out to validate the effectiveness of the proposed method. To show the superiority of the DE-based method, the optimization (PSO) algorithm is added for comparison. The result indicates that the DE-based method has higher accuracy and faster convergence velocity in the UG hydrodynamic identification problem. When the gliding angle is set to 14.23 ∘ , the maximum gliding efficiency is 94.00%. This hydrodynamic identification method also provides a reference for other underwater vehicles. [Display omitted] • An accurate and simple gliding hydrodynamic identification model of UG is derived from the dynamic model. • A data-driven UG hydrodynamic identification method is proposed based on the differential evolution algorithm. • Experimental studies on the towing tank test and CFD simulation are carried out to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Multi-area economic dispatch (MAED) is an important non-linear optimization problem in power system operation. MAED involves multiple power generation areas, and minimizes the total fuel cost by determining the power generation within areas and the power interchange between areas. This paper proposes an improved competitive swarm optimization (ImCSO) algorithm to solve the MAED problems. The ImCSO algorithm introduces two improvements into competitive swarm optimization for performance enhancement. Firstly, a ranking paired learning strategy is adopted to enhance the learning efficiency of the loser particles; secondly, a differential evolution strategy is used to update and improve the winner particles. Combining a constraint repair technique, the proposed ImCSO algorithm is applied to solve 10-unit, 40-unit and 120-unit multi-area static economic dispatch and 40-unit multi-area dynamic economic dispatch problems. The solved MAED problems integrate comprehensive constraints such as valve point effect, multiple fuels, transmission loss, tie-line constraint, prohibited operating zone and ramp rate limit. Through comparison with other well-established optimization algorithms, it is observed that the ImCSO algorithm has superiority in terms of solution accuracy and reliability in solving the MAED problems. • A novel method ImCSO is proposed for solving multi-area economic dispatch (MAED). • Ranking paired learning and differential evolution strategies are adopted in ImCSO. • Static and dynamic MAED problems integrating comprehensive constraints are studied. • Simulation results show that ImCSO has superiority in solution accuracy and reliability. [ABSTRACT FROM AUTHOR]

Feature selection is an important data processing method to reduce dimension of the raw datasets while preserving the information as much as possible. In this paper, an enhanced version of Black Widow Optimization Algorithm called SDABWO is proposed to solve the feature selection problem. The Black Widow Optimization Algorithm (BWO) is a new population-based meta-heuristic algorithm inspired by the evolution process of spider population. Three main improvements were included into the BWO to overcome the shortcoming of low accuracy, slow convergence speed and being easy to fall into local optima. Firstly, a novel strategy for selecting spouses by calculating the weight of female spiders and the distance between spiders is proposed. By applying the strategy to the original algorithm, it has faster convergence speed and higher accuracy. The second improvement includes the use of mutation operator of differential evolution at mutation phase of BWO which helps the algorithm escape from the local optima. And then, three key parameters are set to adjust adaptively with the increase of iteration times. To confirm and validate the performance of the improved BWO, other 10 algorithms are used to compared with the SDABWO on 25 benchmark functions. The results show that the proposed algorithm enhances the exploitation ability, improves the convergence speed and is more stable when solving optimization problems. Furthermore, the proposed SDABWO algorithm is employed for feature selection. Twelve standard datasets from UCI repository prove that SDABWO-based method has stronger search ability in the search space of feature selection than the other five popular feature selection methods. These results confirm the capability of the proposed method simultaneously improve the classification accuracy while reducing the dimensions of the original datasets. Therefore, SDABWO-based method was found to be one of the most promising for feature selection problem over other approaches that are currently used in the literature. [ABSTRACT FROM AUTHOR]

Recently, many evolutionary computation methods have been developed to solve the feature selection problem. However, the studies focused mainly on small-scale issues, resulting in stagnation issues in local optima and numerical instability when dealing with large-scale feature selection dilemmas. To address these challenges, this paper proposes a novel weighted differential evolution algorithm based on self-adaptive mechanism, named SaWDE, to solve large-scale feature selection. First, a multi-population mechanism is adopted to enhance the diversity of the population. Then, we propose a new self-adaptive mechanism that selects several strategies from a strategy pool to capture the diverse characteristics of the datasets from the historical information. Finally, a weighted model is designed to identify the important features, which enables our model to generate the most suitable feature-selection solution. We demonstrate the effectiveness of our algorithm on twelve large-scale datasets. The performance of SaWDE is superior compared to six non-EC algorithms and six other EC algorithms, on both training and test datasets and on subset size, indicating that our algorithm is a favorable tool to solve the large-scale feature selection problem. Moreover, we have experimented SaWDE with six EC algorithms on twelve higher-dimensional data, which demonstrates that SaWDE is more robust and efficient compared to those state-of-the-art methods. SaWDE source code is available on Github at https://github.com/wangxb96/SaWDE. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an efficient stochastic algorithm for solving global numerical optimization problems. To effectively relieve the stagnation and premature convergence problems in DE, this paper presents an efficient DE variant, abbreviated as OLELS-DE, by designing orthogonal learning and elites local search mechanisms. More specifically, the stagnation or premature convergence phenomenon will be detected by monitoring the best individual's update condition during the evolution, then a population diversity estimation technique is utilized to distinguish between these two conditions empirically. To recover the population's evolution vitality according to the classification results, the enhanced orthogonal learning scheme is employed by selecting two different groups of individuals for constructing the orthogonal experimental design procedure. Moreover, the elites local search method is developed by selecting several well-performing elite individuals based on the Gaussian distribution model to further assist the former orthogonal learning mechanism. This scheme is designed to enhance the exploitation ability by searching the regions around elite individuals. The parameters and strategies in above two mechanisms are designed on the expectation of balancing the local exploitation and global exploration capabilities. The optimization performance of proposed OLELS-DE algorithm is evaluated based on 30 benchmark functions from CEC2014 test suite and is compared with eight state-of-the-art DE variants. As it was anticipated, the incorporation of orthogonal learning and elites local search mechanisms helps OLELS-DE have significantly better or at least comparable performance to the adopted DE competitors. • Stagnation and premature convergence are distinguished by population diversity. • Enhanced orthogonal learning scheme is proposed to recover population vitality. • Elites local search method is designed to assist the orthogonal learning scheme. • Algorithm design is based on the balance between exploration and exploitation. • Experiments show that the proposed algorithm has superior performance. [ABSTRACT FROM AUTHOR]

• An improved differential evolution (DE) technique is proposed. • Metaheuristic strategy with Differential Evolution (MSDE) is used to improve the DE. • The performance of the proposed method is tested using several solar PV cells. • Calculation time was reduced by half. Improving the performance of solar photovoltaic systems is reliant on accurately modeling the cells parameters. However, the identification of the lumped element model parameters remains a challenge given the non-linear nature of the diode circuit models. In this paper, we propose an improvement of the state-of-the-art of differential evolution (DE) technique. This relies on the Lambert W function and a preliminary meta -heuristic step to select the optimal mutation factor and crossover parameters for each specific Current-Voltage characteristic. We have applied it to three widely known cells and modules in literature. The Root Mean Squared Error of the proposed method is compared with literature results on the same systems. Furthermore, due to the stochastic nature of DE, an analysis of the stability was performed in order to evaluate its consistency. By using the meta -heuristic strategy, we were able to further improve the convergence characteristics of DE. The selection of DE parameters in the preliminary step have been shown to significantly reduce the computation time by half. [ABSTRACT FROM AUTHOR]

• Early prediction for mammography. • To diagnose the breast cancer at the earliest to reduce the mortality rate. • This paper proposes an expert system for the diagnosis of breast cancer disease using DE-RBF-KELM method. • The proposed method is effectively classifying the abnormal classes of mammograms. Breast cancer disease is a major public health problem among women worldwide. This article proposes an expert system for the diagnosis of breast cancer disease based on an evolutionary algorithm known as Differential Evolution (DE) of a Radial-Based Function Kernel Extreme Learning Machines (RBF-KELM). In the structure of the RBF-KELM, there are two adjustable parameters of the RBF-kernel which are the penalty parameter C and the RBF-kernel's parameter (σ). These parameters play a major role in the efficiency of RBF-KELM. In this study, the optimal values of these parameters have been obtained using a differential evolution (DE) algorithm. To validate the effectiveness of the suggested approach, DE-RBF-KELM was examined on the two datasets: The Mammographic Image Analysis Society (MIAS) and the wisconsin breast cancer database (WBCD) and the results were satisfactory compared to conventional approaches. [ABSTRACT FROM AUTHOR]

Active noise control headphones have become one of the most popular wearable personal devices currently. In this paper, the feedforward controller design problem using cascade biquad filters is addressed with respect to multiple incident directions of the primary noise. Criterion functions evaluating the comprehensive noise reduction performance are given, and the optimization methods for both single-channel and multi-channel controllers are proposed where Differential Evolution algorithm is used to find the global minimum. Experiments based on a commercial headphone are carried out and the results have validated the proposed methods. It has been shown that the comprehensive performance under multiple directions would be significantly improved when multiple reference microphones are used along with a multi-channel control strategy. [ABSTRACT FROM AUTHOR]

In this paper, a new compact algorithm is proposed. Two sinusoidal formulas are used to automatically adjust the crossover rate and the mutation scaling factor in the compact Differential Evolution (cDE) metaheuristic. The proposed algorithm, called Compound Sinusoidal cDE, CScDE, is compared to seven state-of-the-art compact algorithms on the well-known BBOB test-bed, the CEC-2014 test suite for continuous optimisation, as well as five real-world optimisation problems chosen from the CEC-2011 benchmarks. The CScDE algorithm outperformed its competitors for most problem categories and over most dimensions. It is also compared to some well-established population-based metaheuristics. • A compact compound sinusoidal differential evolution, CScDE, is proposed. • CScDE uses sinusoidal formulas to adjust the parameters of compact DE. • Both binomial and exponential crossover are tested. • CScDE is applied on 89 reference problems with several dimensions. • CScDE outperformed state-of-the-art compact algorithms. [ABSTRACT FROM AUTHOR]

[Display omitted] • A contribution to drug repurposing using deep learning and optimization is proposed. • A novel deep learning model is developed to predict Drug-Target binding affinities. • The proposed model integrates a Convolutional Neural Network on the top of a BiLSTM. • The Hyperparameters of the model are set using a Differential Evolution algorithm. • The Differential Evolution-based learning framework outperformed baseline methods. The identification of drug-target interactions (DTIs) is an important process in drug repositioning and drug discovery. However, it is very expensive and time-consuming to determine all possible DTIs with experimental approaches. Most existing machine learning-based methods formulate the DTIs prediction problem as a binary classification problem. Nevertheless, the lack of experimentally validated negative samples results in imbalanced class distribution within the datasets, which may have a negative influence on the DTI prediction performance. Casting DTI prediction task as a regression problem seems an interesting alternative to avoid this issue especially with the recent increase in protein structural data and DTI datasets. Within this context, a twofold contribution is described in this paper. First, we propose a novel deep learning model for predicting drug-target binding affinities called "Convolution Neural Network with Attention-based bidirectional Long Short-Term Memory network" (CNN-AbiLSTM), which combines a CNN with an attention-based biLSTM. Second, building a powerful hybrid CNN-AbiLSTM model can be highly complicated and requires a suitable setting of the model's hyperparameters. To handle this problem, we propose an evolutionary algorithm-based framework more specifically a Differential Evolution (DE) algorithm to find the optimal configuration of the proposed model. Experimental results show that the proposed DE-based CNN-AbiLSTM model achieves better performance compared with baseline methods. [ABSTRACT FROM AUTHOR]

Trajectory optimization is essentially an optimal control problem (OCP) with highly nonlinear dynamic properties and complex constraints, and a critical part of spacecraft design. In this paper, a hybrid algorithm is proposed for automatic reentry trajectory optimization of reusable launch vehicle (RLV) without providing user-specified initial guesses and a priori knowledge about the optimal trajectory. The method combines the strong robustness and global optimization properties of hyper-heuristic whale optimization algorithm (HHWOA) with the efficient and accurate features of the Gauss pseudospectral method (GPM). HHWOA works as the first-stage optimizer aims to obtain an approximate solution to provide a high-quality initial guess for the GPM, while the GPM works as the second-stage optimizer aims to accelerate the search of the optimum neighborhood to obtain an accurate optimal solution. Additionally, to enhance the progress during the evolutionary process, HHWOA is equipped with opposition-based learning, differential evolution operators, chaotic map sequences and smoothing technique strategies. The utilization of such strategies can potentially smooth the flight trajectory and improve the global convergence of the algorithm, while the three OCPs have shown their superiority in HHWOA. In order to evaluate the performance of the hybrid algorithm, complex constrained RLV maximum cross-range reentry problems with three different path constraint scenarios are investigated. Furthermore, more discussion and experiments are likewise conducted to investigate the impact of the parameters on the performance of the algorithm. The results show that the proposed hybrid algorithm can be very effective in addressing RLV reentry trajectory optimization problems. [ABSTRACT FROM AUTHOR]

It is always desirable to keep the number of components in compositional simulation low due to the high CPU time and storage space requirement; on the other hand, the model's accuracy in describing the phase behavior decreases with reducing the number of components. As a result, an optimal lumping approach is needed to group the components to attain high accuracy in the least amount of time. This paper's main objective is to find the best lumping scheme to maintain the split model's accuracy and reach an optimal lumped model in the least amount of time. In accordance with this purpose, five different evolutionary-based metaheuristic optimization algorithms known as genetic algorithm (GA), differential evolution (DE), harmony search (HS), imperialist competitive algorithm (ICA), and shuffled-frog leaping algorithm (SFLA) are incorporated into the objective function developed to cluster heavy fractions using the k-means algorithm. To find the optimal algorithm, the best cost of the objective function and the number of function evaluations (NFE) have been compared, and the final results were substantiated using flash calculation outputs, such as density, viscosity, phase diagram, and Wilson plot. The results indicate that the proposed algorithms are capable of finding the optimal lumped model with high accuracy for all reservoir samples tested in this study. This study also marked ICA as the best evolutionary-based optimization algorithm that can be used in clustering problems. • Clustering approach using Evolutionary-based algorithms to lump heavy-end component. • The order of algorithms efficiency to lump is ICA > SFLA > GA ≈ DE ≫ HS. • ICA and SFLA attain the best cost in the lowest number of function evaluation. • The efficiency of ICA does not reduce with the increase in the number of components. • 16 components are adequate enough to present ICA-16 as the optimum lumping strategy. [ABSTRACT FROM AUTHOR]

Recently, evolutionary algorithms (EAs) are becoming more and more popular in oil industry. It should be noted that the existence of various state constraints largely decreases the efficiency of EAs. In this paper, a double-model differential evolution (CSDE) is proposed for waterflooding production optimization with nonlinear inequality constraints. There are two main stages in CSDE: 1) the boundary of feasible region is explicitly constructed by a classifier, support vector machine (SVM), to prescreen the feasible solutions from current population, and 2) the objective function is approximated by radial basis function (RBF) surrogate model which can be used to select the best individual among feasible solutions. It is worth noticing that problems with a large number of constraints can be significantly simplified by distinguishing the feasible solutions and infeasible solutions with one unique SVM classifier. To the best of our knowledge, this is the first foray to integrate SVM and RBF into evolutionary algorithm for waterflooding optimization problem. The CSDE method has been tested on three artificial cases: three-channel, PUNQ-S3 and SPE9. The test results demonstrate that CSDE algorithm can handle constraints more efficient and achieve higher net present value (NPV) compared with the original EAs and other single-model algorithms. • A classification-based surrogate-assisted differential evolution (CSDE) is proposed. • This is the first foray to integrate SVM and RBF into evolutionary algorithm for constrained production optimization. • The experimental results demonstrate that the proposed method can handle constraints better and achieve higher NPV. [ABSTRACT FROM AUTHOR]

Moth–Flame Optimization (MFO) algorithm is widely used to solve real parameter global optimization problems. However, the original MFO algorithm has some problems. First, the global search ability of the algorithm is insufficient due to the lack of high-quality flames or the lack of diversity of the population. Moreover, due to the reduction of the number of flames mechanism and the simple transverse orientation flight mode of moths, the algorithm is easy to fall into local optimum. To overcome these difficulties, a new MFO called ODSFMFO is proposed in this paper, which consists of an improved flame generation mechanism based on Opposition-Based Learning (OBL) and Differential Evolution (DE) algorithm, and an enhanced local search mechanism based on Shuffled Frog Leaping Algorithm (SFLA) and death mechanism. The ODSFMFO firstly obtains a high-quality population through opposition-based learning, then enhances the diversity of the population through differential evolution algorithm, to improve the global search ability of the algorithm, and then, employs an improved shuffled frog leaping algorithm as a local search algorithm, and uses the death mechanism to eliminate the individuals with low fitness value, which not only make the algorithm easy to jump out of the local optimum, but also further improve the convergence speed. To demonstrate the effectiveness of the proposed algorithm, the ODSFMFO and other fourteen well-known optimization algorithms are tested and compared on 28 benchmark tasks of CEC 2013 and 30 benchmark tasks of CEC 2017. Simulation results show that the proposed algorithm obtains better results compared with other competitive algorithms. • An improved flame generation mechanism of MFO is proposed. • A new updating mechanism of SFLA is proposed to improve the local search ability. • The SFLA and death mechanism are applied in combination to MFO. • ODSFMFO is better than 14 compared algorithms. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) problem featured as a non-linear, non-convex, large-scale and constrained, still remains a popular and challenging work in power systems optimization. Although various optimization algorithms have been devoted to solving this problem, they suffer from some weak points such as insufficient accuracy as well as most of them are unconstrained optimization algorithms that result in optimal solutions that violate certain security operational constraints. To this end, this paper presents an adaptive constraint differential evolution (ACDE) algorithm, in which the novelty lies primarily in these three points: i) the crossover rate (CR) sorting mechanism is employed to build the relationship of CR and individual fitness values; ii) reusing successful evolution direction is proposed to guide the individual evolution towards promising regions; iii) an advanced constraint handling technique named superiority of feasible solutions (SF) is introduced to effectively deal with constraints in power systems. In order to verify the performance of the presented approach to the OPF problem, the standard IEEE-30 bus system is selected as the test case, in which six optimization objectives including total fuel cost, total fuel cost considering the valve-point effect, real active power losses, voltage deviation, voltage stability and emission are studied. The experimental results demonstrate that the presented approach can provide the smaller cost (800.41132$/h), reducing by up to 3.76% compared to the MPIO-COSR. In terms of the emission, ACDE emits the least emissions (0.204817ton/h). In addition, the proposed method also obtains the best results on the real active power losses (3.084041 MW) and voltage deviation (0.085636p.u.) when compared with other state-of-the-art methods. • Crossover rate sorting mechanism is used to improve the performance of adaptive differential evolution. • Reusing successful evolution direction strategy is proposed. • Constraints in OPF are effectively handled through superiority of feasible solutions. • The proposed algorithm can be an effective alternative for constraint OPF problems. [ABSTRACT FROM AUTHOR]

• A CVaR-based method is used to determine power control value of PVs, EVs and DSs. • Energy risk due to power loss and voltage offset is controlled by optimizing method. • A method based on second-order cone programming is proposed to control CVaR risk. • Q-learning driven adaptive differential evolution method is used for faster solution. Distributed generation and energy storage brings opportunities and risks of distribution systems, such as greater power loss and abnormal voltage fluctuation. A Q-learning powered optimization model for these risk control of is presented in this paper. Considering the uncertainties of output power of distributed generation systems, charging and discharging power of electric vehicles and energy storage devices, a CVaR-based energy risk control model for distribution system with renewable energy is presented to determine the control value of output power of distributed generation systems. Q-learning powered adaptive differential evolution algorithm is used to solve the proposed optimization problem. Second order cone programming is used to simplify the objective function and constraints of the optimization model, and Q-learning driven adaptive differential evolution algorithm is used to enhance the ability of solving, simplify the calculation, and make the solution faster and more stable. The feasibility and applicability of the proposed model and algorithm are verified by simulating IEEE-118 distribution system. [ABSTRACT FROM AUTHOR]

Fishmeal is a high-energy feed. Fishmeal quality has an important influence on animal growth. The detection of ash content is essential for evaluating fishmeal quality. Near infrared (NIR) spectroscopy is a rapid detection technique that can be used to determine the ash content in fishmeal. The wavelength related to the target component must be selected in NIR quantitative analysis considering that the NIR spectrum has a strong correlation. In this paper, the adaptive binary differential evolution (ABDE) algorithm was proposed for feature selection. The algorithm was an improvement of the mutation operator of binary differential evolution (BDE) and improved binary differential evolution (IBDE) algorithms. The performance of the BDE, IBDE, and ABDE algorithms was compared in secondary wavelength selection on 11 combined intervals selected by the move window partial least squares (MWPLS) algorithm. The performance of the model established by the ABDE algorithm in feature selection was superior to that constructed by the BDE and IBDE algorithms. Frequency ABDE (FABDE) was designed by introducing frequency into the ABDE algorithm to minimize the influence of random factors. The root mean square error (RMSET) and the correlation coefficient (R T) of the FABDE model for the test set were 1.160 ​% and 0.941, respectively. By comparison, the RMSET and the R T of the ABDE model were 1.212 ​% and 0.935, respectively. The results of model prediction indicated that the FABDE algorithm proposed herein can be feasibly used to improve the model effects for the rapid NIR analysis of ash content in fishmeal. • A novel algorithm is proposed for feature selection in NIR models. • ABDE outperforms the methods of BDE and IBDE. • The FABDE is designed by introducing frequency into the ABDE. • NIRS combined to FABDE is effective for estimating content of ash. [ABSTRACT FROM AUTHOR]

In real world, engineering design problems have been regarded as common and complex problems which always include non-linear standard functions, many complex constraints and decision variables containing both continuous and discrete variables. Problems with these features also can be named as mixed-variable optimization problems (MVOPs). However, because the decision variables of MVOPs present different spatial distribution features, how to design an effective algorithm to solve MVOPs is still a challenge. In this paper, a new variation of differential evolution (DE), named multi-strategy co-evolutionary differential evolution (M C D E m v ) is proposed to solve MVOPs. A mixed-variable co-evolutionary scheme simultaneously considering continuous and discrete variables in MVOPs is utilized. Based on this scheme, a multi-strategy co-evolutionary approach considering a dynamic adaptive selection mechanism and the combination of different characteristic mutation strategies and crossover operators is proposed for adapting to the all-inclusive situations in MVOPs. Furthermore, a statistics-based local search (S B A) specially designed for the optimization of discrete variables in MVOPs is proposed for enhancing the efficiency and flexibility of the M C D E m v . 28 artificial benchmark functions are adopted to verify the effectiveness and efficiency of the proposed algorithm. The experimental results show that our proposed algorithm is more competitive and efficient than other compared similar algorithms, i.e., AEDA, A C O M V , D E M V and P S O m v . Comparing with other similar algorithms in coping with two mixed-variable engineering design problems, M C D E m v also shows higher efficiency and performance. • A multi-strategy co-evolutionary approach for mixed-variables in MVOPs is presented. • A dynamic adaptive selection mechanism for the all-inclusive situations is utilized. • A statistics-based local search for discrete variables in MVOPs is proposed. [ABSTRACT FROM AUTHOR]

Database fragmentation can protect the distributed database's privacy by dividing attributes of sensitive associations into different fragments. Previous database fragmentation algorithms are designed for the initialization of the distributed database. However, the initial database fragmentation cannot maintain its effect during the distributed database's entire life cycle. This paper defines a dynamic database fragmentation problem with privacy preservation requirements, in which both the privacy preservation degree and the communication cost are considered during the optimization. For this problem, a knowledge transfer-based distributed differential evolution algorithm (KT-DDE) is proposed to achieve the optimal communication cost and maintain privacy preservation. The proposed KT-DDE algorithm includes a distributed framework and a differential evolution-based optimizer. In the proposed distributed framework, the fragmentation knowledge is transferred between different database fragmentation subproblems. The fragmentation information of various individuals is exchanged in the optimizer and used to generate trial individuals. After the selection, competitive trial individuals are kept in the population. Experimental results show that the proposed algorithm can outperform the other competitors in terms of solution accuracy, convergence speed, and computation efficiency. In addition, the effectiveness of the proposed components is verified. [ABSTRACT FROM AUTHOR]

• Biologically inspired heuristics for target search lack modularity and modifiability. • The proposed hyper-heuristic is based on modular and adaptive swarm behavior. • Combining stigmergy, flocking and evolution outperforms bio-inspired heuristics. Target search aims to discover elements of various complexity in a physical environment, by minimizing the overall discovery time. Different swarm intelligence algorithms have been proposed in the literature, inspired by biological species. Despite the success of bio-inspired techniques (bio-heuristics), there are relevant algorithm selection and parameterization costs associated with every new type of mission and with new instances of known missions. In this paper, evolutionary optimization is proposed for achieving significant improvements of the mission performance. Although adaptive, the logic of bio-heuristics is nevertheless constrained by models of biological species. To generate more adaptable logics, a novel design approach based on hyper-heuristics is proposed, in which the differential evolution optimizes the aggregation and tuning of modular heuristics for a given application domain. A modeling and optimization testbed has been developed and publicly released. Experimental results on real-world scenarios show that the hyper-heuristics based on stigmergy and flocking significantly outperform the adaptive bio-heuristics. [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel evolutionary symbiotic organisms search algorithm is proposed. • The proposed method has novel hybrid mutations of differential evolution and symbiotic organisms search. • The present paradigm is of a better balance for both exploration and exploitation abilities. • The suggested approach is first validated on truss optimization with free vibration and transient behavior. • The developed algorithm dramatically ameliorates the convergence speed, yet still providing high-quality optimal solutions. In this paper, an evolutionary symbiotic organisms search algorithm as a hybridization of differential evolution and symbiotic organisms search is developed for shape and size optimization of truss structures with free vibration and transient behavior under multiple constraints. For that aim, a mutation operator combined by two global differential evolution operators and a novel symbiotic organisms search operator is proposed to reinforce the exploration ability of the proposed algorithm. This newly suggested symbiotic organisms search operator is relied upon the symbiotic relationship in such a way that an arbitrary organism can receive benefits from both mutualisms and commensalisms. A threshold is automatically integrated into the mutation step to switch from the exploration capability to the exploitation one. In addition, an elitist scheme is applied to the selection phase to purify the most potential candidates for the next symbiotic ecosystem. Accordingly, the present algorithm can result in a high-quality optimal solution with a better convergence evolution. 26 mathematical functions and 7 well-known benchmark problems regarding size and shape truss optimization under multiple constraints are tested to verify the effectiveness of the proposed methodology. Obtained outcomes have indicated that the developed algorithm outperforms both original algorithms and many existing approaches in the literature. To further illustrate the ability of the proposed paradigm, two among the above five examples under transient excitations with strength, displacement, and buckling constraints are then optimized. [ABSTRACT FROM AUTHOR]

• A general algorithm framework for hardware software partitioning problem is proposed based on evolutionary algorithms. • An effective method to deal with the infeasible solution of hardware software partitioning problem is proposed • The performances of genetic algorithm (GA), binary particle swarm optimization (BPSO), binary differential evolution algorithm with hybrid encoding (HBDE), and group theory based optimization algorithm (GTOA) for solving hardware software partiti on ing problem are compared. • According to the comparison results, it is pointed out that GTOA and BPSO are more suitable for solving hardware software partitioning problem than GA and HBDE Hardware/software partitioning (HW/SW) is a significant problem in hardware-software co-design, and it is also an NP-hard problem. In order to solve the HW/SW quickly and effectively by evolutionary algorithms, the HW/SW is firstly regarded as a variant of knapsack problem. Based on a new greedy strategy, a greedy repair and optimization algorithm GROM is proposed to eliminate the infeasible solutions. Subsequently, a general algorithm framework based on discrete evolutionary algorithm for HW/SW problem is proposed. On the basis of the above algorithm framework, genetic algorithm (GA), binary particle swarm optimization (BPSO), binary differential evolution algorithm with hybrid encoding (HBDE) and group theory-based optimization algorithm (GTOA) are used to solve large-scale HW/SW instances. The feasibility and effectiveness of the algorithm framework proposed in the paper are verified by comparing the good and bad of the calculation results of above algorithms, and pointed out that the performance of GTOA and BPSO is better than that of HBDE and GA, they are more suitable for solving large-scale HW/SW problem. [ABSTRACT FROM AUTHOR]

Differential evolution is an effective and practical approach that is widely applied for solving global optimization problems. Nevertheless, its effectiveness and scalability are decreased when the problems' dimension is increased. Hence, this paper is devoted to proposing a novel DE algorithm named quantum-based avian navigation optimizer algorithm (QANA) inspired by the extraordinary precision navigation of migratory birds during long-distance aerial paths. In the QANA, the population is distributed by partitioning into multi flocks to explore the search space effectively using proposed self-adaptive quantum orientation and quantum-based navigation consisted of two mutation strategies, DE/quantum/I and DE/quantum/II. Except for the first iteration, each flock is assigned using an introduced success-based population distribution (SPD) policy to one of the quantum mutation strategies. Meanwhile, the information flow is shared through the population using a new communication topology named V-echelon. Furthermore, we introduce two long-term and short-term memories to provide meaningful knowledge for partial landscape analysis and a qubit-crossover operator to generate the next search agents. The effectiveness and scalability of the proposed QANA were extensively evaluated using benchmark functions CEC 2018 and CEC 2013 as LSGO problems. The results were statistically analyzed by the Wilcoxon signed-rank sum test, ANOVA, and mean absolute error tests. Finally, the applicability of the QANA to solve real-world problems was evaluated by four engineering problems. The experimental results and statistical analysis prove that the QANA is superior to the competitor DE and swarm intelligence algorithms in test functions CEC 2018 and CEC 2013, with overall effectiveness of 80.46% and 73.33%, respectively. • Proposing an optimizer algorithm (QANA) inspired by migratory birds' navigation behaviors. • Introducing LTM and STM memories to share information and partial landscape analysis. • Introducing a V-echelon topology to spread information flow throughout search agents. • Introducing a quantum mutation strategy equipped by two meaningful search strategies. • The proposed QANA optimizer is very competitive and superior to compared algorithms. [ABSTRACT FROM AUTHOR]

Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm's ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator's performance. Experiments were conducted in order to compare six differential evolution algorithms' performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm's performance. • A new selection operator is proposed for differential evolution (DE). • Various DEs can be improved by the new selection operator. • The new selection operator can be easily applied to the existing DEs. [ABSTRACT FROM AUTHOR]