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Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars' attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is one of the most popular and widely used optimizers among the community of evolutionary computation. Despite numerous works having been conducted on the improvement of DE performance, there are still some defects, such as premature convergence and stagnation. In order to alleviate them, this paper presents a novel DE variant by designing a new mutation operator (named "DE/current-to-pbest_id/1") and a new control parameter setting. In the new operator, the fitness value of the individual is adopted to determine the chosen scope of its guider among the population. Meanwhile, a group-based competitive control parameter setting is presented to ensure the various search potentials of the population and the adaptivity of the algorithm. In this setting, the whole population is randomly divided into multiple equivalent groups, the control parameters for each group are independently generated based on its location information, and the worst location information among all groups is competitively updated with the current successful parameters. Moreover, a piecewise population size reduction mechanism is further devised to enhance the exploration and exploitation of the algorithm at the early and later evolution stages, respectively. Differing from the previous DE versions, the proposed method adaptively adjusts the search capability of each individual, simultaneously utilizes multiple pieces of successful parameter information to generate the control parameters, and has different speeds to reduce the population size at different search stages. Then it could achieve the well trade-off of exploration and exploitation. Finally, the performance of the proposed algorithm is measured by comparing with five well-known DE variants and five typical non-DE algorithms on the IEEE CEC 2017 test suite. Numerical results show that the proposed method is a more promising optimizer. [ABSTRACT FROM AUTHOR]

Complex systems provide an opportunity to analyze the essence of phenomena by studying their intricate connections. The networks formed by these connections, known as complex networks, embody the underlying principles governing the system's behavior. While complex networks have been previously applied in the field of evolutionary computation, prior studies have been limited in their ability to reach conclusive conclusions. Based on our investigations, we are against the notion that there is a direct link between the complex network structure of an algorithm and its performance, and we demonstrate this experimentally. In this paper, we address these limitations by analyzing the dynamic complex network structures of five algorithms across three different problems. By incorporating mathematical distributions utilized in prior research, we not only generate novel insights but also refine and challenge previous conclusions. Specifically, we introduce the biased Poisson distribution to describe the algorithm's exploration capability and the biased power-law distribution to represent its exploitation potential during the convergence process. Our aim is to redirect research on the interplay between complex networks and evolutionary computation towards dynamic network structures, elucidating the essence of exploitation and exploration in the black-box optimization process of evolutionary algorithms via dynamic complex networks. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a simple, effective, and robust algorithm, which has demonstrated excellent performance in dealing with global optimization problems. However, different search strategies are designed for different fitness landscape conditions to find the optimal solution, and there is not a single strategy that can be suitable for all fitness landscapes. As a result, developing a strategy to adaptively steer population evolution based on fitness landscape is critical. Motivated by this fact, in this paper, a novel adaptive DE based on fitness landscape (FL-ADE) is proposed, which utilizes the local fitness landscape characteristics in each generation population to (1) adjust the population size adaptively; (2) generate DE/current-to-pcbest mutation strategy. The adaptive mechanism is based on local fitness landscape characteristics of the population and enables to decrease or increase the population size during the search. Due to the adaptive adjustment of population size for different fitness landscapes and evolutionary processes, computational resources can be rationally assigned at different evolutionary stages to satisfy diverse requirements of different fitness landscapes. Besides, the DE/current-to-pcbest mutation strategy, which randomly chooses one of the top p% individuals from the archive cbest of local optimal individuals to be the pcbest, is also an adaptive strategy based on fitness landscape characteristic. Using the individuals that are approximated as local optimums increases the algorithm's ability to explore complex multimodal functions and avoids stagnation due to the use of individuals with good fitness values. Experiments are conducted on CEC2014 benchmark test suit to demonstrate the performance of the proposed FL-ADE algorithm, and the results show that the proposed FL-ADE algorithm performs better than the other seven highly performing state-of-art DE variants, even the winner of the CEC2014 and CEC2017. In addition, the effectiveness of the adaptive population mechanism and DE/current-to-pcbest mutation strategy based on landscape fitness proposed in this paper are respectively verified. [ABSTRACT FROM AUTHOR]

In recent times, numerous innovative and specialized algorithms have emerged to tackle two and three multi-objective types of problems. However, their effectiveness on many-objective challenges remains uncertain. This paper introduces a new Many-objective Sine–Cosine Algorithm (MaOSCA), which employs a reference point mechanism and information feedback principle to achieve efficient, effective, productive, and robust performance. The MaOSCA algorithm's capabilities are enhanced by incorporating multiple features that balance exploration and exploitation, direct the search towards promising areas, and prevent search stagnation. The MaOSCA's performance is evaluated against popular algorithms such as the Non-dominated sorting genetic algorithm-III (NSGA-III), the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) integrated with Differential Evolution (MOEADDE), the Many-objective Particle Swarm Optimizer (MaOPSO), and the Many-objective JAYA Algorithm (MaOJAYA) across various test suites, including DTLZ1-DTLZ7 with 5, 9, and 15 objectives and car cab design, water resources management, car side impact, marine design, and 10-bar truss engineering design problems. The performance evaluation is carried out using various performance metrics. The MaOSCA demonstrates its ability to achieve well-converged and diversified solutions for most problems. The success of the MaOSCA can be attributed to the multiple features of the SCA optimizer integrated into the algorithm. [ABSTRACT FROM AUTHOR]

To reduce logistics scheduling costs and energy consumption, this paper studies the slab allocation and hot-rolling scheduling integrated optimization problem that arises in practical iron and steel enterprises. In this problem, slabs are first allocated to orders and then sent to heating furnaces for heating; then, they are sent to a hot-rolling mill for rolling. A 0–1 integer programming model is established to minimize the attribute difference in the allocation cost between slabs and orders, the switching cost of hot-rolling processing, and waiting times after slabs reach rolling mills. Given the problem's characteristics, an improved differential evolution algorithm using a real-number coding method is designed to solve it. Three different heuristic algorithms are proposed to improve the quality of solutions in the initial population. Multiple parent individuals participate in the mutation operation, which increases the population diversity and prevents the algorithm from falling into the local optimum prematurely. Experiments on 14 sets of real production data from a large domestic iron and steel plant show that our improved differential evolution algorithm generates significantly better solutions in a reasonable amount of time compared with CPLEX, the simulated artificial method, and the classical differential evolution algorithm, and it can be used by practitioners. [ABSTRACT FROM AUTHOR]

In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness. [ABSTRACT FROM AUTHOR]

In this paper, an optimum design algorithm is presented for reinforced concrete folded plate structures. The design provisions are implemented by ACI 318-11 and ACI 318.2-14, which are quite complex to apply. The design variables are divided into three classes. The first class refers to the variables involving the plates, which are the number of supports, thicknesses of the plates, configurations of longitudinal and transverse reinforcement, span length of each plate, and angle of inclination of the inclined plates. The second class consists of the variables involving the auxiliary members' (beams and diaphragms) depth and breadth and the configurations of longitudinal and shear reinforcement. The third class of variables can be the supporting columns, which involve the dimensions of the column along each axis and the configurations of longitudinal and shear reinforcement. The objective function is considered as the total cost of the folded plate structure, which consists of the cost of concrete, reinforcement, and formwork that is required to construct the building. With such formulation, the design problem becomes a discrete nonlinear programming problem. Its solution is obtained by using three different soft computing techniques, which are artificial bee colony, differential evolution, and enhanced beetle antennae search. The enhancement suggested makes use of the population of beetles instead of one, as is the case in the standard algorithm. With this novel improvement, the beetle antennae search algorithm became very efficient. Two folded plate structures are designed by the proposed optimum design algorithm. It is observed that the differential evolution algorithm performed better than the other two metaheuristics and achieved the cheapest solution. [ABSTRACT FROM AUTHOR]

Arithmetic Optimization Algorithm (AOA) is a physically inspired optimization algorithm that mimics arithmetic operators in mathematical calculation. Although the AOA has an acceptable exploration and exploitation ability, it also has some shortcomings such as low population diversity, premature convergence, and easy stagnation into local optimal solutions. The Golden Sine Algorithm (Gold-SA) has strong local searchability and fewer coefficients. To alleviate the above issues and improve the performance of AOA, in this paper, we present a hybrid AOA with Gold-SA called HAGSA for solving industrial engineering design problems. We divide the whole population into two subgroups and optimize them using AOA and Gold-SA during the searching process. By dividing these two subgroups, we can exchange and share profitable information and utilize their advantages to find a satisfactory global optimal solution. Furthermore, we used the Levy flight and proposed a new strategy called Brownian mutation to enhance the searchability of the hybrid algorithm. To evaluate the efficiency of the proposed work, HAGSA, we selected the CEC 2014 competition test suite as a benchmark function and compared HAGSA against other well-known algorithms. Moreover, five industrial engineering design problems were introduced to verify the ability of algorithms to solve real-world problems. The experimental results demonstrate that the proposed work HAGSA is significantly better than original AOA, Gold-SA, and other compared algorithms in terms of optimization accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

The issue of how to improve the usability of data publishing under differential privacy has become one of the top questions in the field of machine learning privacy protection, and the key to solving this problem is to allocate a reasonable privacy protection budget. To solve this problem, we design a privacy budget allocation algorithm based on out-of-bag estimation in random forest. The algorithm firstly calculates the decision tree weights and feature weights by the out-of-bag data under differential privacy protection. Secondly, statistical methods are introduced to classify features into best feature set, pruned feature set, and removable feature set. Then, pruning is performed using the pruned feature set to avoid decision trees over-fitting when constructing an ϵ -differential privacy random forest. Finally, the privacy budget is allocated proportionally based on the decision tree weights and feature weights in the random forest. We conducted experimental comparisons with real data sets from Adult and Mushroom to demonstrate that this algorithm not only protects data security and privacy, but also improves model classification accuracy and data availability. [ABSTRACT FROM AUTHOR]

Operation Sequencing (OS) is one of the most critical tasks in a CAPP system. This process could be modelled as a combinatorial problem where finding a suitable solution within a reasonable time interval is difficult. This work implements a novel Discrete Differential Evolution Algorithm (DDEA) to solve the OS problem, focusing on parts of up to 76 machining operations; the relationships among operations are represented as a directed graph; the contributions of the DDEA are as follows: (1) operates with a discrete representation in the space of feasible solutions; (2) employs mutation and crossover operators to update solutions and to reduce machining and setup costs, (3) possess a local search strategy to achieve better solutions, and (4) integrates a statistical method based on quantiles to measure the quality and likelihood for an achieving a solution. To demonstrate the efficiency and robustness of the DDEA, five prismatic parts with different numbers of machining operations as benchmarks to address the OS problem were selected. The results generated the same OS for parts with a few machining operations (up to 23 machining operations). Conversely, for parts with more machining operations, the DDEA needs more runs to achieve the best solution. [ABSTRACT FROM AUTHOR]

This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly. [ABSTRACT FROM AUTHOR]

This paper presents a hybridization of Strategic Oscillation with Path Relinking to provide a set of high-quality nondominated solutions for the Multiobjective k-Balanced Center Location problem. The considered location problem seeks to locate k out of m facilities in order to serve n demand points, minimizing the maximum distance between any demand point and its closest facility while balancing the workload among the facilities. An extensive computational experimentation is carried out to compare the performance of our proposal, including the best method found in the state-of-the-art as well as traditional multiobjective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

There are some processing problems of imbalanced data such as imbalanced data sets being difficult to integrate efficiently. This paper proposes and constructs a mixed penalty function data integration screening criterion, and proposes Differential Evolution Integration Algorithm Based on Mixed Penalty Function Screening Criteria (DE-MPFSC algorithm). In addition, the theoretical validity and the convergence of the DE-MPFSC algorithm are analyzed and proven by establishing the Markov sequence and Markov evolution process model of the DE-MPFSC algorithm. In this paper, the entanglement degree and enanglement degree error are introduced to analyze the DE-MPFSC algorithm. Finally, the effectiveness and stability of the DE-MPFSC algorithm are verified by UCI machine learning datasets. The test results show that the DE-MPFSC algorithm can effectively improve the effectiveness and application of imbalanced data classification and integration, improve the internal classification of imbalanced data and improve the efficiency of data integration. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal methodology based on the Differential Evolution algorithm for obtaining the set of duty cycles of a recently proposed power electronics converter with input current ripple cancelation capability. The converter understudy was recently introduced to the state-of-the-art as the interleaved connection of two unequal converters to achieve low input current ripple. A latter contribution proposed a so-called proportional strategy. The strategy can be described as the equations to relate the duty cycles of the unequal power stages. This article proposes a third switching strategy that provides a lower input current ripple than the proportional strategy. This is made by considering duty cycles independently of each other instead of proportionally. The proposed method uses the Differential Evolution algorithm to determine the optimal switching pattern that allows high quality at the input current side, given the reactive components, the switching frequency, and power levels. The mathematical model of the converter is analyzed, and thus, the decision variables and the optimization problem are well set. The proposed methodology is validated through numerical experimentation, which shows that the proposed method achieves lower input current ripples than the proportional strategy. [ABSTRACT FROM AUTHOR]

Integrated process planning and scheduling (IPPS) is important for modern manufacturing companies to achieve manufacturing efficiency and improve resource utilization. Meanwhile, multiple objectives need to be considered in the realistic decision-making process for manufacturing systems. Based on the above realistic manufacturing system requirements, it becomes increasingly important to develop effective methods to deal with multi-objective IPPS problems. Therefore, an improved NSGA-II (INSGA-II) algorithm is proposed in this research, which uses the fast non-dominated ranking method for multiple optimization objectives as an assignment scheme for fitness. A multi-layer integrated coding method is adopted to address the characteristics of the integrated optimization model, which involves many optimization parameters and interactions. Elite and mutation strategies are employed during the evolutionary process to enhance population diversity and the quality of solutions. An external archive is also used to store and update the Pareto solution. The experimental results on the Kim test set demonstrate the effectiveness of the proposed INSGA-II algorithm. [ABSTRACT FROM AUTHOR]

The difference between dual-gantry and single-gantry surface-mount placement (SMP) machines is that dual-gantry machines exhibit higher complexity and more problems due to their additional gantry robot, such as component allocation and collision. This paper presents algorithms to prescribe the assembly operations of a dual-gantry multi-head surface-mount placement machine. It considers five inter-related problems: (i) component allocation; (ii) automatic nozzle changer assignment; (iii) feeder arrangement; and (iv) pick-and-place sequence; it incorporates a practical restriction related to (v) component height. The paper proposes a solution to each problem: (i) equalizing "workloads" assigned to the gantries, (ii) using quantity ratio method, (iii) using two similarity measurement mechanisms in a modified differential evolution algorithm with a random-key encoding mapping method that addresses component height restriction, (iv) and a combination of nearest-neighbor search and 2-opt method to plan each placing operation. This study reports an experiment that involved the processing of 10 printed circuit boards and compared the performance of a modified differential evolution algorithm with well-known algorithms including differential evolution, particle swarm optimization, and genetic algorithm. The results reveal that the number of picks, moving distance of picking components, and total assembly time with the modified differential evolution algorithm are less than other algorithms. [ABSTRACT FROM AUTHOR]

The execution time that takes to perform numerical simulation of a chaotic oscillator mainly depends on the time-step h. This paper shows that the optimization of chaotic oscillators can be enhanced by estimating the highest h in either one-step or multi-step methods. Four chaotic oscillators are used as a case study, and the optimization of their Kaplan-Yorke dimension ( D K Y ) is performed by applying three metaheuristics, namely: particle swarm optimization (PSO), many optimizing liaison (MOL), and differential evolution (DE) algorithms. Three representative one-step and three multi-step methods are used to solve the four chaotic oscillators, for which the estimation of the highest h is obtained from their stability analysis. The optimization results show the effectiveness of using a high h value for the six numerical methods in reducing execution time while maximizing the positive Lyapunov exponent ( L E + ) and D K Y of the chaotic oscillators by applying PSO, MOL, and DE algorithms. [ABSTRACT FROM AUTHOR]

Single objective optimization algorithms are the foundation of establishing more complex methods, like constrained optimization, niching and multi-objective algorithms. Therefore, improvements to single objective optimization algorithms are important because they can impact other domains as well. This paper proposes a method using turning-based mutation that is aimed to solve the problem of premature convergence of algorithms based on SHADE (Success-History based Adaptive Differential Evolution) in high dimensional search space. The proposed method is tested on the Single Objective Bound Constrained Numerical Optimization (CEC2020) benchmark sets in 5, 10, 15, and 20 dimensions for all SHADE, L-SHADE, and jSO algorithms. The effectiveness of the method is verified by population diversity measure and population clustering analysis. In addition, the new versions (Tb-SHADE, TbL-SHADE and Tb-jSO) using the proposed turning-based mutation get apparently better optimization results than the original algorithms (SHADE, L-SHADE, and jSO) as well as the advanced DISH and the jDE100 algorithms in 10, 15, and 20 dimensional functions, but only have advantages compared with the advanced j2020 algorithm in 5 dimensional functions. [ABSTRACT FROM AUTHOR]

This study proposes a dual-population algorithmic scheme for differential evolution and specific mutation strategy. The first population contains the newest individuals, and is continuously updated, whereas the other keeps the top individuals throughout the whole search process. The proposed mutation strategy combines information from both populations. The proposed L-NTADE algorithm (Linear population size reduction Newest and Top Adaptive Differential Evolution) follows the L-SHADE approach by utilizing its parameter adaptation scheme and linear population size reduction. The L-NTADE is tested on two benchmark sets, namely CEC 2017 and CEC 2022, and demonstrates highly competitive results compared to the state-of-the-art methods. The deeper analysis of the results shows that it displays different properties compared to known DE schemes. The simplicity of L-NTADE coupled with its high efficiency make it a promising approach. [ABSTRACT FROM AUTHOR]

Fractional calculus is widely used in engineering fields. In complex mechanical systems, multi-body dynamics can be modelled by fractional differential-algebraic equations when considering the fractional constitutive relations of some materials. In recent years, there have been a few works about the numerical method of the fractional differential-algebraic equations. However, most of the methods cannot be directly applied in the equations of dynamic systems. This paper presents a numerical algorithm of fractional differential-algebraic equations based on the theory of sliding mode control and the fractional calculus definition of Grünwald–Letnikov. The algebraic equation is considered as the sliding mode surface. The validity of the present method is verified by comparing with an example with exact solutions. The accuracy and efficiency of the present method are studied. It is found that the present method has very high accuracy and low time consumption. The effect of violation corrections on the accuracy is investigated for different time steps. [ABSTRACT FROM AUTHOR]

Self-adaptive variants of evolutionary algorithms (EAs) tune their parameters on the go by learning from the search history. Adaptive differential evolution with optional external archive (JADE) and self-adaptive differential evolution (SaDE) are two well-known self-adaptive versions of differential evolution (DE). They are both unconstrained search and optimization algorithms. However, if some constraint handling techniques (CHTs) are incorporated in their frameworks, then they can be used to solve constrained optimization problems (COPs). In an early work, an ensemble of constraint handling techniques (ECHT) is probabilistically hybridized with the basic version of DE. The ECHT consists of four different CHTs: superiority of feasible solutions, self-adaptive penalty, ε -constraint handling technique and stochastic ranking. This paper employs ECHT in the selection schemes, where offspring competes with their parents for survival to the next generation, of JADE and SaDE. As a result, JADE-ECHT and SaDE-ECHT are developed, which are the constrained variants of JADE and SaDE. Both algorithms are tested on 24 COPs and the experimental results are collected and compared according to algorithms' evaluation criteria of CEC'06. Their comparison, in terms of feasibility rate (FR) and success rate (SR), shows that SaDE-ECHT surpasses JADE-ECHT in terms of FR, while JADE-ECHT outperforms SaDE-ECHT in terms of SR. [ABSTRACT FROM AUTHOR]

Although proposed more than half a century ago, the Nelder–Mead simplex search algorithm is still widely used. Four numeric constants define the operations and behavior of the algorithm. The algorithm with the original constant values performs fine on most low-dimensional, but poorly on high-dimensional, problems. Therefore, to improve its behavior in high dimensions, several adaptive schemas setting the constants according to the problem dimension were proposed in the past. In this work, we present a novel adaptive schema obtained by a meta-optimization procedure. We describe a schema candidate with eight parameters subject to meta-optimization and define an objective function evaluating the candidate's performance. The schema is optimized on up to 100-dimensional problems using the Parallel Simulated Annealing with Differential Evolution global method. The obtained global minimum represents the proposed schema. We compare the performance of the optimized schema with the existing adaptive schemas. The data profiles on the Gao–Han modified quadratic, Moré–Garbow–Hilstrom, and CUTEr (Constrained and Unconstrained Testing Environment, revisited) benchmark problem sets show that the obtained schema outperforms the existing adaptive schemas in terms of accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants, whose aim is to find those that are linked to a certain trait or illness. Due to the multivariate nature of these kinds of studies, machine learning methodologies have been already applied in them, showing good performance. This work presents a new methodology for GWAS that makes use of extreme learning machines and differential evolution. The proposed methodology was tested with the help of the genetic information (370,750 single-nucleotide polymorphisms) of 2049 individuals, 1076 of whom suffer from colorectal cancer. The possible relationship of 10 different pathways with this illness was tested. The results achieved showed that the proposed methodology is suitable for detecting relevant pathways for the trait under analysis with a lower computational cost than other machine learning methodologies previously proposed. [ABSTRACT FROM AUTHOR]

In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases. [ABSTRACT FROM AUTHOR]

The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

As one of the most promising forms of renewable energy, solar energy is increasingly deployed. The simulation and control of photovoltaic (PV) systems requires identification of their parameters. A Hybrid Adaptive algorithm based on JAYA and Differential Evolution (HAJAYADE) is developed to identify these parameters accurately and reliably. The HAJAYADE algorithm consists of adaptive JAYA, adaptive DE, and the chaotic perturbation method. Two adaptive coefficients are introduced in adaptive JAYA to balance the local and global search. In adaptive DE, the Rank/Best/1 mutation operator is put forward to boost the exploration and maintain the exploitation. The chaotic perturbation method is applied to reinforce the local search further. The HAJAYADE algorithm is employed to address the parameter identification of PV systems through five test cases, and the eight latest meta-heuristic algorithms are its opponents. The mean RMSE values of the HAJAYADE algorithm from five test cases are 9.8602 × 10−4, 9.8294 × 10−4, 2.4251 × 10−3, 1.7298 × 10−3, and 1.6601 × 10−2. Consequently, HAJAYADE is proven to be an efficient and reliable algorithm and could be an alternative algorithm to identify the parameters of PV systems. [ABSTRACT FROM AUTHOR]

Nature-inspired algorithms serve as the backbone of modern computing technology, and over the past three decades, the field has grown enormously. Many applications were solved by such algorithms and are replacing the traditional classical optimization processes. A recent naked mole-rat algorithm (NMRA) was proposed based on the mating patterns of naked mole-rats. This algorithm proved its worth in terms of competitiveness and application to various domains of research. The aim was to propose an algorithm based on NMRA, named enhanced NMRA (ENMRA), by mitigating the problems that this algorithm suffers from: slow convergence, poor exploration, and local optima stagnation. To enhance the exploration capabilities of basic NMRA, grey wolf optimization (GWO)-based search equations were employed. Exploitation was improved using population division methods based on local neighborhood search (LNS) and differential evolution (DE) equations. To avoid the local stagnation problem, a neighborhood search strategy around the best individual was utilized. Such improvements help the new variant to solve highly challenging optimization problems in contrast to existing algorithms. The efficacy of ENMRA was evaluated using CEC 2019 benchmark test suite. The results were statistically analyzed by the Wilcoxon rank-sum test and Friedman rank (f-rank) test. The resulting analysis proved that ENMRA is superior to the competitive algorithms for test functions CEC 2019 with overall effectiveness of 60.33%. Moreover, the real-world optimization problem from underground wireless sensor networks for an efficient cross-layer solution was also used to test the efficiency of ENMRA. The results of comparative study and statistical tests affirmed the efficient performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article mainly focuses on the utilization of shadowed type-2 fuzzy systems used to achieve the goal of dynamically adapting the parameters of two already known algorithms in the literature: the harmony search and the differential evolution algorithms. It has already been established that type-2 fuzzy logic enhances the performance of metaheuristics by enabling parameter adaptation; however, the utilization of fuzzy logic results in an increased execution time. For this reason, in this article, the shadowed type-2 fuzzy approach is put forward as a way of reducing execution time, while maintaining the good results that the complete type-2 fuzzy model produces. The harmony search and differential evolution algorithms with shadowed type-2 parameter adaptations were applied to the problem of optimally designing fuzzy controllers. The simulations were performed with the controllers working in an ideal situation, and then with a real situation under different noise levels in order to reach a conclusion regarding the performance of each of the algorithms that were applied. [ABSTRACT FROM AUTHOR]

Frequency Selective Surfaces (FSSs) have become increasingly popular during the last years due to their combined characteristics, which meet, in general, the requirements of the next-generation wireless communication networks. In this work, a cross-platform design framework for FSS structures is presented and evaluated by utilizing a recently introduced evolutionary optimization algorithm, namely, the Multi-Variant Differential Evolution (MVDE). To the best of the authors knowledge, this is the first time that the MVDE algorithm is applied to a design problem in Electromagnetics. The proposed design framework is described in detail and the utilized evolutionary algorithm is assessed in terms of its performance by applying several benchmark functions. In this context, the MVDE is comparatively evaluated against other popular evolutionary algorithms. Moreover, it is applied to the design and optimization of two different representative examples of FSS structures based on three use cases of unit cell geometry. Optimization results indicate the efficacy of the proposed framework by quantifying the performance of the designed FSS structures in terms of several system metrics. The optimized FSS structures exhibit dual-band operation and quite acceptable results in the ISM frequency bands of 2.45 GHz and 5.8 GHz. [ABSTRACT FROM AUTHOR]

The design of bipedal robots is generally fulfilled through considering a sequential design approach, where a synergistic relationship between its structure and control features is not promoted. Hence, a novel integrated structure-control design approach is proposed to simultaneously obtain the optimal structural description, the torque magnitudes, and the on/off time intervals for the control signal input of a semi-passive bipedal robot. The proposed approach takes advantage of the natural dynamics of the system and the control signal activation/deactivation for generating stable gait cycles with minimum energy consumption. Consequently, the passive features of the semi-passive bipedal robot are included in the integrated structure-control design process through evaluating the system behavior along consecutive passive and semi-passive walking stages. Then, the proposed design approach is formulated as a nonlinear discontinuous dynamic optimization problem, where the solution search is carried out using the differential evolution algorithm due to the discontinuities of the semi-passive bipedal robot dynamics. The results of the proposal are compared with those obtained by a sequential design process. The integrated structure-control design achieves a reduction of 63.55 % in the value of the performance function related to the synergy between the walking capability and energetic efficiency, with a reduction in the activation of the control and its magnitude of 95.41 % . [ABSTRACT FROM AUTHOR]

Recently, the differential evolution (DE) algorithm has been widely used to solve many practical problems. However, DE may suffer from stagnation problems in the iteration process. Thus, we propose an enhancing differential evolution with a rank-up selection, named RUSDE. First, the rank-up individuals in the current population are selected and stored into a new archive; second, a debating mutation strategy is adopted in terms of the updating status of the current population to decide the parent's selection. Both of the two methods can improve the performance of DE. We conducted numerical experiments based on various functions from CEC 2014, where the results demonstrated excellent performance of this algorithm. Furthermore, this algorithm is applied to the real-world optimization problem of the four-bar linkages, where the results show that the performance of RUSDE is better than other algorithms. [ABSTRACT FROM AUTHOR]

Manufacturing industry reflects a country's productivity level and occupies an important share in the national economy of developed countries in the world. Jobshop scheduling (JSS) model originates from modern manufacturing, in which a number of tasks are executed individually on a series of processors following their preset processing routes. This study addresses a JSS problem with the criterion of minimizing total quadratic completion time (TQCT), where each task is available at its own release date. Constructive heuristic and meta-heuristic algorithms are introduced to handle different scale instances as the problem is NP-hard. Given that the shortest-processing-time (SPT)-based heuristic and dense scheduling rule are effective for the TQCT criterion and the JSS problem, respectively, an innovative heuristic combining SPT and dense scheduling rule is put forward to provide feasible solutions for large-scale instances. A preemptive single-machine-based lower bound is designed to estimate the optimal schedule and reveal the performance of the heuristic. Differential evolution algorithm is a global search algorithm on the basis of population, which has the advantages of simple structure, strong robustness, fast convergence, and easy implementation. Therefore, a hybrid discrete differential evolution (HDDE) algorithm is presented to obtain near-optimal solutions for medium-scale instances, where multi-point insertion and a local search scheme enhance the quality of final solutions. The superiority of the HDDE algorithm is highlighted by contrast experiments with population-based meta-heuristics, i.e., ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). Average gaps 45.62, 63.38 and 188.46 between HDDE with ACO, PSO and GA, respectively, are demonstrated by the numerical results with benchmark data, which reveals the domination of the proposed HDDE algorithm. [ABSTRACT FROM AUTHOR]

This study proposes an adaptive fuzzy neural network (AFNN) based on a multi-strategy artificial bee colony (MSABC) algorithm for achieving an actual mobile robot navigation control. During the navigation control process, the AFNN inputs are the distance between the ultrasonic sensors and the angle between the mobile robot and the target, and the AFNN outputs are the robot's left- and right-wheel speeds. A fitness function in reinforcement learning is defined to evaluate the navigation control performance of AFNN. The proposed MSABC algorithm improves the poor exploitation disadvantage in the traditional artificial bee colony (ABC) and adopts the mutation strategies of a differential evolution to balance exploration and exploitation. To escape in special environments, a manual wall-following fuzzy logic controller (WF-FLC) is designed. The experimental results show that the proposed MSABC method has improved the performance of average fitness, navigation time, and travel distance by 79.75%, 33.03%, and 10.74%, respectively, compared with the traditional ABC method. To prove the feasibility of the proposed controller, experiments were carried out on the actual PIONEER 3-DX mobile robot, and the proposed navigation control method was successfully completed. [ABSTRACT FROM AUTHOR]