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Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. DE boasts several advantages, such as ease of implementation, reliability, speed, and adaptability. However, DE does have certain limitations, such as suboptimal solution exploitation and challenging parameter tuning. To address these challenges, this research paper introduces a novel algorithm called Enhanced Binary JADE (EBJADE), which combines differential evolution with multi-population and elites regeneration. The primary innovation of this paper lies in the introduction of strategy with enhanced exploitation capabilities. This strategy is based on utilizing the sorting of three vectors from the current generation to perturb the target vector. By introducing directional differences, guiding the search towards improved solutions. Additionally, this study adopts a multi-population method with a rewarding subpopulation to dynamically adjust the allocation of two different mutation strategies. Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. Experimental results, using the CEC2014 benchmark tests, demonstrate the strong competitiveness and superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The development of offshore oil fields confronts challenges associated with high water cut and low displacement efficiency. Reservoir injection-production optimization stands out as an effective means to reduce costs and enhance efficiency in offshore oilfield development. The process of optimizing injection and production in offshore oil reservoirs involves designing strategies for a large number of wells and optimization time steps, constituting a large-scale, complex, and costly optimization computation problem. In recent years, with the rapid advancements in big data and artificial intelligence technologies, sophisticated evolutionary computation methods have found widespread application in reservoir injection-production optimization problems. However, the abundance of intelligent optimization algorithms raises the question of how to choose a method suitable for the complex optimization background of offshore oilfield injection-production optimization. This paper provides a detailed overview of the application of an existing differential evolution algorithm (DE), conventional surrogate-assisted evolutionary algorithm (CSAEA), and global–local surrogate-assisted differential evolution (GLSADE) in the context of practical offshore oilfield injection-production optimization problems. A comprehensive comparison of their performance differences is presented. The study concludes that the global–local surrogate-assisted evolutionary algorithm is the most suitable method for addressing the current challenges in offshore oilfield injection-production optimization. [ABSTRACT FROM AUTHOR]

Autonomous region protection is a significant research area in multi-agent systems, aiming to empower defenders in preventing intruders from accessing specific regions. This paper presents a Multi-agent Region Protection Environment (MRPE) featuring fewer defenders, defender damages, and intruder evasion strategies targeting defenders. MRPE poses challenges for traditional protection methods due to its high nonstationarity and limited interception time window. To surmount these hurdles, we modify evolutionary reinforcement learning, giving rise to the corresponding multi-agent region protection method (MRPM). MRPM amalgamates the merits of evolutionary algorithms and deep reinforcement learning, specifically leveraging Differential Evolution (DE) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). DE facilitates diverse sample exploration and overcomes sparse rewards, while MADDPG trains defenders and expedites the DE convergence process. Additionally, an elite selection strategy tailored for multi-agent systems is devised to enhance defender collaboration. The paper also presents ingenious designs for the fitness and reward functions to effectively drive policy optimizations. Finally, extensive numerical simulations are conducted to validate the effectiveness of MRPM. [ABSTRACT FROM AUTHOR]

In the field of expensive optimization, numerous papers have proposed surrogate-assisted evolutionary algorithms (SAEAs) for a few thousand or even hundreds of function evaluations. However, in reality, low-cost simulations suffice for a lot of real-world problems, in which the number of function evaluations is moderately restricted, e.g., to several thousands. In such moderately restricted scenario, SAEAs become unnecessarily time-consuming and tend to struggle with premature convergence. In addition, tuning the SAEA parameters becomes impractical under the restricted budgets of function evaluations—in some cases, inadequate configuration may degrade performance instead. In this context, this paper presents a fast and auto-tunable evolutionary algorithm for solving moderately restricted expensive optimization problems. The presented algorithm is a variant of adaptive differential evolution (DE) algorithms, and is called emulation-based adaptive DE or EBADE. The primary aim of EBADE is to emulate the principle of sample-efficient optimization, such as that in SAEAs, by adaptively tuning the DE parameter configurations. Specifically, similar to Expected Improvement-based sampling, EBADE identifies parameter configurations that may produce expected-to-improve solutions, without using function evaluations. Further, EBADE incepts a multi-population mechanism and assigns a parameter configuration to each subpopulation to estimate the effectiveness of parameter configurations with multiple samples carefully. This subpopulation-based adaptation can help improve the selection accuracy of promising parameter configurations, even when using an expected-to-improve indicator with high uncertainty, by validating with respect to multiple samples. The experimental results demonstrate that EBADE outperforms modern adaptive DEs and is highly competitive compared to SAEAs with a much shorter runtime. [ABSTRACT FROM AUTHOR]

In the trajectory planning of crewless ships, the artificial potential field method is commonly used. The results obtained using the classic potential field model for path design are not optimal and cannot fully meet the trajectory design requirements of uncrewed ships. This paper uses the evolutionary potential field model for trajectory planning. The evaluation formula of the potential path is combined with the differential evolution algorithm to evaluate and optimize the potential. A quadratic optimization smoothing algorithm is designed to limit the maximum turning angle of the uncrewed ship. Simulation experiments show that this method is effective and reliable. [ABSTRACT FROM AUTHOR]

PID controllers can regulate and stabilize processes in response to changes and disturbances. This paper provides a comprehensive review of PID controller tuning methods for industrial applications, emphasizing intelligent and nature-inspired algorithms. Techniques such as Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are explored. Additionally, nature-inspired algorithms, including evolutionary algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Simulated Annealing (SA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Cuckoo Search (CS), Harmony Search (HS), and Grey Wolf Optimization (GWO), are examined. While conventional PID tuning methods are valuable, the evolving landscape of control engineering has led to the exploration of intelligent and nature-inspired algorithms to further enhance PID controller performance in specific applications. The study conducts a thorough analysis of these tuning methods, evaluating their effectiveness in industrial applications through a comprehensive literature review. The primary aim is to offer empirical evidence on the efficacy of various algorithms in PID tuning. This work presents a comparative analysis of algorithmic performance and their real-world applications, contributing to a comprehensive understanding of the discussed tuning methods. Findings aim to uncover the strengths and weaknesses of diverse PID tuning methods in industrial contexts, guiding practitioners and researchers. This paper is a sincere effort to address the lack of specific quantitative comparisons in existing literature, bridging the gap in empirical evidence and serving as a valuable reference for optimizing intelligent and nature-inspired algorithms-based PID controllers in various industrial applications. [ABSTRACT FROM AUTHOR]

Formulations of structural optimization problems are proposed in this paper to automatically find the best grouping of columns in 3D steel buildings. In these formulations, the conflicting objective functions, minimized simultaneously, are the weight of the structure and the number of different groups of columns. In other words, the smaller the number of different groups of columns, the greater the weight of the structure, and the greater the number of groups, the smaller the structure's weight. The design variables are the bracing system configuration, column cross-section orientation, and assigned W-shaped profile indices for columns, beams, and braces. The design constraints are the allowable displacements, strength, and geometric considerations. After solving the multi-objective optimization problem, the result is a Pareto front, presenting non-dominated solutions. Three evolutionary algorithms based on differential evolution are adopted in this paper to solve three computational experiments. Even if preliminary groupings of columns are adopted, considering architectural aspects such as the symmetry of the structure, it is possible to discover other interesting structural configurations that will be available to the decision maker, who will be able to make their choices based on the impacts on manufacturing, cutting, transporting, checking and welding. [ABSTRACT FROM AUTHOR]

Nonlinear equation systems (NESs) are ubiquitous in scientific and engineering domains, necessitating efficient methods for discovering multiple roots simultaneously. Employing evolutionary algorithms to solve NESs has received significant attention. To simultaneously locate multiple roots in a single run, this paper proposes a differential evolution algorithm with adaptive niching and reinitialisation (DE-ANR). The proposed differential evolution algorithm integrates four strategies to address the challenges posed by NESs. Firstly, DE-ANR employs a dynamic niche size control mechanism to adjust the niche size during evolution, striking a balance between exploration and exploitation. Secondly, a lifetime mechanism is introduced to identify stagnating individuals for reinitialisation, ensuring diversity of the population and preventing premature convergence. Thirdly, an improved archive technique is employed to conserve computational resources by eliminating redundant solutions, directing the algorithm's efforts toward unexplored regions of the solution space. Finally, an adaptive strategy is introduced to dynamically adjust the scaling factor F based on the feedback of evolving population, simplifying parameter tuning. Extensive evaluations on 30 diverse NES problems and the comparison results with 12 state-of-the-art algorithms clearly show the superiority of proposed method. [ABSTRACT FROM AUTHOR]

Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak shaving or load shifting to compensate for heat losses in the pipeline. This helps to avoid exceedance of power plant capacity. The purpose of this study is to automate the process of building machine learning (ML) models to solve a short-term power demand prediction problem. The dataset contains a district heating network's measured hourly power consumption and ambient temperature for 415 days. In this paper, we propose a hybrid evolutionary-based algorithm, named GA-SHADE, for the simultaneous optimization of ML models and feature selection. The GA-SHADE algorithm is a hybrid algorithm consisting of a Genetic Algorithm (GA) and success-history-based parameter adaptation for differential evolution (SHADE). The results of the numerical experiments show that the proposed GA-SHADE algorithm allows the identification of simplified ML models with good prediction performance in terms of the optimized feature subset and model hyperparameters. The main contributions of the study are (1) using the proposed GA-SHADE, ML models with varying numbers of features and performance are obtained. (2) The proposed GA-SHADE algorithm self-adapts during operation and has only one control parameter. There is no fine-tuning required before execution. (3) Due to the evolutionary nature of the algorithm, it is not sensitive to the number of features and hyperparameters to be optimized in ML models. In conclusion, this study confirms that each optimized ML model uses a unique set and number of features. Out of the six ML models considered, SVR and NN are better candidates and have demonstrated the best performance across several metrics. All numerical experiments were compared against the measurements and proven by the standard statistical tests. [ABSTRACT FROM AUTHOR]

This paper addresses the parameter identification of a one-link flexible manipulator based on the experimental measurement of the inputs/outputs, the finite element model, and the application of evolutionary algorithms. A novel approach is proposed to find the values of inertia, stiffness, and damping parameters by minimizing the difference between the numerical model's outputs and the testbed's outputs, thus considering the joint position and acceleration of the link's tip. The dynamic model is initially obtained using the finite element method and the Lagrange principle. A prototype of a single one-link flexible manipulator is used in the experimental application, wherein the servomotor applies the input torque, and the outputs are the joint angle and the link's tip acceleration. Then, an optimization problem minimizes the difference between the numerical and experimental outputs to determine the set of parameters using evolutionary algorithms. A comparative analysis to obtain the identified parameters is established using genetic algorithms, particle swarm optimization, and differential evolution. The proposed identification approach permitted the determination of the dynamic parameters based on the complete dynamic model of the flexible-link manipulator, which is different from the approaches reported in the literature that identify a simplified model. This information is essential for the design of the motion and vibration control laws. [ABSTRACT FROM AUTHOR]

This paper presents a two-step approach for optimizing the configuration of a mobile photovoltaic-diesel-storage microgrid system. Initially, we developed a planning configuration model to ensure a balance between the mobility of components and a sustainable power supply. Then, we introduced a method that merges optimization and decision-making. The first phase identifies Pareto optimal solutions (POSs) with a favorable distribution by using a multi-objective evolutionary algorithm with classification-based preselection (CPS-MOEA). In the second phase, we utilize the fuzzy C-means algorithm (FCM) and the grey relational projection (GRP) method for comprehensive decision-making. This aims to select the most suitable and compromise solution from the POSs, closely aligning with the decision-maker's preferences. Beyond addressing the optimal planning and configuration issue, the experimental results show that the method surpasses other widely used multi-objective optimization algorithms, including the Preference Inspired Co-evolution Algorithm (PICEA-g), the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO), and the third stage of Generalized Differential Evolution (GDE3). [ABSTRACT FROM AUTHOR]

In the real world, data often exhibits high-dimensional and complex characteristics. In addition, an object may correspond to multiple class labels. Therefore, filtering and processing such data has become a hot research topic. Multi-label feature selection is one of the key preprocessing techniques for effectively solving redundant information. However, due to the high complexity of multi-label learning feature selection and involving optimization of multiple objectives, careful consideration is required to achieve effective optimization. To address these issues, this paper proposes a multiple reference points-based multi-objective feature selection for multi-label learning. Firstly, the neighborhood crossover strategy utilizes individuals in the same neighborhood for crossover, thereby enhancing the search effect within that decision space. The distribution-based mutation strategy adjusts the mutation probability of different features based on their distribution in selected candidate solutions to increase the likelihood of potentially useful features being selected. Secondly, the multiple reference points-based selection strategy uses a scalar function to uniformly evaluate offspring individuals and control their distance from each other through multiple reference points. This approach helps in selecting convergent solutions with balanced diversity. Finally, experimental results demonstrate that the proposed algorithm attains an optimal performance of 83% across fourteen widely used datasets, outperforming ten state-of-the-art algorithms in terms of effectiveness and performance in multi-label feature selection. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an evolutionary algorithm that is straightforward but effective and has been satisfactorily implemented in a variety of fields. However, the DE relies heavily on the mutation strategy, and utilizing different mutation strategies can boost DE's performance at different phases during the evolution. This paper proposes a DE variant with the strategy transformation (qSTDE) based on a quartile. The proposed qSTDE uses feedback information from individuals and the entire population distribution to determine the position ofmutation strategy transformation, balancing theDE's exploration and exploitation capabilities. Individuals are first ranked in accordance with their fitness values, and then the ranked population is separated into four sub-populations using quartile points. Next, the Euclidean distances between the best individual and four individuals that are randomly selected from the four sub-populations are calculated, as well as their Euclidean distances to the searching space ratios. Further, the evolution process is completely separated into two phases, and the transformation position is determined through the comparison of ratios for a given threshold. Finally, different mutation strategies are used in different stages depending on the distinction between the two stages and the transformation position. The proposed qSTDE is evaluated on CEC2005 and CEC2014 benchmark test functions. The experimental results show that the qSTDE is efficient and competitive contrasted to the state-of-the-art DE variants. [ABSTRACT FROM AUTHOR]

This paper focuses on robustly stabilizing stable and unstable fractional-order plants with one uncertain fractional-order term and interval uncertainties using fractional order $ PI^{\mu }D^{\lambda } $ PIμDλ controllers. Two necessary and sufficient conditions are provided to check the robust stability of the closed-loop control system. Moreover, the D-decomposition technique is utilized to determine the robust stability region of the system. Subsequently, evolutionary algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), can be utilized to discover a fractional-order controller within the region of robust stability. This work introduces three primary contributions, outlined as follows: (1) Utilizing a graphical approach, a set of stabilizing controller is obtained. (2) Rather than employing just a single stabilizing fractional-order controller, a collection of controllers is provided for the control system. (3) Employing evolutionary algorithms to find an optimal fractional-order controller. Finally, four numerical examples are presented to validate the results. [ABSTRACT FROM AUTHOR]

This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. [ABSTRACT FROM AUTHOR]

Many practical problems involve the optimization of computationally expensive blackbox functions. The computational cost resulting from expensive function evaluations considerably limits the number of true objective function evaluations allowed in order to find a good solution. In this paper, we propose a clustering-based surrogate-assisted evolutionary algorithm, in which a clustering-based local search technique is embedded into the radial basis function surrogate-assisted evolutionary algorithm framework to obtain sample points which might be close to the local solutions of the actual optimization problem. The algorithm generates sample points cyclically by the clustering-based local search, which takes the cluster centers of the ultimate population obtained by the differential evolution iterations applied to the surrogate model in one cycle as new sample points, and these new sample points are added into the initial population for the differential evolution iterations of the next cycle. In this way the exploration and the exploitation are better balanced during the search process. To verify the effectiveness of the present algorithm, it is compared with four state-of-the-art surrogate-assisted evolutionary algorithms on 24 synthetic test problems and one application problem. Experimental results show that the present algorithm outperforms other algorithms on most synthetic test problems and the application problem. [ABSTRACT FROM AUTHOR]

Hyper-parameter tuning of machine learning models has become a crucial task in achieving optimal results in terms of performance. Several researchers have explored the optimisation task during the last decades to reach a state-of-the-art method. However, most of them focus on batch or offline learning, where data distributions do not change arbitrarily over time. On the other hand, dealing with data streams and online learning is a challenging problem. In fact, the higher the technology goes, the greater the importance of sophisticated techniques to process these data streams. Thus, improving hyper-parameter self-tuning during online learning of these machine learning models is crucial. To this end, in this paper, we present MESSPT, an evolutionary algorithm for self-hyper-parameter tuning for data streams. We apply Differential Evolution to dynamically-sized samples, requiring a single pass-over of data to train and evaluate models and choose the best configurations. We take care of the number of configurations to be evaluated, which necessarily has to be reduced, thus making this evolutionary approach a micro-evolutionary one. Furthermore, we control how our evolutionary algorithm deals with concept drift. Experiments on different learning tasks and over well-known datasets show that our proposed MESSPT outperforms the state-of-the-art on hyper-parameter tuning for data streams. [ABSTRACT FROM AUTHOR]

This paper presents an optimization tool for jacket structures to support Offshore Wind Turbines (OWTs). The tool incorporates several combinations of optimization algorithms and constraint-handling techniques (CHTs): Genetic Algorithm; Differential Evolution (DE); Tournament Selection Method; Multiple Constraint Ranking (MCR); Adaptive Penalty Method, and Helper-and-Equivalent Optimization. The objective function regards the minimization of the jacket weight; the design variables are the diameter and thickness of the tubular members. The constraints are related to natural frequencies and Ultimate Limit State criteria. The candidate solutions are evaluated by full nonlinear time-domain Finite Element coupled analyses. To assess the optimization algorithms and CHTs, a case study is presented for the standardized OWT/jacket structure from the Offshore Code Comparison Collaboration Continuation project. First, a numerical model is built and validated, in terms of masses, natural frequencies, and vibration modes; then, this model is employed to run the optimization tool for all combinations of optimization algorithms and CHTs. The results indicate that, while all methods lead to feasible optimal solutions that comply with the constraints and present considerable weight reductions, the best performer is the combination of the DE algorithm with the MCR constraint-handling technique. [ABSTRACT FROM AUTHOR]

The fixed-charge transportation (FCT) problem, an extension of the classical transportation problem, holds significance in practical logistics scenarios where fixed charges play a crucial role. Fixed charges categorize it as an NP-hard problem, posing challenges for conventional methods due to their inefficiency, high computational costs, and susceptibility to local optima. This paper introduces an enhanced evolutionary algorithm, offline learning-based competitive swarm optimization (OLCSO), tailored to address the FCT problems efficiently. OLCSO draws inspiration from offline learning, where particles benefit from the experiences of high performers. The algorithm incorporates (i) a ranking-based mechanism where losers learn from peer-to-peer winners based on their ranks, (ii) a two-phase cooperative evolutionary mechanism enhancing searchability and convergence of the algorithm. In the first phase, losers learn from personal best experiences and the shifted center of the population, boosting diversity. In the second phase, losers learn from winners and the center of the population, improving exploitation, (iii) a mutation strategy is embedded to update winner locations in each evolutionary procedure, and (iv) to ensure a feasible solution to FCT problems, the paper also introduces negative and fractional repair mechanisms. OLCSO is applied to solve two sets of FCT problems, linear and non-linear. Experimental results are compared with various heuristic and metaheuristic techniques, commonly used for these problems, using diverse metrics. A comparative analysis of OLCSO's design elements is also conducted. The results demonstrate OLCSO's superiority over existing algorithms. [ABSTRACT FROM AUTHOR]

• A model was created to examine the reliability of maintenance budget allocation. • The model was applied to network-level pavement maintenance in Canada. • The impacts of algorithm selection and parameter tuning were studied. • The significance of pavement clustering in increasing reliability was highlighted. • The importance of incorporating actual pavement improvement curves was clarified. The purpose of this paper is to increase the reliability of the network-level pavement maintenance budget allocation by reducing uncertainties of algorithm selection and parameter tuning. In this paper, reliability is defined as the ability of a network-level pavement maintenance plan to improve the condition of a pavement network within a certain budget. In order to quantify reliability, the reliability index is defined as the ratio between the post-maintenance network condition and the net maintenance cost. With this purpose in mind, a two-objective optimization model was developed. To test its applicability, the model was applied to a pavement network in Canada. The model was solved using the Non-Dominated Sorting Genetic Algorithm II and the differential evolution algorithms. Finally, the reliability indices of algorithms and termination criteria were computed. The results indicated that the differential evolution algorithm recommended less frequent but more intense interventions that made the solutions expensive and less reliable. This paper contributed to the network-level pavement maintenance body of knowledge by (1) developing a multi-objective optimization model to reduce uncertainties of parameter tuning and algorithm selection; (2) increasing the reliability of budget allocation; (3) showcasing the significance of pavement clustering in increasing reliability; and (4) developing and incorporating actual pavement improvement curves. [Display omitted] [ABSTRACT FROM AUTHOR]

This editorial discusses the importance of reproducibility in the field of evolutionary computation (EC) and highlights the need for improvement in this area. The editorial acknowledges that reproducibility issues in EC have been a topic of discussion, but there are few studies and reproducibility studies themselves are rare. The editorial also introduces three papers featured in a special issue of Evolutionary Computation that address different aspects of reproducibility in EC research. The editorial concludes by noting that there is a cultural shift within computer science, including EC, towards expecting and rewarding reproducibility efforts. [Extracted from the article]

Wireless sensor network is large-scale, self-organizing and reliable. It is widely used in the military, disaster management, environmental monitoring, and other fields. Algorithms for localization can be classified as range-based or range-free based on their ability to achieve effective localization. Range-based algorithms require hardware support, which increases deployment costs and complexity. Instead of measuring distance directly, range-free algorithms estimate the position based on hop counts between nodes. While simpler in terms of hardware requirements, this algorithm suffers from large localization errors. To address this problem, this paper proposes an improved 3D DV-Hop localization algorithm (3D DEHDV-Hop) using a differential evolutionary algorithm. First of all, theoretical analysis shows a correlation between the volume of the intersection area containing the communication range between neighbors and the number of shared single-hop nodes. Then, using the number of shared single-hop nodes between nodes, the number of hops is converted from a discrete value to an exact continuous value. Finally, the localization problem is transformed into a minimum optimization problem by incorporating a differential evolutionary algorithm. As compared to the other four algorithms compared, 3D DEHDV-Hop improves localization accuracy by an average of 10.3% under different anchor node densities, 13.7% under different communication radiuses, and 12.1% under different anchor node numbers. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is one of the most popular and powerful branches of evolutionary algorithm family. However, even many state-of-the-art DE-based variants still exist weakness such as improper parameter adaptation and population stagnation during the later stage of evolution. To mitigate these deficiencies, differential evolution with fitness-difference based parameter control and hypervolume diversity indicator (FDHD-DE) is proposed in this paper. Firstly, a semi-adaptive adaptation scheme for control parameters is proposed, in which the generation of scale factor and crossover rate is modified by dividing into two stages, thus enhancing the efficiency of parameter adaptation. Secondly, a novel fitness-based weighting strategy is proposed to improve the performance of existing success history-based adaptation by employing a novel approach of utilizing fitness information. Finally, a hypervolume-based diversity indicator and corresponding dimension exchange strategy are proposed to alleviate the problem of population stagnation. The performance of FDHD-DE is verified on the 88 benchmark functions from Congress on Evolutionary Computation (CEC) 2013, CEC 2014, and CEC 2017 test suites on 10D, 30D and 50D and a real-world application. The experiment results are compared with several state-of-art DE variants, and the results show that FDHD-DE has better performance, both in terms of solution accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow problem, called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution operator in a single algorithmic framework. Additionally, an adaptive method is proposed to update the number of solutions evolved by each DE operator based on both the diversity of the population and the quality of solutions. This adaptive method has the ability to maintain diversity at the early stages of the optimization process and boost convergence at the later ones. The performance of the proposed MODE-OPF is tested by solving OPF problems for both small and large IEEE bus systems (i.e., IEEE-30 and IEEE-118) while considering intermittent solar and wind power generation. To prove the suitability of this proposed algorithm, its performance has been compared against several state-of-the-art optimization algorithms, where MODE-OPF outperforms other algorithms in all experimental results thereby improving a network's performance with lower cost. MODE-OPF decreases the total generation cost up to 24.08%, the real power loss up to 6.80% and the total generation cost with emission up to 8.56%. • Development of an adaptive method (AM) for optimizing diversity and solution quality. • Innovative constraint handling approach, progressively adding constraints for improved performance. • Incorporation of intermittent renewable energy models for realistic problem solving. • Extensive validation on IEEE 30-bus and IEEE 118-bus networks, outperforming state-of-the-art algorithms in cost, loss, and environmental impact reduction. [ABSTRACT FROM AUTHOR]

The expensive optimization problem, widespread in real-world applications, poses great challenges to traditional evolutionary algorithms (EAs) due to its costly evaluations. A lot of investigations have proved that it is a practical way to incorporate surrogate models into EAs to reduce the number of time-consuming evaluations. Although various types of surrogate models from the community of machine learning, such as regression models that approximate the fitness values of candidate solutions or classification models that predict candidate solutions' category, have been widely applied separately, none of them consistently outperforms the other. Considering the intrinsic consistency of alleviating computational burden to improve the optimization efficiency, this paper introduces a two-layer architecture to integrate regression and classification models and synergize global and local evolution for high-dimensional expensive many-objective optimization problem (EMaOP). To implement this idea, the Kriging model based on incremental learning is utilized to explore the potential promising space. Further, multi local support vector machine (SVM) classifiers assisted improved differential evolution is run by turn with the global search to accelerate convergence. Accordingly, a novel surrogate-assisted EA, named Classification- and Regression-assisted evolutionary algorithm (CR-SAEA), is proposed on this basis. Experimental studies on several benchmark functions with up to 11 objectives and 200 dimensions confirm the effectiveness of the proposed algorithm and its competitiveness against state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In the domain of automated test case generation for path coverage (ATCG-PC), commonly used evolutionary optimization algorithms face significant computational overhead due to the necessity of executing the test program for individual fitness evaluations. To address this challenge and enhance efficiency while ensuring comprehensive coverage, this paper proposes a Hierarchical Surrogate-Assisted Differential Evolution (HSADE) approach. HSADE innovatively incorporates two surrogate models: a Global Surrogate Model (GSM) utilizing Gaussian Process Regression (GPR) for a macroscopic view of the fitness landscape, and a Local Surrogate Model (LSM) employing Radial Basis Function Network (RBFN) for precise fitness estimations in targeted subspaces. This hierarchical approach facilitates a two-phase optimization process: an initial coarse search to pinpoint areas of interest followed by a focused, fine-grained search for precision, where LSM is combined with differential evolution (DE) algorithm for fine optimization in these regions. To mitigate the risk of converging to local optima, HSADE employs Random Opposition-Based Learning (ROBL) to enhance the population diversity, leveraging the uncertainty assessment capabilities of the GPR. Our experimental validation, conducted on a diverse set of benchmark programs, demonstrates HSADE's effectiveness in reducing the number of fitness evaluations and computational time required for generating high-quality test cases. Compared to established evolutionary algorithms, HSADE achieves superior path coverage with significantly less computational effort. • An ATCG-PC method based on DE and Hierarchical surrogate model (HSM) is proposed. • We proposed HSM for reduced fitness costs and coarse-to-fine optimization. • We evaluated our method's effectiveness across various benchmark programs. • Adopted modified DE and random perturbations for faster convergence and diversity. • Our method is found to be efficient in generating test cases for path coverage. [ABSTRACT FROM AUTHOR]

The differential evolution (DE) algorithm is widely regarded as one of the most influential evolutionary algorithms for addressing complex optimization problems. However, the fixed mutation strategy limits the adaptive ability of DE, and the lack of utilization of historical information limits the optimization ability of DE. In this paper, an indicator-based self-learning differential evolution algorithm (ISDE) is proposed. A jump out mechanism based on deep reinforcement learning is adopted to control the mutation intensity of the population. The neural network in the jump out mechanism is designed as a decision maker. The mutation intensity of the population is controlled by the neural network, and the neural network are trained by a double deep Q network algorithm based on the continuous data generated during the evolution process. A population range indicator (PRI) is utilized to describe individual differences in the population. A diversity maintenance mechanism is designed to maintain individual differences according to the value of PRI. The experimental results reveal that the comprehensive performance of ISDE is superior to comparison algorithms on CEC 2017 real-parameter numerical optimization. [ABSTRACT FROM AUTHOR]

As the fields of engineering, energy, and geology become increasingly complex, decision makers face escalating challenges that require skilled solutions to meet practical production needs. Evolutionary algorithms, inspired by biological evolution, have emerged as powerful methods for tackling intricate optimization problems without relying on gradient data. Among these, the tree-seed algorithm (TSA) distinguishes itself due to its unique mechanism and efficient searching capabilities. However, an imbalance between its exploitation and exploration phases can lead it to be stuck in local optima, impeding the discovery of globally optimal solutions. This study introduces an improved TSA that incorporates water-cycling and quantum rotation-gate mechanisms. These enhancements assist the algorithm in escaping local peaks and achieving a more harmonious balance between its exploitation and exploration phases. Comparative experimental evaluations, using the CEC 2017 benchmarks and a well-known metaheuristic algorithm, demonstrate the upgraded algorithm's faster convergence rate and enhanced ability to locate global optima. Additionally, its application in optimizing reservoir production models underscores its superior performance compared to competing methods, further validating its real-world optimization capabilities. [ABSTRACT FROM AUTHOR]

When the evolutionary algorithm is applied to handle high-dimensional expensive multi-objective optimization problems (MOPs), population evolution is crucial since it controls exploration and exploitation and decides if promising candidate solutions could be generated. However, little attention has been paid to this issue, evolution operators based on Genetic Algorithm (GA) and Differential Evolution (DE) are still the two most common approaches, whose convergence on high-dimensional MOPs with a limited number of fitness evaluations remains challenging. In this paper, we propose a decomposition-based local learning strategy to accelerate convergence in the high-dimensional search space of MOPs. Specifically, an individual is updated by learning from one of the best solutions of its corresponding local area based on the multi-objective decomposition approach. Accordingly, a surrogate-assisted evolutionary algorithm is proposed for better solving expensive high-dimensional MOPs. Experimental studies on MOPs with up to 100 decision variables and with 300 fitness evaluations demonstrate the effectiveness of the proposed method. Furthermore, we use the proposed method to solve a 2-objective shape design problem of the blended wing-body underwater glider (BWBUG) with 39 decision variables, and an impressive solution set is obtained. • Efficacy of evolution operators on high-dimensional EMOPs is analyzed. • Propose a novel evolution strategy based on decomposition for high-dimensional EMOPs. • A surrogate-assisted evolutionary algorithm is accordingly developed. • Comprehensive experimental studies on benchmark problems and an engineering application. [ABSTRACT FROM AUTHOR]

Resource allocation in logistics poses intricate challenges due to its complexity and non-linearity. This paper presents a novel warehouse human resource scheduling model that combines queuing theory and integer programming. We introduce the Adaptive Quantum Differential Evolution (AQDE) algorithm, inspired by quantum computation, to address the optimization complexities. Our approach demonstrates superior global convergence accuracy and speed compared to other DE variants and evolutionary algorithms. The efficacy of AQDE is validated through experiments using real logistics warehouse data, showcasing its efficiency in allocating logistics resources. • Optimization of logistics warehousing staff scheduling has good practical significance. • A multi-objective evolutionary algorithm combined with quantum computing is proposed. • A logistics warehousing scheduling model based on reality is designed. • An improved differential evolution algorithm is used to enhance searchability. • Different scheduling results are given for different scenarios. [ABSTRACT FROM AUTHOR]

Locating multiple roots of nonlinear equation systems (NESs) remains a challenging and meaningful task in the numerical optimization community. Although a large number of NES-solving approaches have been put forward, they can only find the roots of one NES at a time. In this paper, we develop a novel NES-solving algorithm based on evolutionary multitasking referred to as EMNES, the goal of which is to effectively find the multiple roots of multiple different NESs simultaneously in a single run through knowledge sharing and transfer. Specifically, firstly a NES-solving framework based on evolutionary multitasking is proposed. Then an efficient multi-task evolutionary algorithm based on neighborhood-based speciation differential evolution for NESs is designed. Finally, combining Gaussian distribution and uniform distribution, a novel resource release strategy is proposed to release the found roots to improve resource utilization and increase population diversity. Numerous experimental results reveal that the proposed EMNES algorithm can achieve a higher root rate and success rate when compared with several well-established algorithms on thirty NESs. Furthermore, simulation results on a more complex test set show that the proposed EMNES is able to locate more roots than most comparison algorithms. • An evolutionary multitasking NES-solving framework is proposed. • Relationship of different NESs is established by a unified search space. • An adaptive mutation strategy with knowledge transfer is presented. • A novel resource release strategy is developed to improve the utilization of computing resources. [ABSTRACT FROM AUTHOR]

This paper aims to formulate the multi-objective structural optimization problem to find the best member grouping of truss structures. The weight of the structure and the different number of discrete cross-sectional areas are the conflicting objective functions to be minimized simultaneously, generating a Pareto front presenting the non-dominated solutions. Sixteen multi-objective evolutionary algorithms are adopted to solve the proposed optimization problems. Several benchmark problems and a new proposed optimization problem are analyzed. Even though original groupings based on symmetry are defined, it is possible to discover other competitive structural member configurations that can interest decision-makers regarding manufacturing, cutting, transportation, checking, and welding. • Best member-grouping of trusses is found by a multi-objective optimization. • Weight and the distinct number of cross-sectional areas are the objective functions. • Several benchmark problems not yet discussed in the literature are analyzed. • Pareto fronts are obtained using differential evolution (DE) algorithms. • Performance indicators are adopted to evaluate the DE algorithms. [ABSTRACT FROM AUTHOR]

• The gradient information and dimension perturbation mutation are integrated. • Both the local and global search potentials are explored for each solution. • The dynamic local and global searches are adapted to the current search state. • Effective exploration and exploitation are designed in the reduced search space. • GIDPM performs better than MMA in designing the sandwich panel with truss cores. Surrogate-assisted evolutionary algorithms (SAEAs) have recently shown excellent abilities in solving computationally expensive optimization problems (EOPs), but most of them are not designed for high-dimensional EOPs (HEOPs). A new algorithm where the gradient information is well-incorporated into dimension perturbation mutation called as GIDPM for HEOPs, is proposed and fully investigated in this paper. Specifically, for each solution, a local candidate offspring pool is obtained by combining the gradient descent information and dimension perturbation mutation, and the DE/current-to-rand/2/bin is employed to generate a global candidate offspring pool. Then an offspring selection strategy is used to identify the exact offspring solutions, in which the overall qualities of two pools, the directions of near gradient descent of candidate solutions, and the distributions and RBF predictions of candidate solutions are considered in turn, to achieve appropriate local exploitation or global exploration. In addition, an Autoencoder-embedded search strategy is used to quickly jump out of the local optima caused by some small ripples or oscillations of the landscape after each several iterations to achieve leapfrog search. The GIDPM maintains a good balance between exploration and exploitation. Experimental studies on benchmark problems demonstrate that the GIDPM achieves better convergence performance when the problem dimension is larger than or equal to 30, and the better performance of GIDPM can be achieved with the increase of dimension. The GIDPM is also used for the design of the sandwich panel with truss cores. Compared with gradient-based algorithm, the GIDPM obtains the design with better structural performance. [ABSTRACT FROM AUTHOR]

The existing algorithms for solving multi-objective optimization problems fall into three main categories: Decomposition-based, dominance-based, and indicator-based. Traditional multi-objective optimization problems mainly focus on objectives, treating decision variables as a total variable to solve the problem without considering the critical role of decision variables in objective optimization. As seen, a variety of decision variable grouping algorithms have been proposed. However, these algorithms are relatively broad for the changes of most decision variables in the evolution process and are time-consuming in the process of finding the Pareto frontier. To solve these problems, a multi-objective optimization algorithm for grouping decision variables based on extreme point Pareto frontier (MOEA-DV/EPF) is proposed. This algorithm adopts a preprocessing rule to solve the Pareto optimal solution set of extreme points generated by simultaneous evolution in various target directions, obtains the basic Pareto front surface to determine the convergence effect, and analyzes the convergence and distribution effects of decision variables. In the later stages of algorithm optimization, different mutation strategies are adopted according to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals, thus enhancing the performance of the algorithm. Evaluation validation of the test functions shows that this algorithm can solve the multi-objective optimization problem more efficiently. [ABSTRACT FROM AUTHOR]

The exploration of premium and new locations is regarded as a fundamental function of every evolutionary algorithm. This is achieved using the crossover and mutation stages of the differential evolution (DE) method. A best-and-worst position-guided novel exploration approach for the DE algorithm is provided in this study. The proposed version, known as "Improved DE with Best and Worst positions (IDEBW)", offers a more advantageous alternative for exploring new locations, either proceeding directly towards the best location or evacuating the worst location. The performance of the proposed IDEBW is investigated and compared with other DE variants and meta-heuristics algorithms based on 42 benchmark functions, including 13 classical and 29 non-traditional IEEE CEC-2017 test functions and 3 real-life applications of the IEEE CEC-2011 test suite. The results prove that the proposed approach successfully completes its task and makes the DE algorithm more efficient. [ABSTRACT FROM AUTHOR]

Supply chain digital twin has emerged as a powerful tool in studying the behavior of an actual supply chain. However, most studies in the field of supply chain digital twin have only focused on what-if analysis that compares several different scenarios. This study proposes a data-driven evolutionary algorithm to efficiently solve the service constrained inventory optimization problem using historical data that generated by supply chain digital twins. The objective is to minimize the total costs while satisfying the required service level for a supply chain. The random forest algorithm is used to build surrogate models which can be used to estimate the total costs and service level in a supply chain. The surrogate models are optimized by an ensemble approach-based differential evolution algorithm which can adaptively use different search strategies to improve the performance during the computation process. A three-echelon supply chain digital twin on the geographic information system (GIS) map in real-time is used to examine the efficiency of the proposed method. The experimental results indicate that the data-driven evolutionary algorithm can reduce the total costs and maintain the required service level. The finding suggests that our proposed method can learn from the historical data and generate better inventory policies for a supply chain digital twin. [ABSTRACT FROM AUTHOR]

Latin hypercube design (LHD) is one of the most frequently used sampling methods. However, most LHDs generate data samples in a manner that hinders computational efficiency and space-filling performance when high dimensions and large samples are involved. Therefore, a sequential recursive evolution Latin hypercube design (RELHD) is proposed in this article, which adopts a permutation inheritance algorithm to update and optimize the LHD. A recursive split algorithm is also proposed and used to enhance the computational efficiency by dividing the sample set into smaller subsets. Numerical experiments demonstrate that the space-filling quality of the RELHD compares well with the enhanced stochastic evolutionary algorithm (ESE) in complex problems with large samples and high dimensions, with RELHD having a significantly higher computational efficiency than ESE. Finally, the sequential approach of RELHD proves to be a more efficient strategy when dealing with sampling-based analysis problems. [ABSTRACT FROM AUTHOR]

• An output-only structural damage identification method is proposed. • A novel QHEA integrating Jaya, DE, Q-learning algorithm is developed. • The responses reconstruction technique using Duhamel's integral is presented. • The effects of measurement noise and modeling error are investigated. • The proposed method is validated with numerical and experimental studies. Various structural damage identification methods have been developed and employed, while the absence of input excitation measurements may pose a huge challenge in their application since the input forces such as seismic load, traffic load, wind load, are not directly measurable. To address this issue, in the present paper, a novel output-only structural damage detection approach based on Q-learning hybrid evolutionary algorithm (QHEA) and response reconstruction technique is presented. On the one hand, the external excitation and structural acceleration responses are reconstructed with the aid of response reconstruction and Tikhonov regularization techniques in time domain. Structural damage identification can be formulated as an optimization-based inverse problem. On the other hand, a new optimization framework QHEA integrating Jaya algorithm, differential evolution, Q-learning algorithm is developed as search tool. The unknown structural parameters and unmeasured input force are iteratively updated by using two different measurement sets until the reconstructed acceleration responses agree well with the measured responses. Numerical examples involving a cantilever beam structure under single excitation and a simply-supported 51-bar truss structure under multiple excitations, as well as an experimental five-floor steel frame structure in the laboratory are carried out to validate the effectiveness of the proposed method. The final results demonstrate that the proposed method can accurately detect damage locations and quantify damage extents without the information of input excitation. In addition, the superiority of QHEA over other heuristic algorithms, the uncertainties of measurement noise and modeling errors on damage identification results are further examined. [ABSTRACT FROM AUTHOR]

The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward–backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case. [ABSTRACT FROM AUTHOR]