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Many-objective optimization problems (MaOPs) are challenging tasks involving optimizing many conflicting objectives simultaneously. Decomposition-based many-objective evolutionary algorithms have effectively maintained a balance between convergence and diversity in recent years. However, these algorithms face challenges in accurately approximating the complex geometric structure of irregular Pareto fronts (PFs). In this paper, an information entropy-driven evolutionary algorithm based on reinforcement learning (RL-RVEA) for many-objective optimization with irregular Pareto fronts is proposed. The proposed algorithm leverages reinforcement learning to guide the evolution process by interacting with the environment to learn the shape and features of PF, which adaptively adjusts the distribution of reference vectors to cover the PFs structure effectively. Moreover, an information entropy-driven adaptive scalarization approach is designed in this paper to reflect the diversity of nondominated solutions, which facilitates the algorithm to balance multiple competing objectives adaptively and select solutions efficiently while maintaining individual diversity. To verify the effectiveness of the proposed algorithm, the RL-RVEA compared with seven state-of-the-art algorithms on the DTLZ, MaF, and WFG test suites and four real-world MaOPs. The results of the experiments demonstrate that the suggested algorithm provides a novel and practical method for addressing MaOPs with irregular PFs. • A novel RL-RVEA addresses many-objective optimization with irregular pareto fronts. • A reinforcement learning-based adaptive reference vector to guide the direction of convergence. • A scalarization approach preserves the diversity of solutions for next generation. • The RL-RVEA outperforms seven advanced many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel surrogate-assisted multi-objective nutcracker optimization algorithm. This algorithm is built upon the recently proposed nutcracker optimization algorithm, drawing inspiration from the behaviours observed in Clark's nutcrackers. The algorithm is developed based on two distinct behaviours exhibited by these birds. To comprehensively evaluate the performance of the proposed algorithm, a dual-pronged approach is adopted. On the one hand, a set of artificial test problems is employed to scrutinize the algorithm's capabilities, while on the other hand, a set of real-world problems is considered to assess its practical efficacy. The results of the proposed algorithm are evaluated in comparison to existing baseline algorithms and state-of-the-art algorithms, using well-recognized performance metrics, both qualitatively and quantitatively. The obtained results provide convincing evidence of the performance of the proposed algorithm. [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]

Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: "Proceedings of the Congress" (33, 10.09%), "Applied Soft Computing" (19, 5.81%), "Expert Systems with Applications" (18, 5.51%), "Knowledge-Based Systems" (12, 3.67%), "Engineering Applications of Artificial Intelligence" (12, 3.67%), "Advances in Engineering Software" (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece. [Display omitted] • A detailed overview of optimization and its variants. • A literature review on state-of-the-art algorithms from 1965 to 2022. • Statistical analyzes and explanations in each year and during the 57 years. [ABSTRACT FROM AUTHOR]

Prediction-based dynamic multiobjective optimization evolutionary algorithms have become one of the mainstream methods for solving dynamic multiobjective optimization problems. However, the unknown nonlinear relationships in sequential environments bring great challenge to the construction of prediction model and the inevitable error between the historical obtained solutions and the real pareto-optimal solutions troubles the prediction accuracy. In this paper, a gradient boosting regression tree based dynamic multiobjective optimization evolutionary algorithm is proposed, called MOEA/D-XGB. The time series solutions formed by subspace decomposition are trained in the XGBoost-based predictor to produce a high-quality initial population when environmental change occurs. And a novel population promotion strategy based on generalized additive model is proposed to improve the quality of historical obtained solutions. The performance comparisons with five state-of-the-art algorithms have shown that MOEA/D-XGB achieves best performance in 59 out of 70 experiments on MIGD and 60 out of 70 experiments on MHV, respectively, which demonstrates that the proposed design is capable of significantly improving the performance of dynamic multiobjective optimization. [ABSTRACT FROM AUTHOR]

Reducing the number of lower-level function evaluations (LFEs) is crucial to designing a bilevel evolutionary algorithm (BLEA) for bilevel optimization. Direct neighbor solution transfer (DST) is regularly used in BLEA because it can improve the lower-level search efficiency and reduce the number of LFEs. But this method still faces the challenge of misleading searches to make the algorithm converge to the wrong optimum. This paper proposes a bilevel improved multi-operator differential evolution algorithm (BL-IMODE) for bilevel optimization, which uses the knowledge transfer and adaptive coordinate systems approach. Specifically, this paper proposes a lower-level search knowledge transfer (LLS-KT) and a lower-level (LL) optimal solution distribution information transfer (LLO-DIT) mechanism to implement knowledge transfer. In LLS-KT, transferring LL solutions to the LL optimizer is similar to DST, but the LLS-KT transfers multiple solutions and thus has less possibility of misleading searches than DST. Moreover, the LLO-DIT transfers the LL optimal solution distribution information to the LL optimizer for generating promising offspring solutions. Since this method does not directly transfer LL solutions, it avoids misleading searches. Additionally, the adaptive coordinate systems (ACS) approach is used in two-level optimizers to suit different fitness landscapes and further reduce the number of LFEs. In ACS, three differential evolution mutation operators are produced in two coordinate systems. An adaptive strategy is used to select the suitable differential evolution mutation operator to generate promising offspring solutions in the Eigen coordinate or original coordinate system. Experimental studies on three test suites benchmark problems for comparison with seven state-of-the-art algorithms are conducted. The results show BL-IMODE has a significant advantage over the comparative algorithms. Finally, BL-IMODE is applied to tackle the inverse optimal control problem. • We propose a bilevel improved multi-operator differential evolution algorithm. • We propose a knowledge transfer approach to avoid misleading search. • We propose an adaptive coordinate systems approach to improve search efficiency. • We outperform all existing approaches in most test instances. [ABSTRACT FROM AUTHOR]

Coal consumption and free calcium oxide (f-CaO) content are two important production indicators in the cement calcination process, and its collaborative optimization is of great significance to improve the production performance. However, due to the multiple dynamic interferences and multiple conflicts between coal consumption and f-CaO, product quality is unstable and coal consumption becomes high. To address the problems, this paper proposes a multi-objective collaborative optimization method for cement calcination process. This method takes the coal consumption and f-CaO content as the optimization objectives to establish a multi-objective optimization model. In addition, to solve the problem that static single-step optimization is difficult to track the continuous change of working conditions, we propose a time domain rolling multi-objective Jaya algorithm (TDRM-Jaya) to optimize the model. The algorithm, involving a population update strategy, a selection strategy and a time domain rolling strategy, realizes the dynamic rolling optimization for the cement calcination process. The test comparison with several common intelligent optimization algorithms demonstrates the advantages of using TDRM-Jaya. Besides, practical industrial data validates the effectiveness of the proposed optimization method. [Display omitted] • A multi-objective optimization model of cement calcination process is developed. • A TDRM-Jaya algorithm is proposed applied to solve the optimization model. • The multi-objective collaborative optimization using data-driven model is studied. • Computational results show that the expected optimization effect can be achieved. [ABSTRACT FROM AUTHOR]

The last few years have seen an increasing demand for high-capacity Internet services, and this need has intensified in the years 2020 and 2021. In 2020 and 2021, Internet usage grew by 50% in several European countries, mainly due to home office, video streaming, hybrid teaching, and others. High-capacity optical networks usually meet this growing demand for Internet services. Thus, investigations that can improve the quality of optical networks are highly relevant in the current context. One of the research problems in this area is related to the physical topology design (PTD) of optical networks, which is classified as NP-hard. Several studies on PTD consider the application of meta-heuristics that obtain suboptimal solutions in a time compatible with engineering applications. However, meta-heuristics and local search techniques have been combined in several other optimization problems, which is not typical for the PTD problem. This paper proposes a solution to the PTD problem that combines a known multipurpose optimization algorithm, the NSGA-III, with operators considering the domain-specific knowledge of the problem to provide superior-quality networks. According to our results, the new proposal presents quality up to 8% higher than previous proposals concerning the hypervolume metrics (HV), maintaining a similar computational cost. • Proposal of pivot rules for local search in many objective algorithms for PTD. • Provide a method for efficient exploration of neighborhood for network optimization. • Proposal of a new local search that considers both convergence and diversity. • Hybridization of NSGA-III with the proposed LS, providing superior quality solutions. [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

The high dimensionality of decision variables in large-scale multi-objective optimization problems poses significant challenges for evolutionary algorithms, which often struggle to achieve efficient search, are prone to premature convergence, and require a substantial amount of computing resources to converge to the Pareto Front. To address these challenges, a cooperative tri-population based evolutionary algorithm is proposed in this paper. Firstly, the population is partitioned into three subpopulations according to the Pareto dominant relation and crowdedness, including a subpopulation with well-distributed nondominated individuals, a subpopulation with crowded non-dominated individuals, and a subpopulation with dominated individuals. Three distinct reproduction operators are then applied to each subpopulation. The first subpopulation uses fully informed search-based reproduction to locate the true Pareto Front, while the second subpopulation adopts segment learning-based reproduction to preserve elite segments and promote exploitation. Finally, directional exploration-based reproduction is used for the third subpopulation to explore the search space and promote diversity. The proposed algorithm is capable of exploring and exploiting superior solutions through co-evolution among diverse subpopulations. Experiments are performed on 36 LSMOP benchmarks with up to 50,000 decision variables to validate the effectiveness of the proposed algorithm, which demonstrates superior performance compared to five state-of-the-art algorithms in handling large-scale multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Large-scale multi-objective optimization problems (LSMOPs) bring significant challenges due to their large number of decision variables. Most of the existing algorithms fail to obtain high-quality solutions for the LSMOPs. To remedy this issue, an algorithm named dual-stage large-scale multi-objective evolutionary algorithm with dynamic learning strategy (DLMOEA-DLS) is proposed in this paper. In the DLMOEA-DLS, the entire evolution process mainly includes two stages, and each stage plays a different role in the searching process. In the first stage, the decision variables are clustering into two categories to be optimized independently for the convergence of the population. In the second stage, a dynamic learning strategy is designed to generate new offspring, in which each solution learns from a leader with better fitness and coupled control parameter for each solution is adaptively updated by learning from the historical behaviors of the solution. Moreover, an environmental selection operator is adopted to reserve promising solutions for the next iteration. To verify the performance of the DLMOEA-DLS, five state-of-the-art algorithms are used for comparison on 36 LSMOP benchmark instances, 48 LMF benchmark instances, and 6 real-world TREE benchmark instances. The experimental results demonstrate the superiority of the DLMOEA-DLS over the five state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

A large number of metaheuristic algorithms have been proposed in the last three decades, but no metaheuristic algorithm is superior to the others for all the optimization problems. It is laborious to select an appropriate metaheuristic algorithm to solve a problem, especially for laymen. In this paper, a framework for co-evolutionary algorithm using Q-learning with the meme is proposed, which is called as QLMA. The solution generation method in the metaheuristic algorithm is named meme, which is also viewed as the action for an agent in Q-learning, and multiple memes form the action set. In the initial stage, the tent map and opposition based learning are employed to obtain the initial population. In updating stage, a new population is generated by an action that is chosen from the action set by the agent in Q-learning, then the disruption operation is applied, avoiding excessive aggregation of solutions around the current global optimal solution and improving the balance between exploration and exploitation. QLMA is compared to sixteen algorithms on 23 classical benchmark functions, CEC 2017, and CEC 2019 benchmark functions. The experimental results demonstrate that QLMA is superior to the peer algorithms and has a good balance between exploration and exploitation. [ABSTRACT FROM AUTHOR]

Recommendation system is widely used because of its personalized service. It helps users obtain satisfactory results due to ambiguous expression in search engines. However, with the increasing number of users and items, most of the existing recommendation algorithms have problems of cold start, sparse data and high complexity. To address the above issues, this paper applies the rating information and attribute information of users and items to better represent the preferences of users and the activity of items by enriching the available information. Furthermore, we propose a novel game-based evolutionary clustering method to divide interest communities for users, which not only reduces the complexity of recommendation, but also takes full account of users' preferences. In addition, the impact of delayed information transmission on experimental performance is considered in the game-based evolutionary clustering. Since the calculation of similarity plays a significant role in finding the nearest neighbors of target users in the community, we propose a new similarity measurement strategy based on user preferences. Finally, the effectiveness of the proposed algorithm is verified by ablation experiment and comparison experiments. The experimental results illustrate that our algorithm outperforms the existing excellent algorithms. • Rating and attribute information are fused to better represent the preferences of users. • Game-based evolutionary clustering is designed to divide users with similar hobbies. • Delayed information transmission is considered in the game-based clustering methods. • Similarity index based on user preferences is used in the collaborative filtering. [ABSTRACT FROM AUTHOR]

Metaheuristics, providing high level guidelines for heuristic optimisation, have successfully been applied to many complex problems over the past decades. However, their performances often vary depending on the choice of the initial settings for their parameters and operators along with the characteristics of the given problem instance handled. Hence, there is a growing interest into designing adaptive search methods that automate the selection of efficient operators and setting of their parameters during the search process. In this study, an adaptive binary parallel evolutionary algorithm, referred to as ABPEA, is introduced for solving the uncapacitated facility location problem which is proven to be an NP-hard optimisation problem. The approach uses a unary and two other binary operators. A reinforcement learning mechanism is used for assigning credits to operators considering their recent impact on generating improved solutions to the problem instance in hand. An operator is selected adaptively with a greedy policy for perturbing a solution. The performance of the proposed approach is evaluated on a set of well-known benchmark instances using ORLib and M*, and its scaling capacity by running it with different starting points on an increasing number of threads. Parameters are adjusted to derive the best configuration of three different rewarding schemes, which are instant, average and extreme. A performance comparison to the other state-of-the-art algorithms illustrates the superiority of ABPEA. Moreover, ABPEA provides up to a factor of 3.9 times acceleration when compared to the sequential algorithm based on a single-operator. • This paper proposes a multi-threaded adaptive evolutionary algorithm. • The proposed approach (ABPEA) is tested on the well-known binary-encoded problem UFLP. • An operator is selected adaptively with a greedy policy for perturbing a solution. • Three rewarding schemes, which are instant, average and extreme, are discussed. • Effect of varying the number of threads on the performance of ABPEA is investigated. [ABSTRACT FROM AUTHOR]

The Marine Predators Algorithm (MPA) is a novel hunting-based optimizer. The MPA's central concept is based on the well-known Lévy Flight (LF) and Brownian Motion (BM) strategies as well as a simple transition model between these two strategies. The canonical MPA proposes three static steps to tune the transition behavior between the LF and BM strategies. Although MPA provides exemplary performance in many test functions, the discrete transition between the two mentioned phases causes it to get stuck in local optima when faced with real-world optimization problems. In order to address this shortcoming, this paper proposes a soft dynamic transition between LF and BM to model this encounter naturally, considering the continuous nature of the transition between LF and BM in marine predators' real life. In order to evaluate the performance of the developed Dynamic Foraging Strategy MPA (DFSMPA), twenty-nine optimization test functions, thirty complex CEC-BC-2017 functions, ten benchmarks of CEC06-2019 test suit, and ten real applicable engineering problems, including power system design, synthesis and process design, industrial chemical producer, power-electronic design, mechanical design, and animal feed ratio, are employed. The DFSMPA is evaluated against four groups of standard optimization approaches, including (1) Arithmetic Optimization Algorithm (AOA), Slime Mould Algorithm (SMA), Equilibrium Optimizer (EO), Niching Chimp Optimization Algorithm (ChOA), Henry Gas Solubility Optimization (HGSO) as recent optimization algorithms, (2) Lévy Flight GWO (LGWO) and Evolutionary Algorithms with Adaptive Lévy Mutations (EALM) as the two best dynamic Lévy-based optimization algorithms, (3) SHADE, CMA-ES, and LSHADESPACMA as the three state-of-the-art optimization algorithms, and jDE100, DISHchain1e+12, CIPDE, and EBOwithCMAR as best performing algorithms in IEEE CEC06-2019 competition. Three non-parametric statistical tests, including the Wilcoxon rank-sum, Bonferroni–Dunn and Holm, and Friedman average rank tests, are utilized to perform a comprehensive assessment. The results show that the DFSMPA achieved the first rank among 46 out of 70 benchmark functions and engineering problems and exhibited similar results compared with SHADE and CMA-ES in other benchmarks. The statistical analysis demonstrated that DFSMPA is a significantly superior optimizer than the three first categories' benchmark algorithms, while its result is statistically similar to jDE100, DISHchain1e+12. [ABSTRACT FROM AUTHOR]

Max-cut is one of the most classic NP-hard combinatorial optimization problems. The symmetry nature of it leads to special difficulty in extracting meaningful configuration information for learning; none of the state-of-the-art algorithms has employed any learning operators. This paper proposes an original learning method for max-cut, namely post-flip edge-state learning (PF-ESL). Different from previous algorithms, PF-ESL regards edge-states (cut or not cut) rather than vertex-positions as the critical information of a configuration, and extracts their statistics over a population for learning. It is based on following observations. 1) Edges are the only factors considered by the objective function. 2) Edge-states keep invariant when rotating a local configuration to its symmetry position, but vertex-positions do not. These suggest that edge-states contain more meaningful information about a configuration than vertex-positions do. It is impossible to set the state of an edge without influencing some other edges' states due to their dependencies. Therefore, instead of setting edge-states directly, PF-ESL samples the flips on vertices. Flips on vertices are sampled according to their capacities in increasing the similarity on edge-states between the given solution and a population. PF-ESL is employed in an EDA (Estimation of Distribution Algorithm) perturbation operator and a path-relinking operator. Experimental results show that our algorithm is competitive, and show that edge-state learning is value-added for both the two operators. The main contributions of this paper are as follows. Firstly, previous state-of-the-art evolutionary algorithms for max-cut focus on vertex positions in their evolutionary operation, this paper proposes a new and more reasonable perspective suggesting that edge-states are the critical information of divided graphs rather than vertex positions, and introduces a novel method to measure and utilize their similarities based on it. Such a perspective is fundamental to learning based algorithms design for max-cut and other graph partitioning problems, and can shed lights on future researches. Furthermore, since max-cut is one of the most classic and fundamental NP hard problems, many real-world problems involve dividing graph data into different parts to optimize certain functions, this new perspective may inspire related or similar problems. Secondly, besides the original edge-states based perspective, and the post-flip edge-states learning (PFESL) operator based on it, our memetic algorithm also incorporates a novel evolutionary framework which alternates between EDA based Iterated Tabu search (ITS) and path relinking based genetic algorithm. Finally, the proposed algorithm provides competitive results on two mostly used benchmark sets and improves the best-known results of 6 most challenging instances. • An original edge-state perspective for design learning operators for max-cut. • A novel learning operator to learn edge states. • A new evolutionary framework for max-cut. • It provides competitive results on two mostly used benchmark sets. [ABSTRACT FROM AUTHOR]

A Meta-evolutionary Selection of Constituents in Ensemble DE (MeSCEDE) framework is proposed in this paper to automate the design of high-level multi-population ensemble Differential Evolution (DE) algorithms. The automated design of high-level multi-population ensemble DE algorithms involves both automated selection of constituent DE algorithms for the ensemble and automated configuration of ensemble related parameters. Grammatical Evolution has been used as the meta-evolutionary algorithm in MeSCEDE to search the space of design choices so as to evolve effective ensemble design(s) for given problem(s). The simulation experiments carried out in this paper involve applying MeSCEDE to evolve ensemble DE designs for solving 30-dimensional CEC'17 benchmark functions. The effectiveness of the evolved designs are validated on 30 and 50-dimensional CEC'14 functions as well as on 22 real-world problem instances from CEC'11 benchmark suite. The MeSCEDE evolved designs have exhibited a competitive performance against state-of-the-art ensemble DE algorithms. In addition, the potential of MeSCEDE has been demonstrated against irace , a state-of-the-art algorithm configurator. All simulation experiments reiterate the potential of MeSCEDE towards evolving effective and robust ensemble DE designs. • Automated design of ensemble differential evolution (DE) algorithm is presented. • The proposed approach is Meta-evolutionary Selection of Constituents in Ensemble DE. • The approach uses grammatical evolution as the meta evolutionary algorithm. • The approach is demonstrated on CEC'17, CEC'14 and CEC'11 benchmark suites. • Relieves the practitioners from the difficult task of algorithm configuration. [ABSTRACT FROM AUTHOR]

Wind resource is one of the most promising renewable energy, which has become a suitable replacement for fossil fuels. Optimizing the transferring wind energy from a wind turbine is essential to obtain the maximum power output as other variables are uncontrollable. This paper presents four different optimization algorithms, namely ant lion optimization (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and crow search optimization (CSO), considering a hybrid decision-making model to compare the performances of wind energy optimization. In the first phase, the evolutionary algorithms are defined based on several factors to meet the need for wind energy based on volumetric and time reliability, reversibility, and vulnerability as well as evaluate optimized energy to the subscriber from the Gansu region. In the second phase, the ordinal priority approach (OPA) is coupled with VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank the evolutionary algorithms. Then, the results are compared with the absolute optimal response based on the nonlinear programming method obtained from GAMS software. The results demonstrate that an ALO outperforms other algorithms. The average accuracy of ALO is 92%. CSO is the least accurate with 55% of the absolute optimal response. ALO is found to be faster, more efficient, and achieved economy and reliability as compared to other optimization algorithms for solving the problem under consideration. It is shown that the applied models are robust, effective, and able to save costs. [ABSTRACT FROM AUTHOR]

For computation offloading problem (COP) in mobile edge computing (MEC), the energy consumption of terminal equipments(TEs) and the delay of mobile equipment applications are two optimization goals. In real life, terminal equipment is dynamic, and their number, mobility, and continuous changes in wireless channels will affect the balance between the mentioned energy consumption and delay. Different from available works, we model the COP in MEC as a dynamic multi-objective problem (DMOP) in this paper, and propose an improved dynamic multi-objective evolutionary optimization based on decomposition (DMOEA/D) to solve it, namely DMOEA/D-COPMEC. In the proposed algorithm, the environmental change is detected by a fixed detector, and whether the current change is similar to the historical change is determined. If so, the difference prediction is used to re-locate the population individual in the new MEC environment, otherwise, the memory-based strategy is used to response environmental change. In MOEA/D, an adaptive weight adjustment strategy based on chain segmentation (CS) is adopted to generate a set of uniformly distributed weight vectors. The simulation results show that the proposed algorithm can better balance the application delay and the terminal energy consumption if there is environment change. The solution set is closer to reality and better than the related algorithms. [ABSTRACT FROM AUTHOR]

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

The balance between multiple objectives and various constraints is the key to solving constrained multi-objective optimization problems (CMOPs). When dealing with CMOPs with complex feasible regions, some evolutionary algorithms suffer from great challenges in converging to the constrained Pareto front (CPF) with well-distributed feasible solutions. To address this issue, this paper proposes a dual-population based bidirectional coevolution algorithm, called DBC-CMOEA, which aims to converge to the CPF using promising solutions explored from both feasible and infeasible regions. To do so, DBC-CMOEA maintains two populations and an archive, where the dual-population is complementary in the search process and the archive is used to retain promising feasible and infeasible solutions, thus facilitating information exchange between these two populations. For updating the archive, a nondominated sorting procedure and an angle-based selected scheme are conducted to store infeasible and feasible solutions, as they can help to maintain the diversity of the search and find more feasible regions. To evolve the CPF from the bidirectional side of the feasible region, a novel mating selection strategy is used to choose appropriate mating parents. In comparison with some related constraint multi-objective optimization algorithms on a number of benchmark problems, experimental results show that the proposed algorithm performs better than the state-of-the-art constrained multi-objective evolutionary optimizers. • A cooperative dual-population algorithm is proposed for solving CMOPs. • Constraints are handled in three different ways to explore the search space. • A brand-new selection strategy is applied to select the mating parents. • A novel archive update strategy is designed to retain the promising information. [ABSTRACT FROM AUTHOR]

Solving multi-objective optimization problems plays an important role in several applications. Recently, the Marine Predator Algorithm (MPA) was introduced for solving single objective optimization problems inspired by the behaviour of marine predator in their search for a prey. This paper proposes a modified MPA based framework called Guided Multi-objective Marine Predator Algorithm (GMOMPA) to solve multi-objective optimization problems. The proposed GMOMPA incorporates an external archive to help store the optimal Pareto set solution and guide the particles during the exploitation of the search space. For obtaining non-dominated solutions, the Pareto dominance concept is utilized while the epsilon dominance is considered to update the archive's sorted solutions. In this context, the epsilon dominance concept extends the diversity and exploration of the solutions. Further, a fast non-dominated solution and crowding distance are introduced to update the particle's position, while maintaining the diversity and ensuring a fast convergence towards the Pareto optimal. The proposed GMOMPA is evaluated on several different benchmark test functions including multi-objective ZDT, DTLZ, UF, and WFG test functions as well as the recent multi-objective multimodal CEC 2020 test functions. Moreover, the performance of the proposed GMOMPA is compared with well-known multi-objective optimization algorithms. The obtained results show that the proposed GMOMPA is a good tool for multi-objective optimization and has significant advantages over several state-of-the-art algorithms in almost all of the test functions. [ABSTRACT FROM AUTHOR]

The imitating behaviour of the slime mould algorithm (SMA) supports the collective efficiency, whereas the evolutionary algorithm (EA) smoothens the global effectiveness through a crossover. This observation leads to the integration of the EA with SMA. Thus, in this paper, an attempt has been made to hybridize the said algorithms in a parallel and series manner. In parallel structure, EA and SMA are executed parallelly and solutions obtained from both are combined to obtain the best solution which is further saved and updated as to a global best solution. Whereas, in a series structure, the population is fed to the EA initially to obtain the best solution which is again fed to SMA to obtain the global best solution. Under such guidance, the global search ability and search efficiency of EA and SMA are enhanced. The proposed PSEASMA and SSEASMA are tested on 23 classical benchmark functions and ten CEC2019 functions. The Wilcoxon rank-sum test is also carried out to validate the efficacy of the proposed work. The proposed work has been compared with other renowned metaheuristic algorithms in terms of average and standard deviation. Based on average and standard deviation, the ranks of each algorithm have been assigned through the Friedman test. The results suggested that the proposed method outperformed the other state of arts. The PSEASMA and SSEASMA have also been applied to classical engineering design problems. The performance of the proposed work is significantly superior compared to basic SMA, EA and other meta-heuristic algorithms. • A hybrid algorithm has been proposed by combining evolutionary algorithm with slime mould algorithm. • The hybridization is done through series and parallel construction. • The proposed PSEASMA and SSEASMA optimizes the conventional design problems in terms of optimum weight and optimum cost. • The Friedman and Wilcoxon test is carried out to validate the superiority of the proposed work. [ABSTRACT FROM AUTHOR]

• A comprehensive literature review of meta-heuristic optimization algorithms for wind power forecasting is conducted. • A well-designed taxonomy of meta-heuristic algorithms for optimizing wind power forecasting model parameters is developed. • A deeply comprehensive and scientific multiple error evaluation metric is proposed for wind power error analysis. • Further research topics for future works in wind power forecasting are presented. The integration of large-scale wind power introduces issues in modern power systems operations due to its strong randomness and volatility. These issues can be resolved via wind power forecasting that can provide comprehensive future information about wind power uncertainties. This paper presents a timely and comprehensive review of meta-heuristic algorithms in the framework of wind power forecasting. The framework is based on the auxiliary layer, forecasting base layer, and core layer. The auxiliary layer, such as the data-decomposition layer, decomposes the wind power time series into many relatively stationary subseries, and uses prediction models, including artificial neural networks (ANNs) and machine learning (ML). The core layer is based on meta-heuristic algorithms, which include evolutionary-based algorithms, physics-based algorithms, human-based algorithms, swarm-based algorithms, hybrid algorithms, and multi-objective optimization algorithms. These algorithms aim to search for the optimal solutions under constraints, which is highly significant for optimizing the key parameters of the prediction models. Besides, multiple error evaluation metrics, e.g., deterministic, uncertainty, and testing methods used in the field of wind power prediction are described. A quantitative analysis focusing on their advantages, disadvantages, forecasting accuracy, and computational costs are also provided. Finally, a few open research issues and trends related to the topic are discussed, which can contribute to improving the understanding of each wind power forecasting method. In general, this review paper provides valuable insights to wind power engineers. [ABSTRACT FROM AUTHOR]

• A generalized decoupling metaheuristic RBDO framework is proposed. • A particle's memory saving strategy is developed to provide effective guidance. • A new adaptive fractional-order equilibrium optimizer algorithm is developed. • Numerical and engineering examples illustrate the superiority of the proposed algorithm. Reliability-based design optimization (RBDO) algorithm is to minimize the objective under the probabilistic factors. While gradient-based and classical evolutionary RBDO algorithms provide promising performance on simple optimization problems, they are likely to perform poorly on challenging problems, including the multimodal functions, discrete design spaces, non-differential problems, etc. This paper proposes a unified framework to improve the performance of existing RBDO algorithms for complex RBDO problems. Our framework is based on three new strategies: generalized decoupling evolutionary and metaheuristic RBDO framework, particle's memory saving strategy, and adaptive fractional-order equilibrium optimizer algorithm. The proposed algorithm is characterized by a decoupling strategy to enable the parallel operation of the inner reliability computation and outer deterministic optimization, a particle's memory saving strategy to provide effective guidance from the previous iteration, and the adaptive fractional-order equilibrium optimizer algorithm to enhance the search efficiency and global convergence capacity. To evaluate the performance of the proposed algorithm, a wide range of experiments are conducted on different types of use cases. The experimental results demonstrate that our algorithm provides superior performance over other comparative algorithms. [ABSTRACT FROM AUTHOR]

One of the popular metaheuristic search algorithms is Harmony Search (HS). It has been verified that HS can find solutions to optimization problems due to its balanced exploratory and convergence behavior and its simple and flexible structure. This capability makes the algorithm preferable to be applied in several real-world applications in various fields, including healthcare systems, different engineering fields, and computer science. The popularity of HS urges us to provide a comprehensive survey of the literature on HS and its variants on health systems, analyze its strengths and weaknesses, and suggest future research directions. In this review paper, the current studies and uses of harmony search are studied in four main domains. (i) The variants of HS, including its modifications and hybridization. (ii) Summary of the previous review works. (iii) Applications of HS in healthcare systems. (iv) And finally, an operational framework is proposed for the applications of HS in healthcare systems. The main contribution of this review is intended to provide a thorough examination of HS in healthcare systems while also serving as a valuable resource for prospective scholars who want to investigate or implement this method. [ABSTRACT FROM AUTHOR]

Automatic text summarization is a topic of great interest in many fields of knowledge. Particularly, query-oriented extractive multi-document text summarization methods have increased their importance recently, since they can automatically generate a summary according to a query given by the user. One way to address this problem is by multi-objective optimization approaches. In this paper, a memetic algorithm, specifically a Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) has been developed, implemented, and applied to solve the query-oriented extractive multi-document text summarization problem. Experiments have been conducted with datasets from Text Analysis Conference (TAC), and the obtained results have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results have shown that the proposed approach has achieved important improvements with respect to the works of scientific literature. Specifically, 25.41%, 7.13%, and 30.22% of percentage improvements in ROUGE-1, ROUGE-2, and ROUGE-SU4 scores have been respectively reached. In addition, MOSFLA has been applied to medicine texts from the Topically Diverse Query Focus Summarization (TD-QFS) dataset as a case study. • The query-oriented text summarization problem is addressed. • The criteria of query relevance and redundancy reduction are optimized simultaneously. • A multi-objective memetic algorithm (MOSFLA) is designed and developed. • The experiments use the Text Analysis Conference datasets and the ROUGE metrics. • MOSFLA outperforms the results of other 12 approaches from the scientific literature. [ABSTRACT FROM AUTHOR]

• An Enhanced Binary Bat Algorithm (EBBA) for classification of fetal state has been conferred. • Proposed EBBA selects the most discriminative features. • The recommended algorithm has been employed on the cardiotocography dataset with the average accuracy of 96.21%. • Three different machine learning classifiers: (i) DecisionTree (ii) K-Nearest Neighbor (k-NN) (iii) Random Forest has been used to assess the worthiness of optimal features selected by EBBA • Comparison of various evolutionary algorithms like EBBA, qGWO, Genetic in terms of features selected and accuracies obtained. • The proposed EBBA is presented in as simple, forthright and coherent manner as conceivable. The timely assessment of hypoxic fetuses by cardiotocography is of utmost significance as deficiency of oxygen in fetuses' leads to fetal distress which can further prove to be fatal or cause neurological diseases. This paper puts forward an Enhanced Binary Bat algorithm, the altered form of Binary Bat Algorithm for the multi-classification problem of Cardiotocography. Subset of optimal and relevant features is selected using the optimized and Enhanced BBA algorithm from cardiotocography dataset. Features selected by various evolutionary algorithms EBBA (Enhanced Binary Bat algorithm), qGWO (quantum Grey Wolf Optimization), Genetic algorithm are 11, 15, 12 simultaneously. EBBA efficiently selects most reduced set of features. The proposed EBBA can be used in feature selection and classification of cardiotocography dataset under different fetus state i.e. normal, suspect and pathologic with an accuracy of 96.21% using machine learning classifier i.e. random forest. [ABSTRACT FROM AUTHOR]

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

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

• An improved muti-step initialization mechanism is presented. • Different decision variables adopted different operators to optimize particles. • A competition based learning strategy for updating swarm particles is suggested. • Experimental results show it achieved balance between convergence and diversity. Multi-objective particle swarm optimization (MOPSO) has been widely applied to solve multi-objective optimization problems (MOPs), due to its efficient implementation and fast convergence. However, most MOPSOs are ineffective in achieving the balance between convergence and diversity in the high-dimensional objective space. In this paper, an improved competitive particle swarm optimization is proposed for solving many-objective optimization problems. To improve the quality of the first generation population, a decision variable dividing-based multi-step initialization mechanism is presented, decision variables are divided into two groups and optimized individually. Moreover, an improved competitive learning strategy is suggested as the main part to further optimization, where particles are updated via leader information from winner particles with well convergence and diversity. The performance of the proposed algorithm is verified by benchmark comparisons with several state-of-the-art evolutionary algorithms. Experimental results demonstrate the promising performance of the algorithm in terms of balance convergence and diversity. [ABSTRACT FROM AUTHOR]

Students generally attempt to improve themselves with different manners in their spare time. Inspired by this fact, this paper presents a multi-objective elitist feedback teaching–learning-based optimization (MEFTO) algorithm for multi-objective optimization problems. To promote the exploration capacity and convergence of the basic teaching–learning-based optimization (TLBO), a feedback phase that simulates the spare-time learning phenomenon is introduced at the end of the learner phase. Students should compare their score with the average class score to select an appropriate means for further improvement. Poorly performing students can learn from the teacher directly for rapid improvement, whereas high-performing students prefer to motivate themselves for reinforcement learning. Non-dominated sorting is incorporated to permit this heuristic to solve problems with several objectives. Crowding distance calculation is adopted to maintain the diversity of the obtained solution set in a single run. Elitism strategy is also employed to provide a great improvement for the algorithm. The performance of the MEFTO algorithm is compared with seven other well-known algorithms by using a set of unconstrained benchmark test problems and three constrained engineering optimization problems. The qualitative and quantitative results indicate that the proposed algorithm can provide considerably competitive results and outperforms the other algorithms. • A novel multi-objective evolutionary algorithm (MEFTO) is developed. • The developed algorithm is evaluated on a series of benchmark functions. • The performance of the MEFTO is demonstrated using various metrics. • The developed algorithm is applied to solve three constrained engineering problem. [ABSTRACT FROM AUTHOR]

• A novel hybrid multi-objective optimization algorithm is proposed. • The computational efficiency of the optimization algorithm is improved. • A novel multi-constraint optimization method of the free-form grid structures is proposed. • An integrated multi-attribute decision-making method was used in the multi-objective optimization. • The mechanical, economic, and geometric property of the structure are improved. This paper proposes a novel multi-constraint and multi-objective optimization method to improve the integrated performance of a free-form surface reticulated shell. A geometric model was established using a non-uniform rational basis spline (NURBS) technology and bidirectional parameter line bisecting method. The height of the control points and section size of the rods were taken as optimization variables. The strain energy, economic index, and geometric integrated index were taken as optimization objectives. The sensitivity-NSGA-III hybrid multi-objective optimization algorithm (SH-NSGA-III) is developed. The SH-NSGA-III algorithm has significantly better efficiency than the NSGA-III, MOEA/D, and SPEA2 algorithms. The concept of constrained non-dominant sorting was introduced into the hybrid algorithm to process the constraints in a multi-constraint and multi-objective optimization problem. Meanwhile, an integrated multi-attribute decision-making method was used to select the optimal solution based on the Pareto optimal solution set. The multi-constraint and multi-objective optimization of a free-form surface reticulated shell was performed using the proposed method. The results demonstrated that the Pareto optimal solution set could effectively satisfy the constraints. The strain energy, economic index, and geometric integrated index were reduced by 61.4%, 36.8%, and 19.5%, respectively, and the geometric indexes were reduced by 22%, 11.5%, 20.8%, and 18.4%. [ABSTRACT FROM AUTHOR]

Recently, the Slime mould algorithm (SMA) was proposed to solve the single-objective optimization problems. It is considered as a strong algorithm for its efficient global search capability. This paper presents a multi-objective optimization algorithm based on the SMA called multi-objective SMA (MOSMA). An external archive is utilized with the SMA to store the Pareto optimal solutions obtained. The archive applied to emulate the social behaviour of the slime mould in the multi-objective search space. The performance of the MOSMA is validated on the CEC'20 multi-objective benchmark test functions. Furthermore eight well-known of constrained and unconstrained test cases, four constrained engineering design problems are tested to demonstrate the MOSMA superiority. Moreover, the real-world multi-objective optimization of helical coil spring for automotive application to depict the reliability of the presented MOSMA to solve real-world problems. Over the statistical side, the Wilcoxon test and performance indicators are used to assess the effectiveness of MOSMA against six well-known and robust optimization algorithms: multi-objective grey wolf optimizer (MOGWO), multi-objective particle swarm optimization (MOPSO), multi-objective salp swarm algorithm (MSSA), Non-dominated sorting genetic algorithm version 2 (NSGA-II), multi-objective whale optimization algorithm (MOWOA) and strength Pareto evolutionary algorithm 2 (SPEA2). The overall simulation results reveal that the proposed MOSMA has the ability to provide better solutions as compared to the other algorithms in terms of Pareto sets proximity (PSP) and inverted generational distance in decision space (IGDX) indicators. • MOSMA is proposed for solving multi-objective optimization problems. • CEC'20 test suite and 4 engineering problems are utilized for verification. • The helical coil spring for automotive application is solved. • MOSMA method is analysed using various analysis metrics such as PS and PF. • Experimental results confirm the effectiveness of the MOSMA in practice. [ABSTRACT FROM AUTHOR]

In real-world applications, there are many multimodal multi-objective optimization problems, which have multiple equivalent global Pareto-optimal solutions or with at least one local Pareto-optimal solution in the decision space. While some evolutionary algorithms have been proposed to find the global solutions recently, they are difficult to handle multimodal multi-objective optimization problems with local solutions. Meanwhile, there have been few studies on searching for local Pareto solutions. However, local solutions are additional alternatives for the decision makers if global solutions are impracticable. This paper proposes a particle swarm optimizer based on reference point, termed RPPSO, which combines a reference point mechanism and a local solution preserving technique. The reference point strategy is utilized to establish multiple evenly distributed neighborhoods and guide particles to evolve independently in their respective neighborhoods, so as to detect more Pareto solutions in the decision space. The local solution preserving technique is employed to estimate the dominant radius of each front, and with this radius to classify the individuals as either non-local or local solutions with the aim of retaining the local solutions. In addition, a set of benchmark test functions with local Pareto solutions are designed. The proposed algorithm is comprehensively evaluated on forty-four benchmark functions and is compared with fourteen state-of-the-art algorithms. The experimental results show that the proposed RPPSO achieves competitive performance than its competitors in terms of the reciprocal of Pareto sets proximity (rPSP). The RPPSO is also applied to solve on one real-world problem (i.e., map-based problem) to further verify the effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

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

• A two-stage evolution strategy is proposed for solving multi-objective problem. • The convergence and diversity should be balanced in multi-objective optimization. • A local searching method can improve the diversity of solutions in the space. • Experimental results have been presented by using statistical method. The balance of convergence and diversity plays a significant role to the performance of multi-objective evolutionary algorithms (MOEAs). The MOEA/D is a very popular multi-objective optimization algorithm and has been used to solve various real world problems. Like many other algorithms, the MOEA/D also has insufficient ability of convergence and diversity when tackling certain complex multi-objective optimization problems (MOPs). In this paper, a novel algorithm named MOEA/D-TS is proposed for effectively solving MOPs. The new algorithm adopts two stages evolution strategies, the first stage is focused on pushing the solutions into the area of the Pareto front and speeding up its convergence ability, after that, the second stage conducts in the operating solution's diversity and makes the solutions distributed uniformly. The performance of MOEA/D-TS is validated in the ZDT, DTLZ and IMOP problems. Compared with others popular and variants algorithms, the experimental results demonstrate that the proposed algorithm has advantage over other algorithms with regard to the convergence and diversity in most of the tested problems. [ABSTRACT FROM AUTHOR]

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. • An efficient Multi-objective Evolutionary Swarm Hybrid algorithm is proposed. • Development of a nonlinear model to operational control of Hydro-power plants. • Hydro-power plant data regression obtains the maximum efficiency of the power units. • Efficiency energy goals achieved an increasing the profit in the energy production. [ABSTRACT FROM AUTHOR]

• The proposed work efficiently classifies the breast tissues into normal & abnormal. • Nipple & mid-sternum point selection makes the breast MR image rotation invariant. • Primary location guidelines for axial plane determine the exact tumor location. • This work can be used as initial screening or second opinion for the radiologists. The basic objective of this paper is to develop a single structured algorithm to classify the breast tissues into normal or abnormal. For this study, the breast MR images dataset of 448 images collected from Healthmap diagnostics centre, PGIMS, Rohtak, India. The proposed algorithm consists of several steps i.e. an integrated (Median Wiener & Median) filtering technique is used for de-noising; breast boundary region extraction via selection of nipple and mid- sternum points to make the image rotation invariant; determined the tumor region intensity by using morphological operations & hole filling; classify the normal and abnormal breast tissues by SVM using 14 texture features extracted through GLCM & 13 morphological or kinetic features; evaluated the exact location as well as area of abnormal tissues. The proposed algorithm has been evaluated statistically as well as visually. The quality parameters achieved are accuracy, sensitivity and specificity with values 0.937, 0.956 and 0.872 respectively. The Jaccard Index coefficient achieved is 0.921, which indicates promising overlap between the predicted tumor and the manually done image by the radiologist so called ground truth image. This work may be taken as a second opinion by the radiologists. The evaluated results may give a basic foundation for optimization by selecting the features more precisely and also different evolutionary algorithms using multi-classifiers can be designed in future. [ABSTRACT FROM AUTHOR]

• A cellular-based evolutionary fuzzy system for the extraction of emerging patterns in high-speed, massive data streams is proposed. • Smart triggering of the learning method which updates the model only when required. • A reinitialisation and filtering strategy for reducing the extraction of redundant patterns is also defined. • The quality of knowledge extracted outperforms state-of-the-art methods. • The proposed method is able to process batches of data up to 750,000 instances. Today, the number of existing devices generates immense amounts of data on a continuous basis that must be processed by new distributed data stream mining approaches. In this paper we present a new approach for extracting descriptive emerging patterns in massive data streams from different sources through Apache Kafka and Apache Spark Streaming whose objective is to monitor the state of the system with respect to a variable of interest. For this purpose, the proposed algorithm is a cellular-based multi-objective evolutionary fuzzy system that uses an informed strategy for efficient data processing and a re-initialisation and filtering mechanism to eliminate redundant and low-reliable patterns. The experimental study carried out demonstrates an interpretability improvement of 25% in the extraction of high-interest knowledge by the proposed algorithm, which would make it easier for experts to analyse the problem. Finally, the proposed algorithm is up to five times faster than another proposal on the processing of the same amount of data. In this experimental study, up to 750,000 instances have been processed in approximately four seconds. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-level multi-energy management framework for the coordinated and interactive operation of heterogeneous multi-microgrids (MMGs) based on many-criteria optimality. With the proposed framework, the highly nonlinear and complex MMG multi-energy management (MMGMEM) problem is formulated into tri-level scheduling subproblems with multi-energy couplings and multi-level interactions, in which the multi-energy trading with energy networks and multi-energy couplings within MGs are optimized in the upper and middle level, and a middle level is added to correct scheduling decisions of the upper level for coordinating the MMG multi-energy sharing. Then, a multi-step matrix decomposition technique is developed to decompose the high dimensional multi-energy coupling matrix of MMGs into the sum of three linear and sparse submatrices for improving the computation efficiency and scalability. Furthermore, a many-criteria decision making (MCDM) model is proposed for the multi-energy sharing problem to achieve an optimum tradeoff in which all microgrids (MGs) can benefit from electricity-gas exchanges, and an evolutionary many-objective optimization based on hyperplane transformation algorithm is used to solve the MCDM problem. Simulation results verify that the proposed framework can achieve a cost saving for each MG (over 19%), and validate its scalability in solving large-scale MMGMEM problems. • A tri-level interactive framework is proposed for the coordinated operation of MMGs. • An extended multi-energy coupling matrix with MMG interactions is formulated. • A multi-step matrix decomposition technique is used to reduce the problem complexity. • A many-criteria optimality model is proposed for the tradeoff of all MGs' benefits. • The utilization of renewables can be enhanced with the minimal total operation cost. [ABSTRACT FROM AUTHOR]

• A general optimization algorithm for a cooperative hyper-heuristics is described. • Simulated Annealing, Genetic Algorithm and Particle Swarm Optimization are included • A Master processor communicates with all agents and ranks solution candidates • Both on-line parameter retuning and parallel processing speed up the search. • It was effective to optimise PSE models within acceptable computational times. This paper presents the general framework of a parallel cooperative hyper-heuristic optimizer (PCHO) to solve systems of nonlinear algebraic equations with equality and inequality constraints. The algorithm comprises the classical metaheuristics called Genetic Algorithms, Simulated Annealing and Particle Swarm Optimization, whose parameters are adaptively chosen during the executions. A Master-Worker architecture was designed and implemented, where the Master processor ranks the solution candidates informed by the metaheuristics and immediately communicates the most promising candidate to update all Workers. Algorithmic performance was tested with general models, most of them corresponding to PSE process systems. The results confirmed the efficiency of the proposed approach since both online parameter retuning and parallel processing sped up the search. [Display omitted] [ABSTRACT FROM AUTHOR]

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

• Chameleon Swarm Algorithm (CSA) is benchmarked on 67 benchmark functions. • The exploitation ability of CSA is affirmed by the results on unimodal functions. • The results of CSA on multimodal functions show the exploration ability of CSA. • The results of CSA on composite functions prove the reliability level of CSA. • The results on five engineering problems affirm the accuracy of CSA in practice. This paper presents a novel meta-heuristic algorithm named Chameleon Swarm Algorithm (CSA) for solving global numerical optimization problems. The base inspiration for CSA is the dynamic behavior of chameleons when navigating and hunting for food sources on trees, deserts and near swamps. This algorithm mathematically models and implements the behavioral steps of chameleons in their search for food, including their behavior in rotating their eyes to a nearly 360°scope of vision to locate prey and grab prey using their sticky tongues that launch at high speed. These foraging mechanisms practiced by chameleons eventually lead to feasible solutions when applied to address optimization problems. The stability of the proposed algorithm was assessed on sixty-seven benchmark test functions and the performance was examined using several evaluation measures. These test functions involve unimodal, multimodal, hybrid and composition functions with different levels of complexity. An extensive comparative study was conducted to demonstrate the efficacy of CSA over other meta-heuristic algorithms in terms of optimization accuracy. The applicability of the proposed algorithm in reliably addressing real-world problems was demonstrated in solving five constrained and computationally expensive engineering design problems. The overall results of CSA show that it offered a favorable global or near global solution and better performance compared to other meta-heuristics. [ABSTRACT FROM AUTHOR]

Urban rail transit is an efficient public transport, and reducing their energy consumption is beneficial to climate change and sustainable development. Current researches in metro train trajectory optimization are mainly based on the single-particle train operation model, and the selection of energy-saving operation strategy under time-varying passenger flow is not taken into consideration. Therefore, considering the practical line environment and uncertain trainload simultaneously, this paper establishes a multi-particle operation model and develops a new optimization method based on mutated dichotomy and differential evolutionary algorithm to solve the model. Then, the influence of various passenger flow and line information on the train trajectory optimization is analyzed from the perspective of trainload capacity based on the proposed method, and the selection basis of the optimal energy-saving train operation strategy was determined. Finally, a case study was conducted in the Nanning Rail Transit Line 1 and Line 5, respectively. The results show that the proposed method has strong efficiency for energy conservation and better optimization performance than the conventional differential evolution algorithm in solving train trajectory optimization problem. And the simulation results of the Line 5, which is a fully automatic operation line, also verified the reliability of the selection basis. • Passenger flow was considered in a multi-particle metro energy conservation model. • An improved evolution algorithm was proposed to optimize train trajectory. • The optimal operation strategy selection basis was determined for the metro line. [ABSTRACT FROM AUTHOR]

• A target vector replacement strategy (TVRS) is proposed. • Various differential evolution algorithms can be improved by TVRS. • The TVRS can be easily applied to the existing differential evolution algorithms. Individuals find it difficult to avoid stagnation in the iterative process of the differential evolution (DE) algorithm, and the stagnant individuals have restricted improvement in the population, which will have a negative effect on its performance. This research put forward a target vector replacement strategy (TVRS) for reducing the impact of stagnant individuals on the DE algorithm's performance. For stagnant target vectors, while executing mutation operation and crossover operation, TVRS selects non-stagnant individuals in the population to replace the stagnant target vectors with a specific probability. Because improving stagnant individuals are difficult, TVRS provides more opportunities for improvement to those who are not stagnant, while the opportunities for stagnant individuals to be improved are decreased. For assessing the efficiency of TVRS, TVRS was implemented to six DE algorithms and compared to their original algorithms. Judging from the performance outcomes of TVRS in the CEC 2014 benchmark test set, TVRS can greatly increase the DE algorithm's performance. This study will show the evolutionary opportunity that is obtained by an individual should be related to the individual state. Adjusting the individual evolutionary opportunity based on the individual state is helpful for improving the stagnation problem of the differential evolution algorithm. This study will provide a new way for improving the stagnation problem of the differential evolution algorithm and other evolutionary algorithms. [ABSTRACT FROM AUTHOR]