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An effective multi-stage evolutionary algorithm for distributed scheduling with splitting jobs in heterogeneous factories

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ENGINEERING OPTIMIZATION 2025年第3期57卷 688-716页

作者： Guo, Xin Deng, Qianwang Luo, Qiang Xie, Guanhua Hunan Univ Sch Mech & Vehicle Engn Changsha Hunan Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha Hunan Peoples R China

This article examines an alliance of heterogeneous factories operating as a production network, in which jobs can be divided into several sub-jobs and independently processed in distributed factories. This problem is considered as a distributed unrelated parallel machine scheduling with splitting jobs (DUPMSP/S). A mathematical model and an effective multi-stage evolutionary algorithm (EMSEA) are proposed, aiming to minimize the total tardiness and total cost of production and transportation. In the EMSEA, the optimization process is divided into three stages according to the population in each generation, and four problem-based initial methods and three knowledge-based local exploitation strategies are embedded to improve its performance. Extensive experiments are conducted to compare the EMSEA with four other algorithms and to compare the scheduling model with no splitting jobs. The results demonstrate that EMSEA is the most promising method in solving the DUPMSP/S, and the job splitting mode is effective.

关键词： Scheduling distributed parallel machine heterogeneous splitting jobs evolutionary algorithm

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Improved Non-Player Character (NPC) behavior using evolutionary algorithm-A systematic review

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Armanto, Hendrawan Rosyid, Harits Ar Muladi Gunawan Univ Negeri Malang Dept Elect Engn & Informat Jl Semarang 5 Malang 65145 East Java Indonesia Inst Sains & Teknol Terpadu Surabaya Dept Informat Jl Ngagel Jaya Tengah 73-77 Surabaya 60284 East Java Indonesia

Games, once solely intended for entertainment, have emerged as a significant research focus in recent years, with the primary goal of enhancing the gaming experience. Research in the gaming domain has expanded to encompass a wide range of topics, spanning from game theory to artificial intelligence. Within the realm of artificial intelligence itself, Non-Player Characters (NPCs) play a crucial role in shaping the overall gaming experience. The quality of NPC behavior directly influences player satisfaction. evolutionary algorithms stand out as a key algorithm for optimizing NPC behavior and interactions. This review paper extensively explores the intricate relationship between evolutionary algorithms and NPC behavior, proposing six categories (planning, user interaction, position modification, parameter modification, character state modification, and target assignment strategy), each delineating a distinct role for evolutionary algorithms. Ultimately, the paper draws three main conclusions: the pervasive use of evolutionary algorithms in gaming research, the diversity in game selection for research trials, and the varying strategies employed by researchers in selecting testing techniques. This comprehensive review aims to serve as a valuable reference for future research, particularly in the domain of evolutionary algorithms applied to NPC behavior.

关键词： Non-player character NPC's behavior Artificial intelligence evolutionary algorithm Meta-heuristic Systematic review

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Two-Stage Cooperation Multiobjective evolutionary algorithm Guided by Constraint-Sensitive Variables

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第3期55卷 1450-1463页

作者： Ma, Jun Zhang, Yong Gong, Dun-Wei Gao, Xiao-Zhi Peng, Chao China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221008 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China Univ Eastern Finland Sch Comp Kuopio 70210 Finland

Constrained multiobjective optimization problems are widespread in practical engineering fields. Scholars have proposed various effective constrained multiobjective evolutionary algorithms (CMOEAs) for such problems. However, most existing algorithms overlook the differences between different decision variables in influencing the degree of constraint violation and still lack an effective handling mechanism for constraint-sensitive variables. To address this issue, a two-stage cooperation multiobjective evolutionary algorithm guided by constraint-sensitive variables (CV-TCMOEA) is proposed. In the first stage, a relatively simple auxiliary problem with only a few dominant constraints is constructed to approximate the original problem. After obtaining a set of approximate Pareto optimal solutions by dealing with the auxiliary problem, in the second stage, a constraint-sensitive variable-guided multistrategy cooperation search method is developed. In this method, decision variables are divided into two types: 1) constraint-sensitive and 2) constraint-insensitive variables, and a variable-type-guided cooperative individual update strategy is proposed to autonomously select appropriate search strategies for different types of variables. Experimental results on 28 benchmark functions and 10 engineering problems demonstrated the superiority of the CV-TCMOEA over seven state-of-the-art CMOEAs.

关键词： Optimization evolutionary computation Convergence Approximation algorithms Search problems Electronic mail Switches Space exploration Cybernetics Chaos Constraints constraint-sensitive variables evolutionary algorithm multiobjective optimization

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A multi-objective evolutionary algorithm for feature selection incorporating dominance-based initialization and duplication analysis

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SWARM AND evolutionary COMPUTATION 2025年 95卷

作者： Chen, Chuili Yao, Xiangjuan Gong, Dunwei Tu, Huijie China Univ Min & Technol Sch Math Xuzhou 221116 Jiangsu Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China

The primary objective of feature selection is to reduce the number of features while improving classification performance. Therefore, this problem is typically modeled as a multi-objective optimization problem and can be solved using multi-objective evolutionary algorithms (MOEAs). However, feature selection based on weights derived from preferences may lead to the exclusion of specific features, thereby impacting classification performance. Furthermore, if duplicate individuals are not adequately addressed during the evolutionary process, it may adversely affect the convergence and diversity of the population. In this paper, we propose a multi-objective evolutionary algorithm for feature selection incorporating dominance-based initialization and duplication analysis. To filter features impartially, we transform the correlation issues among features, as well as those between features and labels, into a multi-objective optimization problem by assigning corresponding weights based on their dominance relationships. In addressing the duplication problem within the evolutionary process, the disparity between duplicate individuals as well as between duplicate individuals and elite solutions is analyzed to systematically eliminate redundancy. In the experiments, the proposed method was compared with two classical algorithms and three feature selection algorithms across thirteen datasets. The experimental results indicate that the proposed method exhibits superior classification and optimization performance across the majority of datasets.

关键词： Feature selection Multi-objective optimization evolutionary algorithm Duplication analysis

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A multi-objective multi-task evolutionary algorithm based on source task transfer

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APPLIED SOFT COMPUTING 2025年 174卷

作者： Chai, Zheng-Yi Nie, Ying Li, Yan-Lun Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tiangong Univ Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

In the real world, many optimization problems often do not exist in isolation, they usually have complex interactions and dependencies, and have multiple optimization goals. In order to improve the performance of individual task solving, an evolutionary multi-task multi-objective optimization algorithm (MTMOO) is proposed. However, most of the current evolutionary algorithms are based on the assumption that the prior knowledge (experience in solving optimization problems) is zero, which makes the ability of the algorithm to solve problems cannot be improved with the increase of historical experience, and greatly limits the adaptability and learning ability of the algorithm. In order to overcome this limitation, this paper proposed a multi-objective and multi-task adaptive migration evolutionary algorithm (MOMFEA-STT). The algorithm constructs the parameter sharing model of historical task and target task online. By identifying the degree of association between different tasks, the intensity of cross-task knowledge transfer is automatically adjusted to maximize the capture, sharing and utilization of common useful knowledge to solve related tasks. In addition, in order to strengthen the exploration and exploitation ability of the algorithm and avoid the problem that the algorithm is easy to fall into the local optimum, the MOMFEA-STT adopts a new method of generation of children, generates children by using spiral search mode, and constantly adjusts the search direction of the algorithm. Experimental results show that the proposed method outperforms the existing algorithms on the multi-task optimization benchmark problems.

关键词： Multi-objective optimization Multi-task optimization evolutionary algorithm Adaptive transfer

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A Multi-Objective evolutionary algorithm-based optimization framework for hybrid absorption-compression heat pump systems

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APPLIED ENERGY 2025年 382卷

作者： Wei, Junzhuo Wu, Di Wang, Ruzhu Shanghai Jiao Tong Univ Inst Refrigerat & Cryogen 800 DongChuan Rd Shanghai 200240 Peoples R China Shanghai Nuotong New Energy Technol Co Ltd 1391 Humin Rd Shanghai 200241 Peoples R China

The hybrid absorption-compression heat pump (HACHP) system has garnered widespread attention due to its unique advantage of combining absorption and compression heat pump technologies, allowing it to operate over a wider temperature range and exhibit higher system efficiency. However, the distinct characteristics of the HACHP system's performance curves, the nonlinear coupling between variables, and the diverse performance indicators present significant challenges for system optimization. To address these challenges, this paper proposes a simulation-based optimization framework specifically tailored for HACHP systems, using a multiobjective evolutionary algorithm (MOEA) to tackle constrained multi-objective optimization problems (CMOP). The framework integrates three innovative algorithms: differential evolution optimized symbolic regression (DEOSR), constrained range adaptive sampling (CRAS), and variable speed mutation (VSM). The DEOSR algorithm models the constraint relationships between system variables through inequality fitting, and after comparing various mutation strategies, achieves an optimal fitting result with an R2 of 0.9964 and an RMSE of 1.9912. The CRAS algorithm enhances the diversity of the initial population through a classified sampling strategy, significantly improving the population's coverage in the value space and effectively preventing the risk of missing boundary optima in the HACHP system. The VSM algorithm dynamically adjusts the mutation rate during the optimization process, balancing global and local search capabilities, and effectively addresses the abrupt changes often observed in heat pump performance curves. Additionally, the framework incorporates a strategy based on non-dominated sorting and reference points method (RPM), enabling it to efficiently handle high-dimensional multi-objective optimization problems. Ultimately, this framework was successfully applied to a thermally coupled HACHP system.

关键词： Hybrid absorption-compression heat pump evolutionary algorithm Multi-objective optimization TOPSIS

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Identifying stable Nb-O clusters using evolutionary algorithm and DFT: A foundation for machine learning potentials

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CHEMICAL PHYSICS 2025年 590卷

作者： Popov, Ilya S. Valeeva, Albina A. Enyashin, Andrey N. Russian Acad Sci Ural Branch Inst Solid State Chem Ekaterinburg 620990 Russia

Crystal structure of the bulk NbO can be described as the NaCl (B1) lattice with equimolar 25 % content of ordered vacancies in both sublattices. While numerous studies have explored the phase stability ranges and crystallography of niobium oxides under various temperatures and pressures, the atomic structure of these compounds as small clusters remains unsolved. Understanding this structure is crucial for investigating the formation and growth of niobium oxide nanoparticles and thin films. In this work, the evolutionary algorithms guided by DFT calculations were employed to identify the most viable structures of NbnOm clusters with indices 1 < n <6, 0 < m <6. The indices of clusters with enhanced stability and higher probabilities of formation during stochastic synthesis processes were proposed. Additionally, a machine learning potential for the Nb-O system was derived from the accumulated set of DFT calculations of NbnOm clusters.

关键词： DFT Machine learning potential evolutionary algorithm Niobium oxide NbO Nanoparticle Cluster

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A parallel large-scale multiobjective evolutionary algorithm based on two-space decomposition

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COMPLEX & INTELLIGENT SYSTEMS 2025年第5期11卷 1-37页

作者： Yin, Feng Cao, Bin Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China

Decomposition is an effective and popular strategy used by evolutionary algorithms to solve multiobjective optimization problems (MOPs). It can reduce the difficulty of directly solving MOPs, increase the diversity of the obtained solutions, and facilitate parallel computing. However, with the increase of the number of decision variables, the performance of multiobjective evolutionary algorithms (MOEAs) often deteriorates sharply. The advantages of the decomposition strategy are not fully exploited when solving such large-scale MOPs (LSMOPs). To this end, this paper proposes a parallel MOEA based on two-space decomposition (TSD) to solve LSMOPs. The main idea of the algorithm is to decompose the objective space and decision space into multiple subspaces, each of which is expected to contain some complete Pareto-optimal solutions, and then use multiple populations to conduct parallel searches in these subspaces. Specifically, the objective space decomposition approach adopts the traditional reference vector-based method, whereas the decision space decomposition approach adopts the proposed method based on a diversity design subspace (DDS). The algorithm uses a message passing interface (MPI) to implement its parallel environment. The experimental results demonstrate the effectiveness of the proposed DDS-based method. Compared with the state-of-the-art MOEAs in solving various benchmark and real-world problems, the proposed algorithm exhibits advantages in terms of general performance and computational efficiency.

关键词： Multiobjective optimization Large-scale multiobjective optimization evolutionary algorithm Parallel computing Two-space decomposition

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An adjoint feature-selection-based evolutionary algorithm for sparse large-scale multiobjective optimization

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COMPLEX & INTELLIGENT SYSTEMS 2025年第2期11卷 1-18页

作者： Zhang, Panpan Yin, Hang Tian, Ye Zhang, Xingyi Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Sparse large-scale multiobjective optimization problems (sparse LSMOPs) are characterized by an enormous number of decision variables, and their Pareto optimal solutions consist of a majority of decision variables with zero values. This property of sparse LSMOPs presents a great challenge in terms of how to rapidly and precisely search for Pareto optimal solutions. To deal with this issue, this paper proposes an adjoint feature-selection-based evolutionary algorithm tailored for tackling sparse LSMOPs. The proposed optimization strategy combines two distinct feature selection approaches. Specifically, the paper introduces the sequential forward selection approach to investigate independent sparse distribution, denoting it as the best sequence of decision variables for generating a high-quality initial population. Furthermore, it introduces the Relief approach to determine the relative sparse distribution, identifying crucial decisive variables with dynamic updates to guide the population in a promising evolutionary direction. Experiments are conducted on eight benchmark problems and two real-world problems, and experimental results verify that the proposed algorithm outperforms the existing state-of-the-art evolutionary algorithms for solving sparse LSMOPs.

关键词： Large-scale multiobjective optimization Sparse optimization evolutionary algorithm Adjoint feature selection Sparse distribution

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Many-Objective Feedback evolutionary algorithm for Optimizing the Software Test Suite

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SN Computer Science 2025年第1期6卷 1-18页

作者： Jaiswal, Updesh Kumar Prajapati, Amarjeet Department of CSE & IT Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

In automated software testing;optimization of test suites is an important and challenging problem. In the literature of test suite optimization, various single-objective and multi-objective optimization approaches have been proposed by researchers and practitioners. Even though the multi-objective optimization approaches have demonstrated effective performance in test case optimization, their performance tends to degrade when the number of objectives exceeds by more than or equal to three. To tackle this issue, recently few approaches have been proposed. Even though these approaches perform well, there is still scope for improvement. In this work, we introduce a Many-objective Differential Evolution Feedback evolutionary algorithm (MDEFEA) for the test case optimization problem. In this approach, we exploit the Differential Evolution (DE) algorithm and Information Feedback Model (IFM) concept to optimize line coverage (LC), branch coverage (BC), mutation coverage (MC), and multi-criteria coverage (MCC) simultaneously. To assess the performance of the proposed MDEFEA, we evaluated it across 41 complex projects and compared its results with other similar optimization techniques, including NSGA-III, MOPSO, LSMaOA, and MOEA. The findings of the proposed and existing approaches indicate that MDEFEA outperforms the existing approaches in terms of the Mean Coverage Value (MCV) and Worst Coverage Value (WCV) success rates. Overall, the results demonstrate that the proposed MDEFEA is an effective method for test case optimization containing more than three criteria. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automated software testing evolutionary algorithm Information feedback model Many-objective optimization Pareto front Software testing coverage Test suite optimization

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