Swarm and Evolutionary Computation最新文献

{"title":"An evolution strategies-based reinforcement learning algorithm for multi-objective dynamic parallel machine scheduling problems","authors":"Yarong Chen ,&nbsp;Junjie Zhang ,&nbsp;Jabir Mumtaz ,&nbsp;Shenquan Huang ,&nbsp;Shengwei Zhou","doi":"10.1016/j.swevo.2025.101944","DOIUrl":"10.1016/j.swevo.2025.101944","url":null,"abstract":"<div><div>The multi-objective dynamic parallel machine scheduling (PMS) problem is a complex combinatorial optimization challenge encountered in manufacturing systems. Various uncertainties exist in the real-world dynamic PMS problem, such as job release time, processing time, and flexible preventive maintenance for machines. The goal is simultaneously optimizing multiple objectives under dynamic and uncertain environments, such as makespan, total tardiness, and energy consumption. This paper proposes an evolution strategies-based reinforcement learning (ESRL) algorithm to address the current multi-objective dynamic PMS problem. The proposed algorithm leverages the exploration capabilities of evolution strategies to evolve effective policies for reinforcement learning in dynamic scheduling. Moreover, the efficiency of the ESRL algorithm is enhanced by implanting three features: a) train the policy to iteratively produce the sequence directly and mitigate the sparse reward issue resulting from the symmetry inherent in the given problem; b) a multi-agent system with independent interaction and centralized training to generate the PMS policy simultaneously; c) a non-dominated sorting mechanism to determine fitness function. Extensive computational experimental results show that the ESRL algorithm outperforms the comparison state-of-the-art evolutionary algorithms and priority dispatching rules in terms of solution quality, convergence, and efficiency, with the advantage of the C-matrix exceeding 60 %, and the advantages in GD and NR surpassing 50 %. Furthermore, ablation experiments demonstrate the significant contributions of additional features in ESRL in enhancing the algorithm's performance. Meanwhile, the results of generalization experiments indicate that the ESRL quickly generates Pareto optimal solutions allowing the trained model to make optimal scheduling decisions.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101944"},"PeriodicalIF":8.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selection hyperheuristic with knowledge-based Q-learning for dynamic distributed hybrid flow shop scheduling problem considering operation inspection","authors":"Lin Luo ,&nbsp;Xuesong Yan","doi":"10.1016/j.swevo.2025.101936","DOIUrl":"10.1016/j.swevo.2025.101936","url":null,"abstract":"<div><div>In practical production environments, operation inspection plays a critical role in rescheduling defective products within the flow line, ensuring the smooth progression of subsequent processing stages. Despite its importance, this topic has received relatively little research attention. This paper addresses the dynamic distributed hybrid flow shop scheduling problem considering operation inspection (DHFSPI) aimed at minimizing makespan, where a operation of the job can either be scrapped or require reprocessing. A mathematical model is formulated for DHFSPI, and a selection hyperheuristic with knowledge-based Q-learning (SHKQL) is proposed to solve the problem. In SHKQL, eight pre-designed low-level heuristics (LLHs) are employed alongside knowledge-based Q-learning, which serves as the high-level heuristic (HLH). It adaptively selects these LLHs based on historical optimization knowledge. An initialization method is developed to construct the initial population, factoring in factory workload balance and random operation inspection. During the Q-learning process, a time-adaptive <span><math><mi>ϵ</mi></math></span>-greedy strategy is applied to guide the learning and application of historical knowledge. A rescheduling strategy is developed to address reprocessing and scrapping outcomes during operation inspection, considering production-specific characteristics. Benchmark instances of DHFSPI are constructed to evaluate the performance of SHKQL. The SHKQL is compared with several closely relevant scheduling methods through extensive experiments, and the results highlight its superior performance. This research provides valuable insights for managers dealing with dynamic distributed flow shop manufacturing systems, particularly those involving reprocessing and scrapping.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101936"},"PeriodicalIF":8.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic prompt design via particle swarm optimization driven LLM for efficient medical information extraction","authors":"Tian Zhang ,&nbsp;Lianbo Ma ,&nbsp;Shi Cheng ,&nbsp;Yikai Liu ,&nbsp;Nan Li ,&nbsp;Hongjiang Wang","doi":"10.1016/j.swevo.2025.101922","DOIUrl":"10.1016/j.swevo.2025.101922","url":null,"abstract":"<div><div>Medical information extraction (IE) is an essential aspect of electronic health records (EHRs), but it is a challenging task that converts plain text into structured knowledge, where domain models struggle to achieve performance. Recently, large language models (LLMs), which have demonstrated remarkable capabilities in text understanding and generation, have emerged as a promising method for handling natural language texts. However, LLMs are too dependent on elaborate prompts, resulting in extensive expert knowledge and manual prompt templates needed. In this work, we propose a novel method for the automatic prompt design, called <strong>P</strong>article <strong>S</strong>warm <strong>O</strong>ptimization-based <strong>P</strong>rompt using a <strong>L</strong>arge language model (<strong>PSOPL</strong>). As an efficient method for medical information extraction from EHRs, PSOPL can allow particle swarm optimization (PSO) to automate design prompts by leveraging LLM’s ability to generate coherent text token-by-token. Specifically, starting with a small number of initial prompts, evolutionary operators in PSOPL guide the LLM to generate new candidate prompts iteratively, and the PSOPL evaluates population fitness to retain the optimal prompts. In this way, PSOPL can achieve prompt evolution without model training and reduce the human effort and requirement for domain knowledge. We conducted experiments for open-source LLMs (e.g., Alpaca-7B, GPT-J-6B) and closed-source LLM (e.g., GLM-4), on public medical datasets (e.g., CMeEE, CMeIE, CHIP-CDEE) covering information extraction tasks (e.g., named Entity recognition, relation extraction, event extraction) to verify the method’s generalizability. The experimental results demonstrate the potential of using PSO-based LLMs to design prompts automatically, allowing for the swift extraction of important information about patients in the EHRs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101922"},"PeriodicalIF":8.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A constrained multi-objective optimization algorithm with adaptive dual-stage search strategy utilizing the relationship between different Pareto fronts","authors":"Kai Su,&nbsp;Zhihui He,&nbsp;Feng Wang","doi":"10.1016/j.swevo.2025.101937","DOIUrl":"10.1016/j.swevo.2025.101937","url":null,"abstract":"<div><div>Multi-stage dual-population constrained evolutionary algorithms (MDCMOEAs) demonstrate competitive performance in solving constrained multi-objective optimization problems (CMOPs). In these algorithms, the main population addresses the original problem, while the auxiliary population solves the helper problem across multiple stages, including both unconstrained and constrained stages. However, MDCMOEAs face challenges in effectively searching the constrained Pareto front (CPF) that overlaps with the unconstrained Pareto front (UPF), particularly when feasible regions are small or disconnected. This difficulty arises because the auxiliary population considers constraints in some stages, making it susceptible to becoming trapped in local feasible regions. To overcome this challenge, this paper proposes an algorithm with an adaptive dual-stage search strategy (ADSSCMO). First, an improved <span><math><mi>ϵ</mi></math></span>-constraint method is developed for the main population to tackle the original CMOPs. Second, an adaptive dual-stage search strategy is designed for the auxiliary population. This strategy dynamically evaluates the relationship between UPF and CPF and determines whether to solve the unconstrained or constrained problem. Extensive experiments on four test suites and seven real-world problems demonstrate that the proposed algorithm is more competitive than seven state-of-the-art CMOEAs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101937"},"PeriodicalIF":8.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Adaptive search based Grey Wolf optimization algorithm for multi-objective optimization of ethylene cracking furnace” [Swarm Evol. Comput. 92 (2025) 101,810]","authors":"Zhiqiang Geng ,&nbsp;Weikang Kong ,&nbsp;Xintian Wang ,&nbsp;Ling Wang ,&nbsp;Yongming Han","doi":"10.1016/j.swevo.2025.101948","DOIUrl":"10.1016/j.swevo.2025.101948","url":null,"abstract":"<div><div>The ethylene cracking furnace (ECF) is an important device for producing ethylene and propylene, so the optimization problem of the ECF is crucial. However, traditional optimization algorithms such as the grey wolf optimization (GWO) algorithm, are prone to getting stuck in local optima under the early stages and have low optimization accuracy under the later stage, which cannot effectively optimize the production of the ECF. Therefore, a novel multi-objective grey wolf optimization algorithm based on the adaptive search (ASMOGWO) is proposed. The non-linear convergence factor of the cosine transform in the ASMOGWO algorithm offsets its discovery and development capabilities. Then, the velocity formula of the GWO is updated based on the velocity update, effectively preventing individuals from entering local optima and improving the convergence performance. Meanwhile, the linearly decreasing inertia weight coefficients is proposed to control the convergence speed of the ASMOGWO. Compared with other optimization algorithms through public experiments, the ASMOGWO has good effects. Finally, the ASMOGWO algorithm is applied to optimize the ethylene yield and the propylene yield of the ECF. The result shows the proposed ASMOGWO has better feasibility than the original GWO algorithm and other optimization algorithms. Meanwhile, the optimized ethylene yield increased by 1.3570%, while the propylene yield decreased by 0.0093%.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101948"},"PeriodicalIF":8.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DJAYA-RL: Discrete JAYA algorithm integrating reinforcement learning for the discounted {0-1} knapsack problem","authors":"Zuhua Dai ,&nbsp;Yongqi Zhang","doi":"10.1016/j.swevo.2025.101927","DOIUrl":"10.1016/j.swevo.2025.101927","url":null,"abstract":"<div><div>The JAYA algorithm is a swarm heuristic algorithm designed for solving continuous space problems. To apply it to the Discounted {0-1} Knapsack Problem (D{0-1}KP), it must be optimized into a discrete problem solving algorithm. Based on three decision vector encoding schemes for the D{0-1}KP, this paper discretely improves the JAYA algorithm using Q-learning and proposes three Discrete JAYA-RL (DJAYA-RL) algorithms: FirBJAYA-RL (the First Binary JAYA Algorithm Integrated with Reinforcement Learning), SimBJAYA-RL (the Simplified Binary JAYA Algorithm Integrated with Reinforcement Learning), and QJAYA-RL (the Quaternary JAYA Algorithm Integrated with Reinforcement Learning). Subsequently, a comparative analysis of the algorithm performance among the three DJAYA-RLs is conducted.</div><div>The DJAYA-RL algorithms utilize the Q-learning mechanism to adaptively control the number of grouped coding bits during individual updates. This enables the algorithms to explore the solution space in different regions more effectively. Meanwhile, the DJAYA-RL algorithms generate populations in different episodes by leveraging the information entropy of the historically optimal individual. This approach enhances the population diversity and helps avoid premature convergence.</div><div>Experimental results on the standard dataset of the D{0-1}KP demonstrate that the average solution errors of FirBJAYA-RL, SimBJAYA-RL, and QJAYA-RL are 1.1%, 0.15%, and 0.20% respectively. These results indicate that differences in encoding schemes have an impact on algorithm performance. Among the three encoding types, SimBJAYA-RL exhibits the best solution quality, while QJAYA-RL shows the best time performance. When compared with the genetic algorithm, firefly algorithm, and particle swarm algorithm for solving the D{0-1}KP, the average solution error rate of DJAYA-RL is significantly lower than that of the three swarm heuristic algorithms with corresponding encoding schemes. Moreover, compared with the three previously proposed discrete JAYA algorithms, the average solution error rate of the DJAYA-RL algorithm is significantly lower than that of the BJaya-JS, IBJA, and JayaX algorithms with corresponding encoding schemes.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101927"},"PeriodicalIF":8.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three stage based reinforcement learning for combining multiple metaheuristic algorithms","authors":"Xiaotong Liu ,&nbsp;Tianlei Wang ,&nbsp;Zhiqiang Zeng ,&nbsp;Ye Tian ,&nbsp;Jun Tong","doi":"10.1016/j.swevo.2025.101935","DOIUrl":"10.1016/j.swevo.2025.101935","url":null,"abstract":"<div><div>Combined of metaheuristic algorithms can effectively improve the performance of algorithms by utilizing the characteristics of different metaheuristic algorithms, and the key is how to combine multiple metaheuristic algorithms. Reinforcement learning is one of the effective methods for combining multiple metaheuristic algorithms. However, designing a competitive reinforcement learning approach to achieve efficient collaboration among metaheuristic algorithms is a highly challenging task. Therefore, this study proposes a three-stage reinforcement learning for combining multiple metaheuristic algorithms (TSRL-CMM). TSRL-CMM is divided into three stages: the exploration stage, the stage with both exploration and exploitation, and the exploitation stage. On this basis, an adaptive action selection strategy and a reward function are designed. The proposed action selection strategy can adaptively select appropriate metaheuristic algorithms based on the state of the population, achieving a balance between exploration and exploitation. The proposed reward function can effectively guide the population to transition to the expected state based on the iteration stage and state transitions. To verify the effectiveness of TSRL-CMM, we evaluated it using the CEC2017 test suite, nine real-world engineering design problems and six power system optimization problems. TSRL-CMM was compared with 10 state-of-the-art metaheuristic algorithms, and experimental results showed that TSRL-CMM performed better than the compared algorithms in both artificial and real-world problems. Furthermore, TSRL-CMM was specifically compared with three CEC winner algorithms on the CEC 2017 benchmark test suite. The experimental results show that the proposed algorithm is highly competitive. The source code can be obtained from <span><span>https://github.com/xtongliu/TSRL-CMM-code</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101935"},"PeriodicalIF":8.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Swarm Intelligent Search and Rescue method for economic emission load dispatch of renewable integrated power system considering uncertainty","authors":"Shilpa Mishra ,&nbsp;Abdul Gafoor Shaik ,&nbsp;Om Prakash Mahela","doi":"10.1016/j.swevo.2025.101928","DOIUrl":"10.1016/j.swevo.2025.101928","url":null,"abstract":"<div><div>In the realm of power systems, Economic Emission Load Dispatch (EELD) problem is one of the most important bi-objective optimisation problem, associated with high complexity and non-linearities. This research proposes a novel metaheuristic optimization approach hybridizing the PSO and SAR and abbreviated as Swarm Intelligent Search and Rescue method (SISAR). It utilizes the features of Particle Swarm Optimization algorithm to strengthen the global searching capability of original SAR algorithm. SISAR overcomes the drawback of SAR of getting trapped into local minima by utilizing velocity-based position update concept of PSO to improve the overall convergence. SISAR approach is initially evaluated on 10-unit, 2000 MW and 6-unit, IEEE30 bus standard test systems. Results are compared with advanced algorithms such as SAR, PSO, GWO, WOA, GA, DE and MFO in order to prove its superiority. Subsequent to the establishment of the proposed algorithm on system without renewable sources, it is further applied to a RE integrated power system comprising of six thermal units, 1 wind and 1 solar unit. Here, uncertainty due to RESs is dealt using a 2-stage uncertainty handling approach to obtain more accurate and feasible EELD solution. Robustness of the uncertainty handling approach is established by investigating the impact of different penetration levels of RE sources on cost and emission while solving EELD. A reduction of 1.67 % in cost and 2.56 % in emission have been achieved by proposed SISAR algorithm as compared to SAR (next competitive method) while performing EELD on RE integrated system with all sources.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101928"},"PeriodicalIF":8.2,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective evolutionary co-learning framework for energy-efficient hybrid flow-shop scheduling problem with human-machine collaboration","authors":"Jiawei Wu,&nbsp;Yong Liu,&nbsp;Yani Zhang","doi":"10.1016/j.swevo.2025.101932","DOIUrl":"10.1016/j.swevo.2025.101932","url":null,"abstract":"<div><div>The hybrid flow-shop scheduling problem (HFSP) has been extensively studied in modern flexible manufacturing systems. With the advent of Industry 5.0, incorporating energy efficiency and human-machine collaboration into scheduling decisions presents significant challenges. This study introduces a mathematical model for the energy-efficient HFSP with human-machine collaboration (EHFSP-HMC), aiming to minimize both makespan and total energy consumption. To tackle this strongly NP-hard problem, we propose a multi-objective evolutionary co-learning framework (MOECLF) that combines reinforcement learning with evolutionary algorithms. The framework integrates two proximal policy optimization (PPO) agents and a Q-learning agent for multi-agent hyper-heuristic search. The methodology consists of three key components: (1) problem-specific heuristic rules and low-level heuristics derived from identified problem properties, (2) a multi-strategy initialization mechanism for generating high-quality initial solutions, and (3) a hybrid learning approach where PPO agents, equipped with MobileNetV2 and efficient channel attention, identify feasible solution matrices for Pareto-optimal boundary solutions, while Q-learning directs the search for remaining solutions. Both learning mechanisms share a unified action space and reward function based on dominance judgment. Comprehensive computational experiments demonstrate that MOECLF significantly outperforms three state-of-the-art multi-objective evolutionary algorithms in solving the EHFSP-HMC and achieves superior dominance, convergence, and diversity in Pareto solutions. Additionally, an impact analysis of worker fatigue and a real-world case study validate the practical applicability and effectiveness of the proposed model and framework.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101932"},"PeriodicalIF":8.2,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An individual adaptive evolution and regional collaboration based evolutionary algorithm for large-scale constrained multiobjective optimization problems","authors":"Kunjie Yu ,&nbsp;Zhenyu Yang ,&nbsp;Jing Liang ,&nbsp;Kangjia Qiao ,&nbsp;Boyang Qu ,&nbsp;Ponnuthurai Nagaratnam Suganthan","doi":"10.1016/j.swevo.2025.101925","DOIUrl":"10.1016/j.swevo.2025.101925","url":null,"abstract":"<div><div>Large-scale constrained multiobjective optimization problems (LSCMOPs) refer to constrained multiobjective optimization problems (CMOPs) with large-scale decision variables. When using evolutionary algorithms to solve LSCMOPs, the main challenge lies in balancing feasibility, convergence, and diversity in the high-dimensional search space. However, only a few studies focus on LSCMOPs and most existing related algorithms fail to achieve satisfactory performance. This paper proposes two novel mechanisms (the individual adaptive evolution strategy and the regional collaboration mechanism) to tackle these challenges. The individual adaptive evolution mechanism introduces a dynamic approach to optimize convergence-related and diversity-related variables by allocating computational resources to individuals based on their evolution states. This method effectively balances convergence and diversity in the high-dimensional search space. The regional collaboration mechanism, on the other hand, employs an auxiliary population to explore multiple sub-regions to maintain diversity, guiding the main population towards the constrained Pareto front. By combining these two mechanisms within a two-stage algorithm framework, a new algorithm IAERCEA is proposed. IAERCEA and nine other state-of-the-art algorithms are evaluated on several benchmark suites and three dynamic economic emissions dispatch problems. The results show that IAERCEA has better or competitive performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101925"},"PeriodicalIF":8.2,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}