Swarm and Evolutionary Computation最新文献

{"title":"Surrogate-assisted neural learning and evolutionary optimization for expensive constrained multi-objective problems","authors":"Wenji Li ,&nbsp;Yifeng Qiu ,&nbsp;Zhaojun Wang ,&nbsp;Biao Xu ,&nbsp;Zhifeng Hao ,&nbsp;Qingfu Zhang ,&nbsp;Yun Li ,&nbsp;Zhun Fan","doi":"10.1016/j.swevo.2025.102020","DOIUrl":"10.1016/j.swevo.2025.102020","url":null,"abstract":"<div><div>Expensive constrained multi-objective optimization problems (ECMOPs) present significant challenges due to the high computational cost of evaluating objective and constraint functions, which severely limits the number of feasible function evaluations. To address this issue, we propose an efficient surrogate-assisted constrained multi-objective evolutionary algorithm, named LEMO. LEMO integrates neural learning with a novel constraint screening strategy to dynamically construct surrogate models for the most relevant constraints. During the optimization process, a neural network is designed to learn the mapping between arbitrary weight vectors and their corresponding constrained Pareto optimal solutions. This enables the generation of high-quality solutions while requiring fewer expensive function evaluations. Additionally, a constraint screening mechanism is introduced to dynamically exclude constraints that are irrelevant to the current search phase, thus simplifying the surrogate models and improving the efficiency of the constrained search process. To evaluate the effectiveness of LEMO, we compare its performance against seven state-of-the-art algorithms on three benchmark suites, LIRCMOP, DASCMOP, and MW, as well as a real-world optimization problem. The experimental results demonstrate that LEMO consistently outperforms these algorithms in both computational efficiency and solution quality.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102020"},"PeriodicalIF":8.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365083","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":"Constrained multi-objective optimization assisted by competitive mechanism based reward auxiliary optimization problems","authors":"Haoming Zhang ,&nbsp;Qianlong Dang ,&nbsp;Xinyu Feng ,&nbsp;Xiaochuan Gao","doi":"10.1016/j.swevo.2025.102021","DOIUrl":"10.1016/j.swevo.2025.102021","url":null,"abstract":"<div><div>Solving constrained multi-objective optimization problems requires simultaneously satisfying multiple objectives and constraints, presenting a significant challenge for solving tasks. Constructing auxiliary optimization problems to assist the main optimization problem in accelerating convergence is a common approach for constrained multi-objective evolutionary algorithms (CMOEAs). However, this approach may waste computational resources on auxiliary optimization problems that provide little benefit to the main optimization problem at certain stages of the evolution process. Based on the above issue, this paper proposes a constrained multi-objective optimization algorithm to address this issue via competitive mechanism based reward auxiliary optimization problems (RACMO). Specifically, an unconstrained auxiliary optimization problem and a dynamic constrained auxiliary optimization problem are constructed. They are rewarded by the number of solutions provided to the main optimization problem, and the cumulative reward is mapped to the probability to adaptively selecting more valuable auxiliary optimization problems. Moreover, an adaptive stop-update strategy is designed. By controlling the competition between two auxiliary populations and adaptive stop-updating, excellent convergence is guaranteed while significantly saving computational resources. Experimental results demonstrate the competitiveness of RACMO compared to ten advanced CMOEAs on three test suites and eight practical application problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102021"},"PeriodicalIF":8.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365084","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":"Offline evolutionary optimization with problem-driven model pool design and weighted model selection indicator","authors":"Huixiang Zhen ,&nbsp;Bing Xue ,&nbsp;Wenyin Gong ,&nbsp;Mengjie Zhang ,&nbsp;Ling Wang","doi":"10.1016/j.swevo.2025.102034","DOIUrl":"10.1016/j.swevo.2025.102034","url":null,"abstract":"<div><div>Offline data-driven evolutionary algorithms aim to provide a promising solution based on the collected historical data, without online real fitness evaluations. However, the suitability of surrogate models varies significantly across different problem types, and current research often overlooks the relationship between problem characteristics and model performance. To address this gap, we propose a novel offline data-driven evolutionary algorithm, termed MSEA, which integrates a problem-driven model pool design and a weighted indicator-based model selection mechanism. The model pool is carefully designed, incorporating four distinct surrogate models tailored for various optimization landscapes to align with diverse problem characteristics. A weighted selection indicator, derived from both model evaluation and solution quality assessment, is employed to dynamically select the most suitable model for the optimization problem. Extensive experimental results demonstrate that MSEA effectively identifies and utilizes the optimal model from the pool for specific offline optimization tasks. Compared to five state-of-the-art offline data-driven methods, MSEA achieved optimal results for 26 out of 32 functions across dimensions ranging from 10 to 100 and also exhibited faster running times. Furthermore, in high-dimensional spaces, MSEA achieved the best optimization results in dimensions ranging from 200 to 500. Our code is available at <span><span>https://github.com/zhenhuixiang/MSEA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102034"},"PeriodicalIF":8.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364917","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":"Evolutionary algorithm with cross-diversity integration and mutation synergy operation for multi-objective recommendation","authors":"Liang Chu ,&nbsp;Ye Tian","doi":"10.1016/j.swevo.2025.102031","DOIUrl":"10.1016/j.swevo.2025.102031","url":null,"abstract":"<div><div>Recommendation algorithms have become increasingly prevalent in modern society, addressing overload by delivering content aligned with user preferences. While accuracy is prioritized in traditional approaches, diversity is also crucial in recommendation systems. However, the balance between these two objectives is challenged by a fundamental trade-off. To address this issue, an enhanced multi-objective evolutionary algorithm (MOEA-EMRS) is proposed, in which cross-diversity mechanism and mutation synergy operation are integrated for multi-objective recommendations. MOEA-EMRS integrates three core components: a novel population initialization mechanism that constructs a distinctive primitive population with enhanced diversity and accuracy, a diversity-preserving crossover operator, and objective-oriented mutation operation specifically designed to reinforce Pareto optimality. To evaluate the algorithm’s performance, comparative experiments were conducted between MOEA-EMRS and existing multi-objective models. Experimental results demonstrate that MOEA-EMRS outperforms existing algorithms in performance effectiveness.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102031"},"PeriodicalIF":8.2,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335762","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":"Regression and relation-assisted evolutionary algorithm for high-dimensional expensive multi-objective optimization","authors":"Shuwei Zhu ,&nbsp;Yimo Zhang ,&nbsp;Wei Fang ,&nbsp;Meiji Cui ,&nbsp;Kalyanmoy Deb","doi":"10.1016/j.swevo.2025.101978","DOIUrl":"10.1016/j.swevo.2025.101978","url":null,"abstract":"<div><div>Surrogate-assisted evolutionary algorithms (SAEAs) have gained a lot of attention to handle expensive multi-objective optimization problems (EMOPs). However, when it comes to high-dimensional EMOPs (HEMOPs), the performance of existing SAEAs degrades dramatically because of the dimensionality sensitivity issue, in which effective surrogate models are difficult to build. To this end, we propose a regression- and relation-assisted evolutionary algorithm (<span><math><mrow><msup><mrow><mtext>R</mtext></mrow><mrow><mn>2</mn></mrow></msup><mtext>AEA</mtext></mrow></math></span>) to deal with HEMOPs, which involves a regression-assisted weight optimization (RWO) stage and a relation-assisted multi-objective optimization (RMO) stage. To be specific, the RWO is facilitated by the problem transformation strategy and regression models. It reformulates the high-dimensional problem into a relative low-dimensional one and intends to converge to the Pareto-optimal front (PF) efficiently. Thereafter, the RMO concentrates on maintaining the population diversity with a new infill sampling criterion, which considers the optimization performance as well as the uncertainty estimated by the predicted entropy. To validate its effectiveness, we compare <span><math><mrow><msup><mrow><mtext>R</mtext></mrow><mrow><mn>2</mn></mrow></msup><mtext>AEA</mtext></mrow></math></span> with five state-of-the-art algorithms on various benchmark test suites with dimensions varying from 50 to 200, and six real-world HEMOPs. Experimental results show the superiority of <span><math><mrow><msup><mrow><mtext>R</mtext></mrow><mrow><mn>2</mn></mrow></msup><mtext>AEA</mtext></mrow></math></span> in terms of convergence speed and diversity maintenance with limited computational resources.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 101978"},"PeriodicalIF":8.2,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331060","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 improved genetic algorithm and clone selection optimization-based gated recurrent unit networks for earthquake magnitude prediction","authors":"Wen Zhou ,&nbsp;Xinchun Yi ,&nbsp;Changyi Li ,&nbsp;Zhiwei Ye ,&nbsp;Qiyi He ,&nbsp;Xiuwen Gong ,&nbsp;Qiao Lin","doi":"10.1016/j.swevo.2025.102023","DOIUrl":"10.1016/j.swevo.2025.102023","url":null,"abstract":"<div><div>Earthquake magnitude prediction is a vital rendezvous for human safety, economic and property losses. The earthquake occurrence process represents a highly complex nonlinear problem. Meanwhile, artificial intelligence methods have emerged as automated and intelligent frameworks for addressing magnitude prediction challenges. However, these approaches ignore redundant features and have lower prediction accuracy. Genetic Algorithms (GA) excel in feature selection and Gated Recurrent Units (GRU) have strong time series prediction capabilities. Therefore, we propose a novel earthquake magnitude prediction method, named Improved GA and a Clone Selection Optimization-based GRU (IGA-CSOGRU). First, an improved GA with generation gap strategy is presented to enhance the feature selection capability of time-series data in prediction models. Second, GRU is implemented as the core prediction model. To optimize its hyperparameters, a novel approach combining Latin hypercube sampling with adaptive mutation CSO is introduced, thereby enhancing prediction performance. Finally, to validate the performance of the proposed IGA-CSOGRU, a novel earthquake magnitude prediction dataset is constructed, which is acquired from the self-developed Acoustic &amp; Electromagnetics to AI (AETA) platform. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> were used for assessment. The proposed IGA-CSOGRU model demonstrates significant performance improvements across all datasets, achieving an average RMSE reduction of 5%–7% compared to all baseline methods, highlighting the model’s superior capability in handling challenging time series prediction tasks. The implementation code supporting the findings of this study is available at <span><span>https://github.com/123fggv/Earthquake-prediction</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102023"},"PeriodicalIF":8.2,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331059","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 decomposition framework with dual populations and dual stages for constrained multi-objective optimization","authors":"Zhanglu Hou ,&nbsp;Jialu Ye ,&nbsp;Yizhang Xia ,&nbsp;Yibin Gong ,&nbsp;Juan Zou ,&nbsp;Shengxiang Yang","doi":"10.1016/j.swevo.2025.102030","DOIUrl":"10.1016/j.swevo.2025.102030","url":null,"abstract":"<div><div>The main challenge of constrained multi-objective optimization involves achieving a good balance among feasibility, convergence, and diversity simultaneously. However, most existing methods exhibit an imbalance, making it hard to converge towards Pareto-optimal front (PF) while maintaining the diversity of feasible solutions. To address this issue, this paper proposes a new dual-population and dual-stage constrained multi-objective optimization algorithm, denoted as DD-M2M, based on the multi-objective-to-multi-objective decomposition framework (M2M). In this approach, dual populations, referred to as <span><math><mrow><mi>C</mi><mspace></mspace><mi>P</mi></mrow></math></span> and <span><math><mrow><mi>D</mi><mspace></mspace><mi>P</mi></mrow></math></span>, are employed collaboratively. More specifically, in the first stage, the <span><math><mrow><mi>D</mi><mspace></mspace><mi>P</mi></mrow></math></span> evolves independently without considering constraints, focusing solely on convergence towards the unconstrained PF, while the <span><math><mrow><mi>C</mi><mspace></mspace><mi>P</mi></mrow></math></span> evolves with a weak collaboration with the <span><math><mrow><mi>D</mi><mspace></mspace><mi>P</mi></mrow></math></span>, driven by feasibility-based environment selection rules. In the second stage, the dual populations are both divided into multiple sub-populations within the M2M framework, generating offspring in a strongly collaborative manner to eventually converge to the constrained PF while ensuring solution diversity. Experimental results on two widely-used test suites fully demonstrate the superiority of DD-M2M compared to seven state-of-the-art methods on most test problems. Additionally, the proposed method is applied to real-world problems, and experimental results confirm its effectiveness in addressing practical challenges.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102030"},"PeriodicalIF":8.2,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322625","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":"Balancing objective and switch cost for robust optimization over time","authors":"Zhening Liu,&nbsp;Handing Wang","doi":"10.1016/j.swevo.2025.102022","DOIUrl":"10.1016/j.swevo.2025.102022","url":null,"abstract":"<div><div>Dynamic optimization problems (DOPs) commonly arise in real-world scenarios where objective functions and constraints change over time. While existing methods primarily focus on tracking optimal solutions across different environments, the cost of switching solutions cannot be overlooked in practical applications. Robust optimization over time (ROOT) addresses this challenge by simultaneously maximizing objective values and minimizing solution switch costs. This paper proposes a novel ROOT method, introducing a dynamic balancing mechanism that adjusts the search direction according to the correlation between objective value and switch cost. Additionally, a robust solution selection strategy is developed, utilizing switch cost as a constraint to pre-screen solutions before selecting the optimal robust solution. Comprehensive experiments on ROOT test problems of varying dimensions and switch cost weights validate the effectiveness of the proposed approach. Comparisons with ROOT algorithms, dynamic evolutionary algorithms, multi-objective evolutionary algorithms and single-objective evolutionary algorithms demonstrate that the proposed method achieves superior overall performance. Furthermore, ablation studies confirm the effectiveness of the balancing mechanism and the robust solution selection strategy in enhancing optimization quality.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102022"},"PeriodicalIF":8.2,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331058","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 knowledge transfer-based co-evolutionary algorithm for dynamic large-scale crude oil scheduling","authors":"Wanting Zhang,&nbsp;Wenli Du,&nbsp;Wei Du","doi":"10.1016/j.swevo.2025.102024","DOIUrl":"10.1016/j.swevo.2025.102024","url":null,"abstract":"<div><div>Confronted with the intricacies arising from expanding production scales and numerous uncertain events, large-scale dynamic scheduling has emerged as a practical approach in industries. However, within the framework of predictive–reactive scheduling, few works have explored the trade-off between the optimality and stability of the solutions generated. To fill this gap, this paper investigates dynamic scheduling applied to the large-scale crude oil scheduling problem, which is critical for the petroleum industry. Specifically, we develop a model that incorporates multiple sources of uncertainty: vessel arrival delays; tank malfunctions; fluctuations in feed flowrates to the distillation columns; and intermediate product demand. To solve this problem effectively, a knowledge transfer-based cooperative co-evolutionary algorithm (KT-CCEA) is proposed, where specific knowledge from the predictive stage is transferred to the reactive stage. Specifically, multiple subpopulations are generated around distinct reactive points, evolving across diverse search dimensions, to balance optimality and stability. Discretized differential operators are designed to overcome the limitations of standard evolutionary operators in integer-coded matrix representation. Empirical results over a set of 15 benchmark instances validate the superiority of the proposed KT-CCEA over four state-of-the-art algorithms (RSCO-SAGA, VLCEA, DMDE, and RCI-PSO). Ablation experiments on seven algorithm variants further confirm the efficacy of its core components.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102024"},"PeriodicalIF":8.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313795","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":"LABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regions","authors":"E. Visser,&nbsp;C.E. van Daalen,&nbsp;J.C. Schoeman","doi":"10.1016/j.swevo.2025.101986","DOIUrl":"10.1016/j.swevo.2025.101986","url":null,"abstract":"<div><div>Bayesian optimization (BO) is a popular method for optimizing expensive black-box functions. BO has several well-documented shortcomings, including computational slowdown with longer optimization runs, poor suitability for non-stationary or ill-conditioned objective functions, and poor convergence characteristics. Several algorithms have been proposed that incorporate local strategies, such as trust regions, into BO to mitigate these limitations; however, none address all of them satisfactorily. To address these shortcomings, we propose the LABCAT algorithm, which extends trust-region-based BO by adding a rotation aligning the trust region with the weighted principal components and an adaptive rescaling strategy based on the length-scales of a local Gaussian process surrogate model with automatic relevance determination. Through extensive numerical experiments using a set of synthetic test functions and the well-known COCO benchmarking software, we show that the LABCAT algorithm outperforms several state-of-the-art BO and other black-box optimization algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 101986"},"PeriodicalIF":8.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297892","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}