Haofan Wang , Li Chen , Xingxing Hao , Rong Qu , Wei Zhou , Dekui Wang , Wei Liu
{"title":"大规模进化多目标优化的学习引导交叉采样","authors":"Haofan Wang , Li Chen , Xingxing Hao , Rong Qu , Wei Zhou , Dekui Wang , Wei Liu","doi":"10.1016/j.swevo.2024.101763","DOIUrl":null,"url":null,"abstract":"<div><div>When tackling large-scale multi-objective problems (LSMOPs), the computational budget could be wasted by traditional offspring generators that explore the search space in a nearly directionless manner, impairing the efficiency of many existing algorithms. To address this issue, this paper proposes a novel two-level large-scale multi-objective evolutionary algorithm called LMOEA-LGCS that incorporates neural network (NN) learning-guided cross-sampling for offspring generation in the first level and a layered competitive swarm optimizer in the second level. Specifically, in the first level, two NNs are trained online to learn promising vertical and horizontal search directions, respectively, against the Pareto Set, and then a batch of candidate solutions are sampled on the learned directions. The merit of learning two explicit search directions is to devote the employed NNs to concentrating on separate or even conflicting targets, i.e., the convergence and diversity of the population, thus achieving a good trade-off between them. In this way, the algorithm can thus explore adaptively towards more promising search directions that have the potential to facilitate the convergence of the population while maintaining a good diversity. In the second level, the layered competitive swarm optimizer is employed to perform a deeper optimization of the solutions generated in the first level across the entire search space to increase their diversity further. Comparisons with six state-of-the-art algorithms on three LSMOP benchmarks, i.e., the LSMOP, UF, and IMF, with 2-12 objectives and 500-8000 decision variables, and the real-world problem TREE demonstrate the advantages of the proposed algorithm.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning-guided cross-sampling for large-scale evolutionary multi-objective optimization\",\"authors\":\"Haofan Wang , Li Chen , Xingxing Hao , Rong Qu , Wei Zhou , Dekui Wang , Wei Liu\",\"doi\":\"10.1016/j.swevo.2024.101763\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>When tackling large-scale multi-objective problems (LSMOPs), the computational budget could be wasted by traditional offspring generators that explore the search space in a nearly directionless manner, impairing the efficiency of many existing algorithms. To address this issue, this paper proposes a novel two-level large-scale multi-objective evolutionary algorithm called LMOEA-LGCS that incorporates neural network (NN) learning-guided cross-sampling for offspring generation in the first level and a layered competitive swarm optimizer in the second level. Specifically, in the first level, two NNs are trained online to learn promising vertical and horizontal search directions, respectively, against the Pareto Set, and then a batch of candidate solutions are sampled on the learned directions. The merit of learning two explicit search directions is to devote the employed NNs to concentrating on separate or even conflicting targets, i.e., the convergence and diversity of the population, thus achieving a good trade-off between them. In this way, the algorithm can thus explore adaptively towards more promising search directions that have the potential to facilitate the convergence of the population while maintaining a good diversity. In the second level, the layered competitive swarm optimizer is employed to perform a deeper optimization of the solutions generated in the first level across the entire search space to increase their diversity further. Comparisons with six state-of-the-art algorithms on three LSMOP benchmarks, i.e., the LSMOP, UF, and IMF, with 2-12 objectives and 500-8000 decision variables, and the real-world problem TREE demonstrate the advantages of the proposed algorithm.</div></div>\",\"PeriodicalId\":48682,\"journal\":{\"name\":\"Swarm and Evolutionary Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2024-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Swarm and Evolutionary Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210650224003018\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650224003018","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Learning-guided cross-sampling for large-scale evolutionary multi-objective optimization
When tackling large-scale multi-objective problems (LSMOPs), the computational budget could be wasted by traditional offspring generators that explore the search space in a nearly directionless manner, impairing the efficiency of many existing algorithms. To address this issue, this paper proposes a novel two-level large-scale multi-objective evolutionary algorithm called LMOEA-LGCS that incorporates neural network (NN) learning-guided cross-sampling for offspring generation in the first level and a layered competitive swarm optimizer in the second level. Specifically, in the first level, two NNs are trained online to learn promising vertical and horizontal search directions, respectively, against the Pareto Set, and then a batch of candidate solutions are sampled on the learned directions. The merit of learning two explicit search directions is to devote the employed NNs to concentrating on separate or even conflicting targets, i.e., the convergence and diversity of the population, thus achieving a good trade-off between them. In this way, the algorithm can thus explore adaptively towards more promising search directions that have the potential to facilitate the convergence of the population while maintaining a good diversity. In the second level, the layered competitive swarm optimizer is employed to perform a deeper optimization of the solutions generated in the first level across the entire search space to increase their diversity further. Comparisons with six state-of-the-art algorithms on three LSMOP benchmarks, i.e., the LSMOP, UF, and IMF, with 2-12 objectives and 500-8000 decision variables, and the real-world problem TREE demonstrate the advantages of the proposed algorithm.
期刊介绍:
Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.