Enhanced QPSO driven by swarm cooperative evolution and its applications in portfolio optimization

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-02-07 DOI:10.1016/j.swevo.2025.101872

Xiao-li Lu , Guang He

{"title":"Enhanced QPSO driven by swarm cooperative evolution and its applications in portfolio optimization","authors":"Xiao-li Lu , Guang He","doi":"10.1016/j.swevo.2025.101872","DOIUrl":null,"url":null,"abstract":"<div><div>Being a simple and popular method grounded in swarm evolution, Quantum-behaved particle swarm optimization (QPSO) has been extensively implemented to seek the optimal solution of various practical cases. Nevertheless, while managing intricate multimodal problems, the original QPSO algorithm renders the algorithm susceptible to premature convergence, characterized by slow iteration speed and suboptimal searching precision. To deal with these disadvantages, this paper puts forward an enhanced QPSO driven by swarm cooperative evolution (SCQPSO). In the SCQPSO algorithm, a binary swarm cooperative evolution strategy is designed to enhance QPSO’s convergence speed and optimization precision. Additionally, some improvement measures including Halton sequence initialization of individual locations, maintenance of population diversity, and mutation strategy for out-of-bounds particles, are also adopted to facilitate prevention of premature convergence and assist the algorithm in overcoming local optimality. Then, compared results obtained by SCQPSO and six improved intelligent approaches on CEC 2017 cases indicate that SCQPSO offers highly competitive solutions when solving complex multimodal problems. Further, the exceptional capability of SCQPSO in addressing two portfolio optimization issues demonstrates its outstanding global search performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101872"},"PeriodicalIF":8.2000,"publicationDate":"2025-02-07","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/S2210650225000306","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}

引用次数: 0

Abstract

Being a simple and popular method grounded in swarm evolution, Quantum-behaved particle swarm optimization (QPSO) has been extensively implemented to seek the optimal solution of various practical cases. Nevertheless, while managing intricate multimodal problems, the original QPSO algorithm renders the algorithm susceptible to premature convergence, characterized by slow iteration speed and suboptimal searching precision. To deal with these disadvantages, this paper puts forward an enhanced QPSO driven by swarm cooperative evolution (SCQPSO). In the SCQPSO algorithm, a binary swarm cooperative evolution strategy is designed to enhance QPSO’s convergence speed and optimization precision. Additionally, some improvement measures including Halton sequence initialization of individual locations, maintenance of population diversity, and mutation strategy for out-of-bounds particles, are also adopted to facilitate prevention of premature convergence and assist the algorithm in overcoming local optimality. Then, compared results obtained by SCQPSO and six improved intelligent approaches on CEC 2017 cases indicate that SCQPSO offers highly competitive solutions when solving complex multimodal problems. Further, the exceptional capability of SCQPSO in addressing two portfolio optimization issues demonstrates its outstanding global search performance.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

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.