{"title":"Structural bias in metaheuristic algorithms: Insights, open problems, and future prospects","authors":"Kanchan Rajwar , Kusum Deep","doi":"10.1016/j.swevo.2024.101812","DOIUrl":null,"url":null,"abstract":"<div><div>This paper addresses a critical issue of structural bias in metaheuristic algorithms, a key factor that often hinders their effectiveness in solving complex optimization problems. Such biases, typically resulting from the design of algorithmic operators and solution construction processes, can lead to a decrease in performance over time. Despite its importance, structural bias is little understood and rarely explored. Moreover, the theoretical framework for structural bias in this context is notably underdeveloped. To the best of our knowledge, no comprehensive review of structural bias in metaheuristic algorithms is available to date. Consequently, this study is subjected to a thorough literature review, providing the mathematical definition of structural bias, the theoretical background, and an extensive analysis of its various forms within metaheuristic algorithms. This paper discusses structural bias in several metaheuristic algorithms, including the Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, and Ant Colony Optimization. Methodologies for identifying structural bias, currently scattered across several studies, are categorized into four classes and discussed through the implementation of Particle Swarm Optimization, highlighting their advantages and limitations. Additionally, five critical open problems are identified, and essential research directions for future exploration are outlined. As the first comprehensive review of structural bias – an issue gaining increasing attention – this work is expected to serve as a vital resource for algorithm designers and the research community.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"92 ","pages":"Article 101812"},"PeriodicalIF":8.2000,"publicationDate":"2025-02-01","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/S221065022400350X","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
This paper addresses a critical issue of structural bias in metaheuristic algorithms, a key factor that often hinders their effectiveness in solving complex optimization problems. Such biases, typically resulting from the design of algorithmic operators and solution construction processes, can lead to a decrease in performance over time. Despite its importance, structural bias is little understood and rarely explored. Moreover, the theoretical framework for structural bias in this context is notably underdeveloped. To the best of our knowledge, no comprehensive review of structural bias in metaheuristic algorithms is available to date. Consequently, this study is subjected to a thorough literature review, providing the mathematical definition of structural bias, the theoretical background, and an extensive analysis of its various forms within metaheuristic algorithms. This paper discusses structural bias in several metaheuristic algorithms, including the Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, and Ant Colony Optimization. Methodologies for identifying structural bias, currently scattered across several studies, are categorized into four classes and discussed through the implementation of Particle Swarm Optimization, highlighting their advantages and limitations. Additionally, five critical open problems are identified, and essential research directions for future exploration are outlined. As the first comprehensive review of structural bias – an issue gaining increasing attention – this work is expected to serve as a vital resource for algorithm designers and the research community.
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.