元启发式算法中的结构性偏差:见解、开放问题和未来展望

IF 8.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kanchan Rajwar , Kusum Deep
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引用次数: 0

摘要

本文解决了元启发式算法中的一个关键问题——结构偏差,这是一个经常阻碍其有效解决复杂优化问题的关键因素。这种偏差通常是由算法运算符的设计和解决方案构建过程造成的,随着时间的推移可能导致性能下降。尽管它很重要,但人们对结构性偏见知之甚少,也很少探索。此外,在这种情况下,结构性偏见的理论框架明显不发达。据我们所知,到目前为止,还没有对元启发式算法中的结构偏差进行全面的审查。因此,本研究将进行全面的文献综述,提供结构偏差的数学定义,理论背景,并广泛分析其在元启发式算法中的各种形式。本文讨论了几种元启发式算法中的结构偏差,包括遗传算法、粒子群优化、差分进化和蚁群优化。识别结构偏差的方法,目前分散在几个研究中,分为四类,并通过粒子群优化的实施进行讨论,突出了它们的优点和局限性。此外,还确定了五个关键的开放性问题,并概述了未来探索的重要研究方向。作为对结构偏差的第一次全面审查-一个越来越受到关注的问题-这项工作有望成为算法设计者和研究界的重要资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural bias in metaheuristic algorithms: Insights, open problems, and future prospects
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.
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来源期刊
Swarm and Evolutionary Computation
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.
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