基于多指标的人工蜂群算法,用于具有不规则帕累托前沿的多目标优化

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hui Wang , Dong Xiao , Shahryar Rahnamayan , Wei Li , Jia Zhao
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引用次数: 0

摘要

人工蜂群(ABC)算法在许多单目标和多目标优化问题(MOPs)中表现出了卓越的性能。然而,在解决具有不规则帕累托前沿(PFs)的多目标优化问题(MaOPs)时,人工蜂群算法遇到了一些困难。可能的原因包括两个方面:(1)种群中有许多非主导解,低选择压力无法使种群向帕累托前沿移动;(2)对于具有不规则几何结构的帕累托前沿,很难保持种群的多样性。针对这些问题,本文提出了一种新的基于多指标的多目标 ABC 变体(称为 MIMaOABC)。首先,利用收敛指标 Iɛ+ 和基于平行距离的多样性指标 (Div)。单一指标可能具有偏好性,容易使种群收敛到 PF 的一个子区域。然后,根据这两个指标设计了一种两阶段环境选择方法。在第一阶段,使用基于 Iɛ+ 的环境选择来提高收敛性。在第二阶段,采用基于 Div 的环境选择来保持多样性和处理不规则的 PF。为了平衡搜索过程中的探索和利用,在不同的搜索阶段分别采用了多种搜索策略。在观望蜂阶段,基于新的选择机制,选择收敛性好的解决方案进行进一步搜索。为了验证 MIMaOABC 的性能,测试了一组具有退化、不连续、倒置和规则 PF 的著名基准问题。MIMaOABC 的性能与八种最先进的算法进行了比较。计算结果表明,所提出的 MIMaOABC 在解决具有不规则和规则 PF 的 MaOPs 时都具有竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial bee colony algorithm based on multiple indicators for many-objective optimization with irregular Pareto fronts
Artificial bee colony (ABC) algorithm has shown excellent performance over many single and multi-objective optimization problems (MOPs). However, ABC encounters some difficulties when solving many-objective optimization problems (MaOPs) with irregular Pareto fronts (PFs). The possible reasons include two aspects: (1) there are many non-dominated solutions in the population and the low selection pressure cannot move the population toward the PF; and (2) it is hard to maintain population diversity for PFs having irregular geometric structures. To address these issues, a new many-objective ABC variant based on multiple indicators (called MIMaOABC) is proposed in this paper. Firstly, a convergence indicator Iɛ+ and a diversity indicator (Div) based on parallel distance are utilized. A single indicator may have preferences and it easily causes the population to converge to a subregion of the PF. Then, a two-stage environmental selection method is designed based on the two indicators. In the first stage, the Iɛ+ based environmental selection is used to improve the convergence. In the second stage, the Div based environmental selection is employed to maintain diversity and handle irregular PFs. To balance exploration and exploitation during the search, multiple search strategies are used in different search stages, respectively. In the onlooker bee stage, solutions with good convergence are chosen for further search based on a new selection mechanism. In order to verify the performance of MIMaOABC, a set of well-known benchmark problems with degenerate, discontinuous, inverted, and regular PFs are tested. Performance of MIMaOABC is compared with eight state-of-the-art algorithms. Computational results shows that the proposed MIMaOABC is competitive in solving MaOPs with both irregular and regular PFs.
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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