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{"title":"Ant Lion Optimizer Based on Spatial Information","authors":"Nian-Feng Weng, Yi Liu, Qi-Bin Zheng, Geng-Song Li, Hong-Mei Li","doi":"10.1002/tee.24165","DOIUrl":null,"url":null,"abstract":"<p>Optimization problems widely exist in different real-world applications. Ant lion optimizer (ALO) is an effective optimization algorithm that simulates the hunting behavior of antlions, but it will easily fall into local optima. To address this issue, this paper proposes a modified version of ALO called spatial ant lion optimizer (SALO). SALO makes full use of spatial informations of solutions to promote its searchability. It utilizes a distance-based indicator instead of objective fitness to choose ant lions for updating solutions. Besides, it maintains a candidate enhanced archive by a distance-based updating approach and implements a hybrid-enhanced strategy in the archive to search for better solutions. Exhaust experiments are carried out through six classic benchmark functions and seven state-of-the-art compared algorithms, and the results validate the superior performance of SALO. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"19 12","pages":"2081-2084"},"PeriodicalIF":1.0000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEJ Transactions on Electrical and Electronic Engineering","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/tee.24165","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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Abstract
Optimization problems widely exist in different real-world applications. Ant lion optimizer (ALO) is an effective optimization algorithm that simulates the hunting behavior of antlions, but it will easily fall into local optima. To address this issue, this paper proposes a modified version of ALO called spatial ant lion optimizer (SALO). SALO makes full use of spatial informations of solutions to promote its searchability. It utilizes a distance-based indicator instead of objective fitness to choose ant lions for updating solutions. Besides, it maintains a candidate enhanced archive by a distance-based updating approach and implements a hybrid-enhanced strategy in the archive to search for better solutions. Exhaust experiments are carried out through six classic benchmark functions and seven state-of-the-art compared algorithms, and the results validate the superior performance of SALO. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
基于空间信息的蚁狮优化器
优化问题广泛存在于不同的实际应用中。蚁狮优化器（ALO）是一种模拟蚁狮狩猎行为的有效优化算法，但它很容易陷入局部最优。针对这一问题，本文提出了一种名为空间蚁狮优化器（SALO）的 ALO 改进版。SALO 充分利用解的空间信息来提高其可搜索性。它利用基于距离的指标而不是客观适配度来选择蚁狮更新解决方案。此外，它还通过基于距离的更新方法来维护一个候选增强档案库，并在档案库中实施混合增强策略来搜索更好的解决方案。通过六种经典基准函数和七种最先进的比较算法进行了详尽的实验，结果验证了 SALO 的优越性能。© 2024 日本电气工程师学会和 Wiley Periodicals LLC。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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