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{"title":"Piecewise Logarithmic Fitting-Based Ant Lion Optimizer","authors":"Cheng-ze Li, Geng-song Li, Yi Liu, Qi-bin Zheng","doi":"10.1002/tee.24275","DOIUrl":null,"url":null,"abstract":"<p>The ant lion optimizer (ALO) can address optimization problems by searching for approximate solutions. However, current implementations of ALO are prone to becoming trapped in local optima and exhibit mediocre random search capabilities. To address these issues, this paper introduces the piecewise logarithmic fitting-based ant lion optimizer (PLFALO). PLFALO initializes the population with Logistic mapping to increase the population diversity, then refines the original contraction of the ants' random walk boundary into a smoother process thereby enhancing random search capability, and finally mutates the ant position with a golden sine strategy to ensure the diversity of solutions. Exhaustive experiments have been conducted using six classic benchmark functions, comparing PLFALO against six state-of-the-art algorithms. The results confirm the superior performance of PLFALO. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 8","pages":"1294-1297"},"PeriodicalIF":1.1000,"publicationDate":"2025-01-31","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.24275","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
The ant lion optimizer (ALO) can address optimization problems by searching for approximate solutions. However, current implementations of ALO are prone to becoming trapped in local optima and exhibit mediocre random search capabilities. To address these issues, this paper introduces the piecewise logarithmic fitting-based ant lion optimizer (PLFALO). PLFALO initializes the population with Logistic mapping to increase the population diversity, then refines the original contraction of the ants' random walk boundary into a smoother process thereby enhancing random search capability, and finally mutates the ant position with a golden sine strategy to ensure the diversity of solutions. Exhaustive experiments have been conducted using six classic benchmark functions, comparing PLFALO against six state-of-the-art algorithms. The results confirm the superior performance of PLFALO. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
基于分段对数拟合的蚁狮优化器
蚁狮优化器（ALO）可以通过搜索近似解来解决优化问题。然而，当前的ALO实现容易陷入局部最优，并且表现出平庸的随机搜索功能。为了解决这些问题，本文引入了基于分段对数拟合的蚁狮优化器（PLFALO）。PLFALO算法通过Logistic映射对蚁群进行初始化，增加蚁群的多样性，然后将蚁群随机游走边界的原始收缩细化为更平滑的过程，从而增强蚁群的随机搜索能力，最后采用金正弦策略对蚁群位置进行变异，保证解的多样性。使用六个经典基准函数进行了详尽的实验，将PLFALO与六种最先进的算法进行了比较。结果证实了PLFALO的优越性能。©2025日本电气工程师协会和Wiley期刊有限责任公司。
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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