Interval-based multi-objective metaheuristic honey badger algorithm

被引：0
作者：
Huang, Peixin [2 ]
Zhou, Guo [1 ]
Zhou, Yongquan [2 ,3 ]
Luo, Qifang [2 ,3 ]
机构：
[1] Department of Science and Technology Teaching, China University of Political Science and Law, Beijing
[2] College of Artificial Intelligence, Guangxi Minzu University, Nanning
[3] Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning
来源：
Soft Computing | 2024年 / 28卷 / 19期
基金：
中国国家自然科学基金;
关键词：
Honey badger algorithm; Interval based-metaheuristic; Interval parameter; Multi-objective optimization problem; Non-dominated sorting; Pareto dominance relation;
D O I：
10.1007/s00500-024-09893-8
中图分类号：
学科分类号：
摘要：
Optimization problem involving interval parameters and multiple conflicting objectives are called multi-objective optimization problems with interval parameters (IMOPs), which are common and hard to be solved effectively in practical applications. An interval multi-objective honey badger algorithm (IMOHBA) is proposed to address the IMOPs in this paper. Firstly, the μ metric is employed to assess the Pareto dominance relationship among interval individuals, which reflects the quality of the optimal solutions. Secondly, the crowding distance suitable for the interval objective is utilized to reflect the distribution of the optimal solution. Finally, the candidate solutions are ranked and selected by the non-dominated sorting method. To validate the performance of IMOHBA, it is tested on 19 benchmark IMOPs as well as an interval multi-objective scheduling problem for underwater wireless sensor networks and compared with three state-of-the-art algorithms. The experimental results demonstrate the superiority and strong competitiveness of IMOHBA in addressing IMOPs, exhibiting improved convergence and broader exploration capabilities of the solution space. These findings further validate the effectiveness and feasibility of IMOHBA, highlighting its unique advantage in solving IMOPs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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收藏
页码：11295 / 11322
页数：27
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