Spherical search algorithm with memory-guided population stage-wise control for bound-constrained global optimization problems

• Published in: Applied soft computing Vol. 161; p. 111677
• Main Authors: Tao, Sichen, Wang, Kaiyu, Jin, Ting, Wu, Zhengwei, Lei, Zhenyu, Gao, Shangce
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2024
• Subjects: Adaptive population control; Evolutionary computation; Exploration and exploitation; Memory-based strategy; Spherical search; Exploration and exploitation; Spherical search; Adaptive population control; Memory-based strategy; Evolutionary computation
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2024.111677

The recently proposed Spherical Search (SS) algorithm replaces the traditional square search pattern with a spherical boundary to provide position-diverse solutions. The algorithm balances its exploration and exploitation performance by utilizing 2 exploration and exploitation sub-populations of equal size. SS has been proven to be highly competitive. However, we observed that when it is used to solve a variety of problems as well as during different searching stages, the fixed sub-population size limits its adaptability and flexibility for achieving continuous exploitation–exploration balance. The balance potential of two operators with distinct characteristics is underdeveloped. As a result, SS and its advanced variants are prone to still easily falling into local optima and lacks certain performance advantages over peer algorithms. In this paper, we further develop SS and propose a memory-guided population stage-wise control strategy based SS, called SSM. By our proposed memory-guided stage-wise evaluation mechanism, SS evaluates the exploitation–exploration balance extent in real time and thus adaptively optimizes and predicts better resource allocation ratio values between its 2 sub-populations and thus achieves significant performance advantages over peer algorithms. The experiments are conducted on 120 benchmark functions and 22 real-world problems, and the results show that SSM significantly outperforms other 13 state-of-the-art evolutionary algorithms. Additionally, we conduct analyses based on method characteristics, convergence process, solution quality robustness testing, population diversity, exploitation and exploration balance, and computational complexity. •We propose SSM for solving bound-constrained global optimization problems.•We propose a novel memory-guided stage-wise resource allocation strategy.•The experimental results show that SSM significantly outperforms its peers.•Several related analyses are conducted.