Hybrid Archimedes optimization algorithm enhanced with mutualism scheme for global optimization problems

作     者：Varol Altay, Elif 

作者机构：Manisa Celal Bayar Univ Dept Software Engn Manisa Turkey 

出 版 物：《ARTIFICIAL INTELLIGENCE REVIEW》 (Artif Intell Rev)

年 卷 期：2023年第56卷第7期

页      面：6885-6946页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Archimedes optimization algorithm Symbiosis organisms search Mutualism scheme Metaheuristics Hybrid method Global optimization problems 

摘      要：Archimedes optimization algorithm (AOA) is a recent metaheuristic method inspired by the Archimedes principle, which is the law of physics. Like other metaheuristic methods, it suffers from the disadvantages of being stuck in local areas, suffering from weak exploitation abilities, and an inability to maintain a balance between exploration and exploitation. To overcome these weaknesses, a new hybrid Mutualism Archimedes Optimization Algorithm (MAOA) method has been proposed by combining the AOA and the mutation phase in the Symbiosis organism search (SOS) method. SOS algorithm is known for its exploitation ability. With the mutation phase, it has been used to improve local search for swarm agents, help prevent premature convergence and increase population diversity. To verify the applicability and performance of the proposed algorithm, extensive analysis of standard benchmark functions, CEC 17 test suites, and engineering design problems were performed. The proposed method is compared with the recently emerged and popular AOA, SOS, Harris Hawks Optimization (HHO), COOT Optimization Algorithm (COOT), Aquila Optimizer (AO), Salp Swarm Algorithm (SSA), and Multi-Verse Optimization (MVO) methods, and statistical analyses were performed. The results obtained from the experiments show that the proposed MAOA method has superior global search performance and faster convergence speed compared to AOA, SOS, and other recently emerged and popular metaheuristic methods. Furthermore, this study compares MAOA to five well-established and recent algorithms constructed using various metaheuristic methodologies utilizing nine benchmark datasets to assess the general competence of MAOA in feature selection. Therefore, the proposed method is considered to be a promising optimization method for real-world engineering design problems, global optimization problems, and feature selection.