An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

作     者：Zhang, Xuan-Yu Zhou, Kai-Qing Li, Peng-Cheng Xiang, Yin-Hong Zain, Azlan Mohd Sarkheyli-Hagele, Arezoo 

作者机构：Jishou Univ Sch Commun & Elect Engn Jishou 416000 Hunan Peoples R China Jishou Univ Lab Ethn Cultural Heritage Digitizat Wuling Mt Ar Jishou 416000 Hunan Peoples R China Univ Teknol Malaysia UTM Big Data Ctr Skudai 81310 Johor Malaysia Malmo Univ Internet Things & People Res Ctr Dept Comp Sci & Media Technol S-20506 Malmo Sweden 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2022年第10卷

页      面：96159-96179页

核心收录：

基　　金：National Natural Science Foundation of China Natural Science Foundation of Hunan Province, China [2020JJ5458] Fundamental Research Grant Scheme of Malaysia [R.J130000.7809.5F524] Jishou University Graduate Research and Innovation Project [JDY21067] 

主　　题：Statistics Sociology Optimization Chaos Standards Search problems Convergence Adaptive weighting modification cubic chaos mapping levy flight reverse learning sparrow search algorithm 

摘      要：Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optima easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, the values for both accuracy and convergence speed are higher than other algorithms. The results also indicate that the ICSSOA has an outstanding ability to jump out of the local optimum.