Incorporating mutation scheme into krill herd algorithm for global numerical optimization

• Published in: Neural computing & applications Vol. 24; no. 3-4; pp. 853 - 871
• Main Authors: Wang, Gaige, Guo, Lihong, Wang, Heqi, Duan, Hong, Liu, Luo, Li, Jiang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2014; Springer
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Exact sciences and technology; Image Processing and Computer Vision; Original Article; Probability and Statistics in Computer Science; Theoretical computing; Harmony search (HS); Multimodal function; Krill herd (KH); Global optimization problem; Composition; Euphausiacea; Evolutionary algorithm; Local optimum; Competitiveness; Updating; Global optimum; Optimization; Crustacea; Harmony search; Krill; Multimodality; Arthropoda; Heuristic method; Swarm intelligence; Robustness; Mutation; Invertebrata; Mathematical programming
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-012-1304-8

Recently, Gandomi and Alavi proposed a robust meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization. To improve the performance of the KH algorithm, harmony search (HS) is applied to mutate between krill during the process of krill updating instead of physical diffusion used in KH. A novel hybrid meta-heuristic optimization approach HS/KH is proposed to solve global numerical optimization problem. HS/KH combines the exploration of harmony search (HS) with the exploitation of KH effectively, and hence, it can generate the promising candidate solutions. The detailed implementation procedure for this improved meta-heuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most cases, the performance of this hybrid meta-heuristic method (HS/KH) is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, HS, KH, PSO, and SGA. The effect of the HS/FA parameters is also analyzed.