Evolutionary multimodal optimization using the principle of locality

• Published in: Information sciences Vol. 194; pp. 138 - 170
• Main Authors: Wong, Ka-Chun, Wu, Chun-Ho, Mok, Ricky K.P., Peng, Chengbin, Zhang, Zhaolei
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2012
• Subjects: Crowding differential evolution; Evolutionary Computation; Multimodal optimization; Niching; Optimization; Spatial locality; Temporal locality; Niching; Spatial locality; Crowding differential evolution; Temporal locality; Evolutionary Computation; Multimodal optimization; Optimization
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2011.12.016

The principle of locality is one of the most widely used concepts in designing computing systems. To explore the principle in evolutionary computation, crowding differential evolution is incorporated with locality for multimodal optimization. Instead of generating trial vectors randomly, the first method proposed takes advantage of spatial locality to generate trial vectors. Temporal locality is also adopted to help generate offspring in the second method proposed. Temporal and spatial locality are then applied together in the third method proposed. Numerical experiments are conducted to compare the proposed methods with the state-of-the-art methods on benchmark functions. Experimental analysis is undertaken to observe the effect of locality and the synergy between temporal locality and spatial locality. Further experiments are also conducted on two application problems. One is the varied-line-spacing holographic grating design problem, while the other is the protein structure prediction problem. The numerical results demonstrate the effectiveness of the methods proposed.