Evolutionary multimodal optimization using the principle of locality
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
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AbstractThe 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. © 2012 Elsevier Inc. All rights reserved.
SponsorsThe authors would like to express their deep gratitudes to the anonymous reviewers for their constructive comments. The authors would also like to thank Ling Qing for his source codes and insightful discussions. Last but not least, the authors would like to thank Kwong-Sak Leung and Man-Hon Wong for their contributions. ZZ acknowledges funding support from a NSERC Discovery Grant (RGPIN 327612-09).