An Improved Shuffled Frog Leaping Algorithm with Comprehensive Learning for Continuous Optimization
This paper presents a shuffled frog leaping algorithm (SFLA) with comprehensive learning strategy (SFLA-CL) for global optimization.This algorithm uses a novel learning strategy whereby all other frogs information of the memplex is used to update the worst frogs position.The strategy enables the diversity of the memplex to be preserved to discourage premature convergence.SFLA-CL also introduces a new search learning coefficient into the formulation of the original SFLA to enhance the convergence performance of SFLA.SFLA-CL has been evaluated,in comparison with existing evolutionary algorithm,such as SFLA,particle swarm optimization (PSO) and fast evolutionary programming (FEP),on five mathematical benchmark functions.Experimental results demonstrate that the SFLA-CL performs much better than SFLA,PSO,and FEP in optimizing these benchmark functions,particularly,in terms of its convergence rates and robustness.
Evolutionary compution Shuffled forg leaping algorithm Comprehensive learning strategy Particle swarm optimization Continuous Optimization
Liping Xue Yinglong Yao Hong Zhou Zhiqiang Wang
College of Computer Science and Software Engineering,Shenzhen University Shenzhen,518060,China
国际会议
杭州
英文
758-761
2013-03-22（万方平台首次上网日期，不代表论文的发表时间）