An Adaptive Cultural Algorithm Based on Dynamic Particle Swarm Optimization
To avoid the local optimum problems and to improve convergent speed when particle swarm optimization algorithm in solving complex problems, an adaptive cultural algorithm based on dynamic particle swarm optimization algorithm was proposed. Particle swarm algorithm introduced evaluation premature convergence degree of index to judge the population space condition to determine the role of the influence function time. The inertia weight of the particle was adjusted adaptively based on the premature convergence degree of the swarm. The diversity of inertia weight makes a compromise between the global convergence and the speed of convergence. The proposed algorithm was tested with four well-known benchmark functions. The experimental results show that the new algorithm has great global search ability convergence accuracy and convergence velocity is also increased and avoid the premature convergence problem effectively.
KeywordsAdaptive Particle swarm Cultural algorithm Inertia weight Influence function
This research was supported by the National Natural Science Foundation of China (No.60873247), the Natural Science Foundation of Shandong Province of China (No. ZR2009GZ007, ZR2011FM030), and National Social Science foundation of China (12BXW040).
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