An Improved Cuckoo Search Algorithm with Adaptive Method
CSO（2014）
摘要
To improve the refining ability and convergence rate of cuckoo search algorithm for finding optimal solution. An improved cuckoo search algorithm with adaptive method is proposed. The self-adaptive machine is used to control the scaling factor and find probability so as to improve population diversity and avoid premature, as a result, more individuals participating in the evolution, and then refining ability and convergence rate are improved. The result of experiment show the ICS algorithm has better performance when lots of test functions are considered, ICS algorithm has faster convergence speed and higher precision.
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关键词
refining ability,cuckoo search algorithm,adaptive method,optimal solution,self-adaptive machine,convergence rate,search problems,convergence of numerical methods,cuckoo search algorithm, self-adaptive machine, refining ability, convergence rate, optimal solution,ics algorithm,scaling factor,probability,refining,algorithm design and analysis,convergence,particle swarm optimization,optimization
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