A Novel Differential Evolution Algorithm Based On Simulated Annealing
Differential Evolution (DE) which has been focused on computation intelligence is a new swarm intelligent algorithm by simulating intelligence of population after GA and PSO etc. It is more robust and efficient. Because the differential degree of individuals is minimized in the last, the diversity of population will be reduced and DE will converge ahead of schedule. It is well known that simulated annealing(SA) can accept both better solution and worse solution according to definite probability. This mechanism can maintain the diversity of the population so that can avoid appearing premature convergence. This paper proposes a novel hybrid DE (DESA) by combining original DE algorithm and simulated annealing strategy. At last, it is proved that the DESA algorithm is effective in solving global optimization problem by testing on five Benchmark functions.
Differential Evolution Simulated Annealing Hybrid Global Convergence
PeiChong Wang Xu Qian Yu Zhou Ning Li
School of Information Engineering, China University of Mining & Technology (Beijing), Beijing, China School of Information Engineering, China University of Mining & Technology (Beijing), Beijing, China Department of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang , China ,
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
7-10
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）