混沌模拟退火粒子群优化算法研究及应用

针对粒子群优化算法容易陷入局部极值点、进化后期收敛速度慢、精度较差等缺点,提出混沌模拟退火粒子群优化(PSO)算法.引入混沌理论对粒子群优化算法的参数进行自适应调整,提高了算法的全局收敛性能|采用模拟退火(SA)算法,依据概率性的劣向转移,以一定概率接受劣解,使算法具有跳出局部最优而实现全局最优的能力.引入自适应温度衰变系数,使模拟退火算法能够根据当前环境自动调整搜索条件,从而提高算法的搜索效率.通过7个经典函数测试混沌模拟退火粒子群优化算法的性能,并将其应用于Job Shop调度问题.仿真实验结果表明,采用新算法有效地克服了停滞现象,增强了全局搜索能力,与遗传算法、粒子群优化算法相比寻优性能更佳.

Chaotic simulated annealing particle swarm optimization algorithm research and its application

A chaotic simulated annealing particle swarm algorithm was proposed to deal with the deficiencies of particle swarm optimization (PSO) algorithm, such as easily being lost in local optimum, the slow evolutionary convergence speed and poor search accuracy and so on. The chaos theory was introduced to adjust the parameters of PSO algorithm adaptively, which improved the global convergence property. The simulated annealing(SA) algorithm, accepting inferior solutions at a certain probability based on probabilistic interior transfer, was adopted to make the algorithm has capability to jump out of local optimization and achieve global optimization. The adaptive temperature decay factor was introduced to make the SA algorithm adjust the search conditions automatically based on the current environmental conditions. Then the search efficiency of the algorithm was improved. The property of chaotic simulated annealing particle swarm algorithm was tested by seven classic functions and it was applied in job shop scheduling. Simulation results demonstrated that the stagnation was effectively overcome and the global search capability was enhanced through the proposed algorithm whose performance of global searching was superior to genetic algorithms and particle swarm optimization algorithms.