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.