模拟退火猫群优化算法

猫群优化算法(Cat Swarm Optimization,CSO)是建立在猫的行为模式和群体智能基础上的一种新型群体智能优化算法。为提高猫群优化算法的性能,把模拟退火算法应用于猫群优化算法,提出模拟退火猫群优化算法(Simulated Annealing Cat Swarm Optimization,SACSO),通过变异算子调整所要优化的种群。其基本过程为先行产生随机初始种群,接着进行搜索,并设置初始温度,继而应用模拟退火算法获取全局最优替代值,再依据位置和速度公式更新新解,然后在个体较优位置再运用变异运算,进行进一步地搜索。然后分别将猫群优化算法、模拟退火粒子群算法(Simulated Annealing Particle Swarm Optimization,SAPSO)、模拟退火猫群优化算法在11个典型的基准测试函数下进行仿真对比,结果表明模拟退火猫群优化算法不仅增加了全局收敛性,而且在收敛速度和精度方面均优于其它两种算法。

Simulated Annealing Cat Swarm Optimization

Cat swarm optimization(CSO)is a new swarm intelligence optimization algorithm based on cat’s behavior pattern and swarm intelligence.In order to improve the performance of the cat swarm optimization algorithm,the simulated annealing cat swarm optimization(SACSO)algorithm is proposed by applying the simulated annealing algorithm to the cat swarm optimization algorithm,the mutation operator is used to adjust the population to be optimized.The basic process is to generate a random initial population first,then search,and set the initial temperature,then use the simulated annealing algorithm to obtain the global optimal alternative value,then update the new solution according to the location and speed formula,and then use the mutation operation in the individual better location to further search.Then the cat swarm optimization algorithm,simulated annealing particle swarm optimization(SAPSO)algorithm and simulated annealing cat swarm optimization algorithm are simulated and compared under 11 typical benchmark functions.The results show that the simulated annealing cat swarm optimization algorithm not only increases the global convergence,but also has better convergence speed and accuracy than the other two algorithms.