改进遗传算法与粒子群优化算法及其对比分析

进化算法作为一类新的优化搜索方法，广泛应用于各种优化问题．现对简单遗传算法进行了改进，采用实值编码，并与模拟退火算法及基于适值排序和随机选择的方法相结合，形成了改进遗传算法．同时还介绍了一种新的进化算法一粒子群优化算法．将这两种优化算法应用于函数优化，并对优化结果进行了对比分析．比较结果表明，改进遗传算法和粒子群优化算法都可以在函数优化方面表现出较好的健壮性，但在找寻最优解的效率上，粒子群优化算法较好．

Improved Genetic Algorithm and Particle Swarm Optimization as well as Comparison between Them

As a new kind of optimization search techniques, the evolutionary algorithms are widely used to solve different problems in optimal areas. After a careful research, the simple genetic algorithm has been improved by adopting float coding method, simulated annealing algorithm and sorted stochastic fitness selection strategy; and has been applied to mathematic function optimization. In addition, a new evolutionary algorithm Particle Swarm Optimization is introduced and applied to the same mathematic function optimization.The optimization results are compared with each other in this paper.The comparative result indicates that the Improved Genetic Algorithm and Particle Swarm Optimization are both robust.But Particle Swarm Optimization can obtain the optimum solutions more easily than the Improved Genetic Algorithm,and it is a good optimization method with strong competitiveness.