GA 与 PSO 解 DE 与 LP 问题的效率比较

摘要

Genetic algorithm and Particle Swarm Optimization algorithm with strong search capability have a very wide range of applications in the optimization problem. This paper focuses on approximate solutions of ordinary differential equations and LP solutions, based on genetic algorithm and particle swarm algorithms, a comparison and analysis of the efficiency of two kinds of optimization problems is made. We then fix other parameters but adjust the particle population, in the purpose to compare optimization capability of GA and PSO in approximate solutions of differential equation and the LP problem.%遗传算法和粒子群算法都具有很强的搜索能力，在最优化问题中有着极其广泛的应用。文章针对常微分方程(DE)近似解和一般线性规划(LP)问题的解利用遗传算法和粒子群算法求解，深入的比较和分析了 GA 与 PSO在这两种优化问题中的效率。在固定其他参数而调整群体数量的基础上比较了 GA 与 PSO 在微分方程近似解和LP 问题解的优化能力。