一种改进的差分进化算法及其应用

An improved differential evolution algorithm and its application

多目标优化问题(MOP)存在范围广且人工求解难度大,通过差分进化算法(DE)解决MOP问题具有重要意义.由于常用DE算法性能有限、收敛速度、计算精度和优化能力相互制约,通过改善变异因子、进化机制以及与粒子群算法融合等措施,研究一类基于粒子群优化和DE的混合算法(PSODE),经典优化函数的仿真实验和对比分析,结果表明在高维复杂寻优问题中可以求得高精度解.在实际数字滤波器优化设计中,表明其改进算法在计算精度和运行速度上均能取得满意的应用效果.

Multi-objective Optimization Problem（MOP） is very common but difficult to be solved, so it has important significance to solve MOP using Differential Evolution（DE） algorithm. To overcome the shortcomings in DE algorithm,such as the limited performance, the mutual restriction among convergent rate, computational accuracy and optimization ability, a hybrid approach to particle swarm optimization（PSO） and DE algorithm is presented by improving mutagenic factor, evolutionism and mixing PSO algorithm. Simulation experiment and comparative analysis on classical testing functions show that the presented improved approach can get the high accuracy solution in high dimensional complex optimization problems. In the actual design on digital filter by improved approach, the application effect can be obtained satisfactory at computational accuracy and operation rate.