基于混沌多目标遗传算法的分布式电源规划

针对多目标遗传算法(multi-objective genetic algorithm,MGA)在解决分布式电源(dis-tributed generation,DG)优化问题上存在的不足,加入混沌变量、虛拟适应度、精英保留策略等方法进行多目标改进,提出一种改进混沌多目标遗传算法(improved chaotic optimization multi-ob-jective genetic algorithm,ICMGA),并依据种群进化状态自适应调整搜索精度,提高了算法搜索效率和收敛速度。结果表明:与NSGA_II算法相比,ICMGA算法不但寻优能力更强,收敛速度快,还具有良好的经济性。能够为分布式电源优化问题提供优良的解决方案。

Distributed generation planning based on the chaotic multi- -objective genetic algorithm

Aiming at the weakness of multi- objective genetic algorithm solving the problem ofdistributed generation optimization, by adding the methods of Chaotic variables, virtual fitness, elitereserved strategy, etc. improves the algorithm, puts forward an improved chaotic optimization multiobjective genetic algorithm. According to the population evolution self adaptability changes the searchstep to improve the searching efficiency and convergence speed of the algorithm. The result shows thatcompared with the NSGA_ II method, the ICMGA algorithm has stronger optimization capability, fasterconvergence speed, and better economy. It can effectively solve the multi objective optimizationproblem of distributed generation.