基于新型Memetic算法的多目标优化

为了更好地解决多目标优化问题，提出一种求解多目标优化问题的新型memetic算法。该算法利用微粒子群算法的全局搜索能力和同步启发式局部搜索相结合进行局部微
调；利用基于模糊全局极值的概念处理种群中过早出现收敛以及解多样性保持等问题。通过进一步检测得出新算法的特点并展示其在多目标优化问题上的独立性和综合效应。同时应用新型算法对IEEE14节点标准电网进行无功优化计算。结果证明，该新型memetic算法具有很好的寻优能力，验证了该算法的有效性及科学性。

Multi-Objective Optimization Based on Memetic Algorithm

In order to solve multi-objective optimization problems,a newmemetic algorithm is proposed,which combines the global search ability of particle swarm optimization with synchronous local search heuristic for directed local fine-tuning.A new particle updating strategy is presented to deal with the problem of premature convergence and diversity maintenance in the swarm based upon the concept of fuzzy global-best.The proposed new features are verified to show their individual and combined effect in multi-objective optimization.The reactive power optimization result of IEEE14 node system by the new memetic shows that it has a good astringency and efficiency.