Abstract: A new Individual Advantages Genetic Algorithm （IAGA） based on elitist strategy is proposed to deal with the problems of poor local search ability, premature convergence and slow convergence speed. In the IAGA, the amount and diversity of elite individual are increased with the regeneration of elite subpopulation. While maintaining global search convergence, it enhances the ability of searching the local area near the optimal solution. Firstly, this paper introduces a Semi-Particle Swarm Mutation Operator （SPSMO） into the genetic algorithm to improve the speed of reaching the neighborhood of the optimal solution in prophase. Then, the Individual Advantages Operator （IAO） is introduced to improve the diversity of advantageous individual and the issues of slow convergence speed in anaphase. Compared with existing similar algorithms, IAGA has balanced the contradiction between convergence speed and global convergence and further improved the speed of convergence and precision.

Key words: genetic algorithm, elitist strategy, individual advantage, local search

摘要： 针对遗传算法存在的局部搜索能力差、早熟收敛和进化后期收敛速度慢的问题，提出了一种改进精英策略的个体优势遗传算法（Individual Advantages Genetic Algorithm，IAGA）。IAGA通过在精英子种群更新中不断增加精英个体数量和多样性，在保持算法全局收敛性的同时，增强算法在最优解区域的局部搜索能力。引入半粒子群变异算子，提高了算法前期向全局最优解靠拢的速度;引入个体优势算子，提高种群优势个体的多样性，有效改善了进化后期收敛速度慢的问题；与已有同类算法相比，平衡了收敛速度和全局收敛性之间矛盾的同时，进一步提高了收敛速度和精度。

关键词: 遗传算法, 精英策略, 个体优势, 局部搜索