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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