A Modified Group Search Optimizer Algorithm for High Dimensional Function Optimization

This paper presents a modified group search optimizer algorithm for high dimensional function optimization, which is based on levy flight strategy, self-adaptive joining strategy, and chaotic mutation strategy. The levy flight strategy is employed for the producer to simplify the computation and improve efficiency in the exploring space. The self-adaptive joining strategy is used for the scroungers walking towards the producer to promote convergence speed. The chaotic mutation strategy is designed for the rangers to strengthen diversification. Using those strategies, the modified algorithm can get better balance between intensification and diversification. The simulation experiments, which were carried on benchmark functions, show that those strategies are effective, and they improve the global optimization ability and convergence speed of modified group search optimizer for high dimensional function optimization.

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