Inspired by the mechanism of small-world phenomenon and immune-genetic algorithm, a novel algorithm is presented in this paper. By introducing the long-range operator and short-range operator in the small-world effect, the individuals with smaller fitness values after crossover are searched locally, and the individuals with high density and large fitness values are searched globally. Compared with the genetic algorithm and immune-genetic algorithm, results of function optimization show that the proposed algorithm has obviously improved the optimization capacity, efficiency and stability. Additionally, the algorithm is applied to the path planning problem of mobile robot. According to the density of obstacles in environment, a new adaptive division method is designed. Simulation results in multiple environments indicate that the new algorithm is characterized by improved search speed and short planning path, which verifies the validity of adaptive division and optimization performance of the algorithm.
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