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Vehicle Scheduling Algorithm based on Improved Immune-PSO with Adaptive Search Strategy

Immune Particle Swarm Optimization (IPSO) is an effective optimization algorithm that introduces the concept of concentration from Artificial Immune Algorithm (AIA) into PSO. This Hybrid algorithm can upgrade the diversity of PSO and enhance its search quality. However, arisen from the concentration repression method and the random vaccine in AIA, the algorithm will degenerate during iteration. In this paper, we present an adaptive search strategy that can regulate the search range and population size according to the concentration in different periods of optimization to avoid the negative impact from swarm degeneration. In addition, Chaos sequences are also brought in to generate the random numbers in PSO in order to improve the algorithm's performance. For the typical business management problem, vehicle-scheduling problem, an appropriate mathematical model has been constructed in the article. Experimental results show that the Chaos based Immune-PSO with Adaptive Search Strategy (AS-ICPSO) proposed in this paper can handle vehicle-scheduling problem excellently and has a better performance than classical IPSO and PSO.