Application Research On The Multi-objective Chimp Optimization Algorithm And Vehicle Routing Problem

The chimp optimization algorithm(ChOA)is a meta-heuristic optimization algorithm that simulates the cooperative predation behavior of chimp groups.The algorithm has the characteristics of fast convergence speed at the beginning of iterations,and it can adaptively balance the exploration and exploitation capacity with fewer control parameters,thus it has been successfully applied to optimization problems in many fields.However,it is found that the algorithm still has the shortcomings of slow convergence speed in the later stage,low solution accuracy,and easy to fall into the local optimal solution.Aiming at the shortcomings of chimp optimization algorithm,this paper analyzes and improves the algorithm from the perspective of multi-strategy and multi-objective,and proposes two improved versions of chimp optimization algorithm.Finally,this paper applies this improved algorithm to solve the VRPTW,the multi-objective GVRP and the three-dimensional spherical vehicle routing problem,so as to further expand the theory of ChOA and explore its application scope.The main research work of this paper is as follows:(1)Two genetic operators(crossover and mutation)are introduced to enhance the exploration ability of the algorithm in the search process;In addition,archiving,elite selection mechanism and non-dominant ranking strategies are introduced to adapt to multi-objective optimization tasks,and an improved multi-objective chimp optimization algorithm(MO-ChOA)is proposed.The improved ChOA is applied to solve vehicle routing problem with time windows,and the test results are compared with the currently popular algorithms.Experimental results show that MO-ChOA has strong competitiveness in solving such problems.(2)In the initialization stage of MO-ChOA,an improved PFIH method is introduced to improve the initial convergence speed of the population.A grid elite selection mechanism and another genetic operator(selection)are introduced to select outstanding chimp individuals,so as to further improve the later exploration and exploitation capacity of MO-ChOA.A local search strategy is introduced to improve the solution accuracy of the algorithm,and a multi-objective chimp optimization algorithm(MO-GChOA)with mixed genetic operators is proposed.The proposed algorithm is applied to the multi-objective green vehicle path problem,and the test results are compared with the current mainstream multi-objective optimization algorithms.Experimental results show that MO-GChOA has strong optimization ability in solving multi-objective green vehicle path problems.(3)In the initialization stage of ChOA,quantum coding methods are introduced to increase the diversity of populations.Genetic operators,local search strategy and multi-population strategy are introduced to improve the exploration ability and solution accuracy of the algorithm,and an improved multi-population chimp optimization algorithm(MG-ChOA)is proposed.Apply this algorithm to solve the proposed 3D spherical vehicle path problem and compare the test results with other algorithms.The simulation results show that the search ability and solving accuracy of MG-ChOA are superior to other algorithms,and it is proved that the improved algorithm is effective and feasible in solving the three-dimensional spherical vehicle path problem.