Research On Improvement And Application Of Artificial Bee Colony Algorithm

Nowadays,there are a lot of optimization problems which need to solve,such as production scheduling,economic analysis,biological medicine,etc.Especially,with the rise of e-commerce industry,modern logistics industry has ushered in the peak period of development.However,as one of the most critical part in logistics industry,the speed of logistics distribution has directly impact on customers' service evaluation of logistics companies.Vehicle routing optimization problem is an important topic in logistics transportation,which is mainly optimized by swarm intelligence optimization algorithms.Artificial Bee Colony algorithm is a very promising bionic algorithm.It has the advantages of few control parameters,strong robustness and implementation simplicity and has been successfully applied to combinatorial optimization,wireless sensor network and image processing,etc.However,Artificial Bee Colony algorithm still owns some disadvantages,such as weak local development ability,slow convergence speed,and low optimization precision.To further improve the performance of Artificial Bee Colony algorithm,this paper analyzes the existing problems,and focuses on the improvements and applications of Artificial Bee Colony algorithm.In the aspect of algorithm improvement,the ideas of differential evolution and Gauss mutation are introduced into the search strategy and we have designed an artificial bee colony algorithm based on the mixed mutation of current optimal solution.The differential evolution search strategy is adopted by followers.In early iterations,differential mutation factor is introduced to increase the diversity of solutions with the global search,and in later iterations,it can accelerate the convergence speed of the algorithm with the local search.The scouts adopt Gauss mutation detection strategy to perturb the local optimal solution with Gauss mutation operator,and then jump out of local optimal value.A new solution can be generated near the current optimal solution.The proposed algorithm enriches the diversity of population and effectively improves the convergence speed of algorithm with the guidance of the current optimal solution.The experimental results on 6 typical test functions show that the proposed artificial bee colony algorithm based on hybrid mutation is superior to other artificial bee colony algorithms in the convergence speed and optimization precision.In the aspect of algorithm applications,the proposed artificial bee colony algorithm has been applied to the path optimization problem in logistics distribution vehicles.Firstly,the algorithm is discretized,and then the greedy strategy is used to generate the initial solution of problem.We design 3 generation strategies of candidate solution and combine them to expand the neighborhood search range.Meanwhile,the crossover idea of genetic algorithm is introduced into the detection strategy,and the detection performance of scouts is improved through 2 kinds of crossover methods.Finally,the experimental results on 2 different scale examples show that,compared with other evolutionary algorithms,the proposed algorithm is very effective for solving the vehicle routing problems.