| Multi-objective generalized ant colony algorithm is the improvement and enhancement based ontraditional multi-objective ant colony algorithm, but there is still a lot of missing on convergencerate theoretical analysis and optimization performance. Lack of theoretical research has become abottleneck constraint the further development and application of the algorithm.This article establishes a universal process model based on absorbing Markov process toanalyze the convergence rate of multi-objective generalized ant colony algorithm.Two multi-objective generalized ant colony algorithm simple examples as the foundation, thisarticle discusses the multi-objective generalized ant colony algorithm time complexity.This paper analyzes the convergence of multi-objective generalized ant colony algorithmconverges type theoretically, pushed to the calculation formula for the algorithm convergence. Inthe analysis of the convergence rate of Multiobjective generalized ant colony algorithm, theconvergence time is an important index to estimate the convergence rate, change interval gives theconvergence rate of the multi-object generalized ant colony algorithm. This paper obtaines themulti-object generalized ant colony algorithm’s time complexity and space complexity.The convergence of the simulation experiments are carried out to validate the multi-objectivegeneralized ant colony algorithm convergence theory, in addition to validate the convergence speedof multi-objective generalized ant colony algorithm is superior than traditional multi-objective antcolony algorithm.This paper uses multi-objective ant colony algorithm for solving multi-objective vehicle routingproblem, and establishes the mathematical model of multi-objective logistics vehicle path planningproblem. Through Matlab simulation experiments verify that the multi-object generalized antcolony algorithm can can solve multi-objective vehicle routing problem. |
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