Application of Improved Genetic Algorithm and Ant Colony Algorithm in Multi-objective Path Planning

Abstract Due to the shortcomings of GA in path planning, there are too many control variables and it is easy fall into local areas. We introduce idea a gene bank store new chromosomes generated each time bank. The linear regression proposed predict probability crossover mutation next generation. At same time, method increasing chromosome diversity proposed, which avoids problem that difficult set variable parameters reasonably optimum when applied planning. To overcome ACO an with improved heuristic function transition was real-time environment given. Through traveling salesman problem, combined apply experimental results show fusion this paper superior ordinary all aspects