New Little-Window-Based Self-adaptive Ant Colony-Genetic Hybrid Algorithm

Abstract

To improve the convergence time of basic ant colony optimization algorithm and avoid falling in local best, a novel ant colony-genetic hybrid algorithm is proposed. Firstly, the self-adaptive strategy of evaporation coefficient is adopted to enhance global search ability. Secondly, the global pheromone update rule is introduced to restrict ants release pheromone only in the best route and the worst route. And the local pheromone update rule is used to decrease pheromone on the traversed edges to avoid ants produce identical solutions and falling in local best. Thirdly, with the greedy inversion operator, genetic algorithm mutation mechanism deals with falling in local best and degeneration. Finally, variable width little-window limits the mobile range of ants so that inferior solutions could be eliminated in terms of fact. Comparing with traditional methods, the simulation result on TSP shows that new algorithm has higher convergence speed and better escape capability from local best.