An ant colony optimization algorithm with evolutionary experience-guided pheromone updating strategies for multi-objective optimization

Since the multi-objective ant colony optimization algorithm consumes a massive cost of time and computation resources, improving its convergence performance is essential. This paper proposes a historical experience-guided pheromone updating approach to improve the efficiency and enhance the optimization quality, which uses a learning automata to choose suitable pheromone updating methods adaptively according to the searching history. The learning automata performs different kinds of pheromone updating schemes, including a novel intragroup evolutionary information-guided strategy and two widely accepted strategies. A comparative experiment tests the proposed algorithm on multi-objective benchmark traveling salesman problems with a high-dimensional search space, as well as the primer design problem, which is practical in the biology area. The experimental results indicate that the proposed algorithm has a competitive performance on both the benchmark problems and the practical problem.