An improved many-objective meta-heuristic adaptive decomposition algorithm based on mutation individual position detection

Abstract

Industrial applications and optimization problems in reality often involve multiple objectives. Due to the high dimensionality of objective space in many-objective optimization problems (MaOPs), the ability of traditional evolution operators to search the optimal region and generate promising offspring sharply decreases. Besides, as the number of objectives increases, it becomes difficult to balance the convergence and diversity of the population. Considering all these facts, this paper proposes a mutation individual position detection strategy. It estimates both individual fitness and diversity contributions, and assigns appropriate positions to individuals in the mutation operator through individual ranking. Then, by introducing an external population to adjust the weight vectors, its maintenance process takes into account the matching information between the population and the weight vectors. By comparing five representative algorithms, numerical experiments have shown that the algorithm can obtain a well distributed final solution set on optimization problems of various objective scales. Moreover, it also demonstrates advantages in generating excellent offspring individuals and balancing the overall performance of the population. In summary, the algorithm has competitiveness in solving MaOPs.