An adaptive neighborhood based evolutionary algorithm with pivot- solution based selection for multi- and many-objective optimization

Pareto dominance-based multi-objective evolutionary algorithms (PDMOEAs) encounter scalability issues due to the lack of selection pressure as the dimensionality of objective space increases. In addition, PDMOEAs combat difficulties in achieving the proper balance between convergence and diversity. To overcome this issue, recently, additional convergence-related metrics have been proposed for PDMOEAs to improve their performance by enhancing the selection pressure towards the true Pareto front; however, these approaches have limitations. To address the drawbacks of the previous approaches, in this paper, we propose an adaptive neighborhood based evolutionary algorithm with pivot-solution based selection (Pi-MOEA) to tackle multi- and many-objective optimization problems. The proposed Pi-MOEA approach identifies a set of pivot-solutions to improve the convergence performance. An adaptive neighborhood is designed among the individuals, and the average ranking method is employed to identify the pivot-solutions within the neighborhood. In addition, to preserve the population diversity, density estimation based on Euclidean distance is adopted in Pi-MOEA. The performance of the Pi-MOEA is investigated extensively on 26 test problems from three popular benchmark problem suites by comparing them with seven state-of-the-art algorithms. The experimental results show that the Pi-MOEA algorithm performs considerably better when compared with state-of-the-art algorithms.