Abstract
Recently, Indicator-based Evolutionary Algorithms are considered the main issue for researchers in the evolutionary multi-objective frameworks. Due to the capability of the Indicator-based approaches in obtaining a finest non-dominated solutions and the potential of these approaches on achieving the well-distributed solutions, these approaches become popular among modern Multi-Objective Evolutionary Algorithms (MOEAs). Most modern MOEAs are intended to converge to the Pareto optimal front through preserving the population diversity in the objective space. In this regard, the intention of this work is presenting a novel MOEA to enhance the population diversity among the non-dominated vectors in the solution space. The idea of this method is inspired by the Hierarchical clustering. In this attitude, an adept approach is planned to present a new indicator as a selection method during the optimization cycle. The gain of this technique is a desirable set with more diverse solutions in the solution space during the environmental selection operator. In the last part, this work also improved the rate of the convergence by introducing a parent selection mechanism. The selection method is simple and effective, which is worked base on the selection of proper members of parents' population instead of a random mechanism. This bright parent selection is adopted to accelerate the convergence of the proposed method. This work is applied to a wide range of well-established test problems. The obtained results validate the motivation on the basis of diversity and performance measures in comparison with the state of the art algorithms.