A Survey on Pareto-Based EAs to Solve Multi-objective Optimization Problems

Most of the real-world optimization problems have multiple objectives to deal with. Satisfying one objective at a time may lead to the huge deviation in other. Therefore, an efficient tool is required which can handle multiple objectives simultaneously in order to provide a set of desired solutions. In view of this, multi-objective optimization (MOO) attracts the attention of the researchers since last few decades. Many classical optimization techniques have been proposed by the researchers to solve the multi-objective optimization problems. However mostly, the gradient-based approaches fail to handle complex MOO problems. Hence, as an alternative, researchers have shown their interest toward population-based optimization approaches to solve the MOO problems and come up with convincing results even in the complex environment. Evolutionary algorithms (EAs), which are the first in the group of population-based approach, enjoy almost a decade in providing the solutions to MOO problems. The real challenge is to achieve the set of solutions called Pareto-optimal set. The smooth landing on such set is only possible if there exists diversified solution in the population. Due to the continuous effort, there is a gradual development in the proposition of various efficient Pareto-based approaches in the literature to solve MOEAs. A critical review of those approaches is being carried out in this present study.