Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm

Abstract

In this paper we present a new optimization algorithm, and the proposed algorithm operates in two phases. In the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems.

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

“…6. The obtained results are distribution, spread and smooth which are the same or better than the results obtained in other researches [11,45,62].…”

“…The welded beam design is a real-life application problem [11,62], which the aim is to minimize the cost and the endpoint's deflection subject to constraints on shear stress, bending stress and buckling load (Fig.5). The detailed formulation can be found in [11,45,62,63]. It is desired to find four design parameters …”

An Improved SPEA2 Algorithm with Adaptive Selection of Evolutionary Operators Scheme for Multiobjective Optimization Problems

“…Meanwhile, many other nature-inspired metaheuristics including Ant Colony Optimization [15,16], Particle Swarm Optimization [17,18], Immune Algorithm [19,20], and Estimation of Distribution Algorithm [21,22] have been successfully applied to handle MOPs. Moreover, MOEAs for complicated MOPs have also been extensively investigated, such as MOEA for constraint MOPs [23], dynamic MOPs [24], and many objective optimization problems [25].…”