A Multi-objective Approach based on Competitive Optimization Algorithm and its Engineering Applications

Herein a new multi-objective evolutionary optimization algorithm is presented based on the competitive optimization algorithm in order to solve the multi-objective optimization problems. Based on the nature-inspired competition, the competitive optimization algorithm acts between the animals such as birds, cats, bees and ants. The present work entails the main contributions as what follows. Primarily, a novel method is presented for pruning the external archive, while keeping the diversity of the Pareto front. Secondly, a hybrid approach of powerful mechanisms such as opposition-based learning and chaotic maps is was used in order to maintain the diversity in the search space of the initial population. Thirdly, a novel method is provided in order to transform a multi-objective optimization problem into a single-objective optimization problem. A Comparison of the results of the simulation for the proposed algorithm is performed with some well-known optimization algorithms. The comparison indicates that the proposed approach could be a better option for solving the multi-objective optimization problems.