Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Micro-Genetic Algorithm for Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In this paper, a new meta-heuristic algorithm is proposed for solving combinatorial optimization problems. The proposed algorithm follows the adaptive swarm intelligence technique proposed earlier, but uses the novel concept of multiple swarms. Unlike the existing multi swarm techniques, the swarms in the proposed algorithm are dynamic and operate with a small size. The whole swarm (population) is divided into many small swarms, these swarms are regrouped frequently by using various regrouping schemes and information is exchanged among the swarms. A discrete neighborhood search algorithm is also augmented to the proposed swarm intelligence algorithm to improve the intensification mechanism. The resulting hybrid adaptive multi-swarm algorithm is built with typical features like Pareto dominance, density estimation, and an external archive to store the non-dominated solutions in order to handle multiple objectives. The performance of the proposed multi-objective multi swarm algorithm is demonstrated by solving a laminate composite pressure vessel subjected to both combinatorial as well as design constraints. Further, the proposed algorithm is compared with three state-of-the- art multi-objective optimizers: Non-dominated sorting Genetic Algorithm (NSGA-II), Pareto Archived Evolutionary Strategy (PAES) and multi-objective PSO( MPSO). The studies presented in this paper indicate that proposed algorithm produces very competitive Pareto fronts according to the applied convergence metric and it clearly outperforms the other three algorithms