An Empirical Study on Parallel Multi-objective Genetic Algorithms: 0/1 Knapsack Problem-A Case Study

Many of the optimization problems in the real world are multi-objective in nature, and NSGA II is commonly used. Frequently, the multi-objective problems present a high complexity, so classical meta-heuristic algorithms fail to solve them in a reasonable amount of time. In this context, parallelism is a choice to overcome this fact to some extent. In this paper we study three different models i.e. trigger model, island model and cone separation model to parallelize NSGA-II, by considering 0/1 knapsack problem as a case study. Further we emphasize on two factors that scale the parallelism i.e., convergence and time. The experimental results conform that cone separation model is better than other two models in terms of processing time and approximation to true Pareto front.