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A Simple but Powerful Multiobjective Hybrid Genetic Algorithm

We propose a new multiobjective hybrid genetic algorithm by combining local search with an EMO (evolutionary multiobjective optimization) algorithm. In the design of our algorithm, we try to make its algorithmic complexity as simple as possible so that it can be easily understood, easily implemented and easily executed within short CPU time. At the same time, we try to maximize its search ability. Our algorithm makes use of advantages of both EMO and local search for achieving high search ability without increasing its algorithmic complexity. For example, each solution is evaluated based on Pareto ranking and the concept of crowding as in many EMO algorithms. On the other hand, a weighted scalar fitness function is used for efficiently executing local search. A kind of elitism is also implemented using Pareto ranking in the process of generation update. The search ability of our algorithm is examined through computational experiments on multiobjective 0/1 knapsack problems. Our algorithm is compared with well-known EMO algorithms (i.e., SPEA of Zitzler & Thiele and NSGA-II of Deb et al.) and memetic EMO algorithms (i.e., M-PAES of Knowles & Corne and MOGLS of Jaszkiewicz).