In this paper, an entropy-based metric is presented for quality assessment of non-dominated solution sets obtained from a multiobjective optimization technique. This metric quantifies the ‘goodness’ of a solution set in terms of its distribution quality over the Pareto-optimal frontier. Therefore, it can be useful in comparison studies of different multi-objective optimization techniques, such as Multi-Objective Genetic Algorithms (MOGAs), wherein the capabilities of such techniques to produce and maintain diversity among different solution points are desired to be compared on a quantitative basis. An engineering test example, the multiobjective design optimization of a speed-reducer, is presented in order to demonstrate an application of the proposed entropy metric.

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