An entropy-based metric is presented that can be used for assessing the quality of a solution set as obtained from multi-objective optimization techniques. This metric quantifies the “goodness” of a set of solutions in terms of distribution quality over the Pareto frontier. The metric can be used to compare the performance of different multi-objective optimization techniques. In particular, the metric can be used in analysis of multi-objective evolutionary algorithms, 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 multi-objective design optimization of a speed-reducer, is provided to demonstrate an application of the proposed entropy metric.
An Information-Theoretic Entropy Metric for Assessing Multi-Objective Optimization Solution Set Quality
Contributed by the Design Automation Committee for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received November 2001; revised March 2003. Associate Editor: G. M. Fadel.
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Farhang-Mehr, A., and Azarm, S. (January 22, 2004). "An Information-Theoretic Entropy Metric for Assessing Multi-Objective Optimization Solution Set Quality ." ASME. J. Mech. Des. December 2003; 125(4): 655–663. https://doi.org/10.1115/1.1623186
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