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2002

Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons

8 years 10 months ago
Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons
The rapid advances of evolutionary methods for multi-objective (MO) optimization poses the difficulty of keeping track of the developments in this field as well as selecting an appropriate evolutionary approach that best suits the problem in-hand. This paper aims to analyze the strength and weakness of different evolutionary methods proposed in literatures. For this purpose, ten existing well-known evolutionary MO approaches have been experimented and compared extensively on two benchmark problems with different MO optimization difficulties and characteristics. Besides considering the usual two important aspects of MO performance, i.e., the spread across the Pareto-optimal front as well as the ability to attain the global optimum or final trade -offs, this paper also proposes a few useful performance measures for better and comprehensive examination of each approach both quantitatively and qualitatively. Simulation results for the comparisons are commented and summarized.
Kay Chen Tan, Tong Heng Lee, Eik Fun Khor
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2002
Where AIR
Authors Kay Chen Tan, Tong Heng Lee, Eik Fun Khor
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