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Solving Two Multi-Objective Optimization Problems Using Evolutionary Algorithm
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Author(s): Ruhul A. Sarker (University of New South Wales, Australia), Hussein A. Abbass (University of New South Wales, Australia)and Charles S. Newton (University of New South Wales, Australia)
Copyright: 2003
Pages: 16
Source title:
Computational Intelligence in Control
Source Author(s)/Editor(s): Masoud Mohammadian (University of Canberra, Australia), Rahul A. Sarker (University of New South Wales, Australia)and Xin Yao (The University of Birmingham, UK)
DOI: 10.4018/978-1-59140-037-0.ch013
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Abstract
Being capable of finding a set of pareto-optimal solutions in a single run is a necessary feature for multi-criteria decision making, Evolutionary algorithms (EAs) have attracted many researchers and practitioners to address the solution of Multi-objective Optimization Problems (MOPs). In a previous work, we developed a Pareto Differential Evolution (PDE) algorithm to handle multi-objective optimization problems. Despite the overwhelming number of Multi-objective Evolutionary Algorithms (MEAs) in the literature, little work has been done to identify the best MEA using an appropriate assessment methodology. In this chapter, we compare our algorithm with twelve other well-known MEAs, using a popular assessment methodology, by solving two benchmark problems. The comparison shows the superiority of our algorithm over others.
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