Parallel Multi-Criterion Genetic Algorithms: Review and Comprehensive Study

Parallel Multi-Criterion Genetic Algorithms: Review and Comprehensive Study

Bhabani Shankar Prasad Mishra (KIIT University, India), Subhashree Mishra (KIIT University, India) and Sudhansu Sekhar Singh (School of Electronics Engineering, KIIT University, India)
DOI: 10.4018/978-1-5225-0788-8.ch008
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The objective of this paper is to study the existing and current research on parallel multi-objective genetic algorithms (PMOGAs) through an intensive experiment. Many early efforts on parallelizing multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an acceptable solution of them with various examples. Further, the authors tried to identify some of the issues that have not yet been studied systematically under the umbrella of parallel multi-objective genetic algorithms. Finally, some of the potential application of parallel multi objective genetic algorithm is discussed.
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2. Multi-Objectives Genetic Algorithms

Multi-objective optimization methods are based on the idea of finding optimal solutions to problems having multiple objectives (Coello, C. A. 1999; Coello, C. A. 2000; Zydallis 2003). Hence, for this type of problems the user is never satisfied by finding one solution that is optimized with respect to a single criterion. The principle of a multi-criteria optimization procedure is different from that of a single criterion optimization. In a single criterion the main objective is to find a globally optimal solution. However, in a multi-criteria optimization problem, there is more than one objective function, each of which may have a different individual optimal solution. The objective functions are said to be conflicting if there exists an adequate distinction in the optimal solutions corresponding to different objectives, this presents a set of optimal solutions, instead of one optimal solution known as Pareto-optimal solutions (Coello, C. A. 2001 & 2006; Deb 1999; Schaffer 1985; Veldhuizen & Lamont 2000)

A multi-objective problem can be defined having x objectives (say fi, i = 1, 2, …, x and x>1). Any two solutions S1 and S2 (having ’m’ decision variables, each) can have one, two possibilities -one dominates the other or none dominates the other’. Solution S1 is said to dominate the other solution, S2, if the following conditions are true:

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