In: Computers and Technology

Submitted By ssv101194
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THE PRESENCE of multiple objectives in a problem, in principle, gives rise to a set of optimal solutions (largely known as Pareto-optimal solutions), instead of a single optimal solution. In the absence of any further information, one of these
Pareto-optimal solutions cannot be said to be better than the other. This demands a user to find as many Pareto-optimal solutions as possible. Classical optimization methods (including the multicriterion decision-making methods) suggest converting the multiobjective optimization problem to a single-objective optimization problem by emphasizing one particular Pareto-optimal solution at a time. When such a method is to be used for finding multiple solutions, it has to be applied many times, hopefully finding a different solution at each simulation run.
Over the past decade, a number of multiobjective evolutionary algorithms (MOEAs) have been suggested [1], [7], [13],
Manuscript received August 18, 2000; revised February 5, 2001 and
September 7, 2001. The work of K. Deb was supported by the Ministry of Human Resources and Development, India, under the Research and
Development Scheme.
The authors are with the Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur PIN 208 016, India (e-mail: deb@iitk.ac.in).
Publisher Item Identifier S 1089-778X(02)04101-2.
[20], [26]. The primary reason for this is their ability to find multiple Pareto-optimal solutions in one single simulation run.
Since evolutionary algorithms (EAs) work with a population of solutions, a simple EA can be extended to maintain a diverse set of solutions. With an emphasis for moving toward the true
Pareto-optimal region, an EA can be used to find multiple
Pareto-optimal solutions in one single simulation run.
The nondominated sorting genetic algorithm (NSGA) proposed in [20] was one of the first such EAs. Over the…...

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