Reference point based multi-objective optimization using evolutionary algorithms
Kalyanmoy Deb,J. Sundar +1 more
- 08 Jul 2006
- pp 635-642
TL;DR: This paper proposes a modified EMO procedure based on the elitist non-dominated sorting GAor NSGA-II and demonstrates how, instead of one solution, a preferred set solutions near the reference points can be found parallely.
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Abstract: Evolutionary multi-objective optimization (EMO) methodologies have been amply applied to find a representative set of Pareto-optimal solutions in the past decade and beyond. Although there are advantages of knowing the range of each objective for Pareto-optimality and the shape of the Pareto-optimal frontier itself in a problem for an adequate decision-making, the task of choosing a single preferred Pareto-optimal solution is also an important task which has received a lukewarm attention so far. In this paper, we combine one such preference based strategy with an EMO methodology and demonstrate how, instead of one solution, a preferred set solutions near the reference points can be found parallely. We propose a modified EMO procedure based on the elitist non-dominated sorting GAor NSGA-II. On two-objective to 10-objective optimization problems, the modified NSGA-II approach shows its efficacy in finding an adequate set of Pareto-optimal points. Such procedures will provide the decision-maker with a set of solutions near her/his preference so that a better and a more reliable decision can be made.
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Citations
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
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References
A fast and elitist multiobjective genetic algorithm: NSGA-II
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Kalyanmoy Deb,Deb Kalyanmoy +1 more
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Gary B. Lamont,David A. Van Veldhuizen +1 more
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Nonlinear Multiobjective Optimization
Kaisa Miettinen
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TL;DR: This paper is concerned with the development of methods for dealing with the role of symbols in the interpretation of semantics.
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An efficient constraint handling method for genetic algorithms
TL;DR: GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter to guide the search towards the constrained optimum.
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