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Reference Point Based Multi-Objective Optimization Using Evolutionary Algorithms
| Content Provider | CiteSeerX |
|---|---|
| Abstract | 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 Paretooptimality 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 two approaches for this task: (i) a modified EMO procedure based on the elitist nondominated sorting GA or NSGA-II [1] and (ii) a predatorprey approach based on original grid based procedure [2]. On two-objective to 10-objective optimization test problems, the modified NSGA-II approach shows its efficacy in finding an adequate set of Pareto-optimal points. On two and three-objective problems, the predator-prey approach also demonstrate its usefulness. 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. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Reference Point Multi-objective Optimization Using Evolutionary Algorithm Three-objective Problem Predator-prey Approach Emo Procedure 10-objective Optimization Test Problem Single Preferred Pareto-optimal Solution Reliable Decision Original Grid Pareto-optimal Solution Important Task Pareto-optimal Frontier Adequate Decision-making Evolutionary Multi-objective Optimization Predatorprey Approach Pareto-optimal Point Nsga-ii Approach Representative Set Emo Methodology Preferred Set Solution Preference-based Strategy Lukewarm Attention Adequate Set |
| Content Type | Text |
| Resource Type | Article |
