Author:
Makoto Ohki
Affiliation:
Field of Technology, Tottori University, 4, 101 Koyama-Minami, Tottori, Tottori 680-8552 and Japan
Keyword(s):
Many-Objective Evolutionary Algorithm, Pareto Partial Dominance, Subset Size Scheduling, NSGA-II, 0/1 Knapsack Problem.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computation and Control
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Soft Computing
Abstract:
This paper describes techniques for improving the solution search performance of a multi-objective evolutionary algorithm (MOEA) in many-objective optimization problems (MaOP). As an MOEA for MaOP, we focus on NSGA-II based on Pareto partial dominance. NSGA-II based on Pareto partial dominance requires beforehand a combination list of the number of objective functions to be used for Pareto partial dominance. Moreover, the contents of the combination list greatly influence the optimization result. We propose to schedule a parameter r meaning the subset size of objective functions for Pareto partial dominance. This improvement not only releases users from the schedule of the parameter r but also improves the convergence to Pareto optimal solutions (POS) and the diversity of the individual set obtained by the optimization. Moreover, we propose to kill individuals of the archive set, where the individuals have the same contents as the individual created by the mating. This improvement ex
cludes individuals with the same contents which obtained relatively good evaluations. The improved technique and other conventional techniques are applied to a many-objective 0/1 knapsack problem for verification of the effectiveness.
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