Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization

The simultaneous optimization of many objectives (in excess of 3), in order to obtain a full and satisfactory set of tradeoff solutions to support a posteriori decision making, remains a challenging problem. The concept of coevolving a family of decision-maker preferences together with a population...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 17; no. 4; pp. 474 - 494
Main Authors: Rui Wang, Purshouse, Robin C., Fleming, Peter J.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.08.2013
Institute of Electrical and Electronics Engineers
Subjects:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Approximation algorithms
Coevolution
Computer science; control theory; systems
evolutionary algorithms
Evolutionary computation
Exact sciences and technology
many-objective optimization
Monte Carlo methods
Optimization
Search problems
Theoretical computing
Vectors
Dominating set
Pareto optimum
Decision support system
Decision making
Evolutionary algorithm
Performance characteristic
Multiobjective programming
evolutionary algorithms
Dominance
many-objective optimization
Randomized algorithm
Coevolution
Optimization
Search algorithm
Genetic algorithm
Preference
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:The simultaneous optimization of many objectives (in excess of 3), in order to obtain a full and satisfactory set of tradeoff solutions to support a posteriori decision making, remains a challenging problem. The concept of coevolving a family of decision-maker preferences together with a population of candidate solutions is studied here and demonstrated to have promising performance characteristics for such problems. After introducing the concept of the preference-inspired coevolutionary algorithm (PICEA), a realization of this concept, PICEA-g, is systematically compared with four of the best-in-class evolutionary algorithms (EAs); random search is also studied as a baseline approach. The four EAs used in the comparison are a Pareto-dominance relation-based algorithm (NSGA-II), an ε-dominance relation-based algorithm [ ε-multiobjective evolutionary algorithm (MOEA)], a scalarizing function-based algorithm (MOEA/D), and an indicator-based algorithm [hypervolume-based algorithm (HypE)]. It is demonstrated that, for bi-objective problems, all of the multi-objective evolutionary algorithms perform competitively. As the number of objectives increases, PICEA-g and HypE, which have comparable performance, tend to outperform NSGA-II, ε-MOEA, and MOEA/D. All the algorithms outperformed random search.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2012.2204264