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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Vydané v:	IEEE transactions on evolutionary computation Ročník 17; číslo 4; s. 474 - 494
Hlavní autori:	Rui Wang, Purshouse, Robin C., Fleming, Peter J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.08.2013 Institute of Electrical and Electronics Engineers
Predmet:	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
On-line prístup:	Získať plný text
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Abstract	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.
AbstractList	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.
Author	Fleming, Peter J. Purshouse, Robin C. Rui Wang
Author_xml	– sequence: 1 surname: Rui Wang fullname: Rui Wang email: cop10rw@sheffield.ac.uk organization: Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK – sequence: 2 givenname: Robin C. surname: Purshouse fullname: Purshouse, Robin C. email: r.purshouse@sheffield.ac.uk organization: Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK – sequence: 3 givenname: Peter J. surname: Fleming fullname: Fleming, Peter J. email: p.fleming@shef.ac.uk organization: Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK
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Snippet	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...
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SubjectTerms	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
Title	Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization
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