Book Chapter10.1007/978-3-319-13356-0_35
A Novel Evolutionary Multi-objective Algorithm Based on S Metric Selection and M2M Population Decomposition
Lei Chen,Hai-Lin Liu,Chuan Lu,Chuan Lu,Yiu-ming Cheung,Jun Zhang +5 more
- 01 Jan 2015
- pp 441-452
6
TL;DR: A novel S metric selection evolutionary algorithm based on the population decomposition strategy MOEA/D-M2M is proposed to give a simple but effective method to improve the effectiveness of SMS based algorithm.
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Abstract: The excellent performance of evolutionary multi-objective algorithms based on S metric selection (SMS) has been identified by many researchers. However, huge computational effort of S metric calculation has limited the full application of those algorithms. This paper proposes a novel S metric selection evolutionary algorithm (SMS-M2M) based on the population decomposition strategy MOEA/D-M2M. In SMS-M2M, SMS is conducted in each subpopulation instead of the whole population, which can avoid the S metric calculation of the total population. The purpose of population decomposition is to directly reduce the huge computational effort of calculating S metric and thus to give a simple but effective method to improve the effectiveness of SMS based algorithm. SMS-M2M utilizes the same SMS with a popular SMS based evolutionary algorithm SMS-EMOA. Numerical studies of SMS-M2M and SMS-EMOA have shown that the M2M population decomposition can effectively reduce the computational effort of SMS, meanwhile the theoretic analysis identifies the efficiency and effectiveness of SMS-M2M.
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References
•Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
- 01 Jan 2001
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
•Book
Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications
Eckart Zitzler
- 27 Dec 1999
TL;DR: The basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective and the focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms.
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SMS-EMOA : Multiobjective selection based on dominated hypervolume
TL;DR: A steady-state EMOA is proposed that features a selection operator based on the hypervolume measure combined with the concept of non-dominated sorting, thereby focussing on interesting regions of the Pareto front.
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