Journal Article10.1109/TEVC.2015.2433266
Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement
Zhenan He,Gary G. Yen +1 more
164
TL;DR: A new approach to directly handle the challenges to solve many-objective optimization problems (MaOPs) is proposed, which includes two stages: first, the whole population quickly approaches a small number of "target” points near the true Pareto front; then, the proposed diversity improvement strategy is applied to facilitate these individuals to spread and well distribute.
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Abstract: Evolutionary algorithms have been successfully applied for exploring both converged and diversified approximate Pareto-optimal fronts in multiobjective optimization problems, two- or three-objective in general. However, when solving problems with many objectives, nearly all algorithms perform poorly due to the loss of selection pressure in fitness evaluation. An extremely large objective space could inadvertently deteriorate the effect of an evolutionary operator. In this paper, we propose a new approach to directly handle the challenges to solve many-objective optimization problems (MaOPs). This novel design includes two stages: first, the whole population quickly approaches a small number of “target” points near the true Pareto front; then, the proposed diversity improvement strategy is applied to facilitate these individuals to spread and well distribute. As a case study, the proposed algorithm based on this design is compared with five state-of-the-art algorithms. Experimental results show that the proposed method exhibits improved performance in both convergence and diversity for solving MaOPs.
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Citations
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]
TL;DR: PlatEMO as discussed by the authors is a MATLAB platform for evolutionary multi-objective optimization, which includes more than 50 multiobjective evolutionary algorithms and more than 100 multobjective test problems, along with several widely used performance indicators.
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•Posted Content
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization
TL;DR: The main features of PlatEMO are introduced and how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators are illustrated.
A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition
TL;DR: A comprehensive survey of the decomposition-based MOEAs proposed in the last decade is presented, including development of novel weight vector generation methods, use of new decomposition approaches, efficient allocation of computational resources, modifications in the reproduction operation, mating selection and replacement mechanism, hybridizing decompositions- and dominance-based approaches, etc.
575
Localized Weighted Sum Method for Many-Objective Optimization
TL;DR: A novel decomposition-based EMO algorithm called multiobjective evolutionary algorithm based on decomposition LWS (MOEA/D-LWS) is proposed in which the WS method is applied in a local manner, and is a competitive algorithm for many-objective optimization.
312
Particle Swarm Optimization With a Balanceable Fitness Estimation for Many-Objective Optimization Problems
Qiuzhen Lin,Songbai Liu,Qingling Zhu,Chaoyu Tang,Ruizhen Song,Jianyong Chen,Carlos A. Coello Coello,Ka-Chun Wong,Jun Zhang +8 more
TL;DR: A balanceable fitness estimation method and a novel velocity update equation are presented, to compose a novel MOPSO (NMPSO), which is shown to be more effective to tackle MaOPs.
274
References
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TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
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