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Partial Dominance for Many-Objective Optimization
Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 2020Many optimisation problems have more than three objectives, referred to as many-objective optimisation problems (MaOPs). As the number of objectives increases, the number of solutions that are non-dominated with regards to one another also increases.
Mardé Helbig, Andries Engelbrecht
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Differential evolution induced many objective optimization
2017 IEEE Congress on Evolutionary Computation (CEC), 2017We propose a novel approach to solve the many objective optimization (MaOO) problem using a ranking policy, instead of the Pareto ranking, supposing that a solution is unlikely to perform well for all objectives in a MaOO problem. A solution is thus evolved with respect to a specific objective only, which it may proficiently optimize.
Pratyusha Rakshit +3 more
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Guest Editorial Evolutionary Many-Objective Optimization
IEEE Transactions on Evolutionary Computation, 2018Over the past two decades, evolutionary algorithms have successfully been applied to single and multiobjective optimization problems having up to three objectives. Compared to traditional mathematical programming techniques, evolutionary multiobjective algorithms (MOEAs) are particularly powerful in achieving multiple nondominated solutions in a single
Yaochu Jin +2 more
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Behavior of Evolutionary Many-Objective Optimization
Tenth International Conference on Computer Modeling and Simulation (uksim 2008), 2008Evolutionary multiobjective optimization (EMO) is one of the most active research areas in the field of evolutionary computation. Whereas EMO algorithms have been successfully used in various application tasks, it has also been reported that they do not work well on many-objective problems.
Hisao Ishibuchi +2 more
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Information Sciences, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Luo, Jianping +5 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Luo, Jianping +5 more
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Corner Based Many-Objective Optimization
2014The performance of multi-objective evolutionary algorithms (MOEA) is severely deteriorated when applied to many-objective problems. For Pareto dominance based techniques, available information about optimal solutions can be used to improve their performance. This is the case of corner solutions.
Hélio Freire +3 more
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Ranking-Dominance and Many-Objective Optimization
2007 IEEE Congress on Evolutionary Computation, 2007An alternative relation to Pareto-dominance is studied. The relation is based on ranking a set of solutions according to each separate objective and an aggregation function to calculate a scalar fitness value for each solution. The relation is called as ranking-dominance and it tries to tackle the curse of dimensionality commonly observed in multi ...
null Saku Kukkonen, null Jouni Lampinen
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Age-Layered Strategies for Many-Objective Optimization
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020Many-objective optimization problems (MaOPs) are multi-objective problems that have four or more objectives. MaOPs face significant challenges because of search inefficiency, computational cost, decision making, and visualization. Most MaOP systems use variants of non-dominated sorting (Pareto ranking). However, Pareto dominance is ineffective when the
Arpi Sen Gupta, Brian J. Ross
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Ranking Methods for Many-Objective Optimization
2009An important issue with Evolutionary Algorithms (EAs) is the way to identify the best solutions in order to guide the search process. Fitness comparisons among solutions in single-objective optimization is straightforward, but when dealing with multiple objectives, it becomes a non-trivial task.
Mario Garza-Fabre +2 more
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Generalization of Pareto-Optimality for Many-Objective Evolutionary Optimization
IEEE Transactions on Evolutionary Computation, 2016The vast majority of multiobjective evolutionary algorithms presented to date are Pareto-based. Usually, these algorithms perform well for problems with few (two or three) objectives. However, due to the poor discriminability of Pareto-optimality in many-objective spaces (typically four or more objectives), their effectiveness deteriorates ...
Chenwen Zhu, Lihong Xu, Erik D. Goodman
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