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A new proposal for multi-objective optimization using differential evolution and rough sets theory
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Comparison of MCDM and EMO Approaches in Wastewater Treatment Plan Design
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Automatic Clustering Using a Synergy of Genetic Algorithm and Multi-objective Differential Evolution
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Expert Systems with Applications: An International Journal
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Asynchronous master-slave parallelization of differential evolution for multi-objective optimization
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Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines the advantages of DE with the mechanisms of Pareto-based ranking and crowding distance sorting, used by state-of-the-art evolutionary algorithms for multiobjective optimization. DEMO is implemented in three variants that achieve competitive results on five ZDT test problems.