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The idea of decomposition is becoming increasingly successful and popular in evolutionary multi-objective optimization. An efficient cone decomposition approach was further developed in the conical area evolutionary algorithm (CAEA). This approach improves the runtime efficiency and population diversity of decomposition-based algorithms effectively for bi-objective optimization in practice. In this...
On the basis of MOEA/D-DE algorithm, the introduction of the concept of a self-adaptive differential evolution algorithm, named self-adaptive multi-objective differential evolutionary algorithm based on decomposition (SAMODEA/D). This algorithm enhances the local search algorithm in the case of maintaining the diversity of the population, while effectively avoids “premature convergence” and the occurrence...
Achieving balance between convergence and diversity is a key issue in evolutionary multiobjective optimization. Most existing methodologies, which have demonstrated their niche on various practical problems involving two and three objectives, face significant challenges in many-objective optimization. This paper suggests a unified paradigm, which combines dominance- and decomposition-based approaches,...
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