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MOEA/D in MATLAB

One of the classic approaches to deal with multi-objective optimization problems, is decomposition, which means that a multi-objective is decomposed to several (theoretically infinite) single-objective optimization problems. Decomposed objective functions, can be defined using several methods, like weighted sum of objectives and distance (or norm) of difference vector of objectives and a predefined ideal point in the objective space. However, these classic methods have limited performance for the real-world multi-objective optimization problems and usually the full set of Pareto solutions cannot be discovered.

Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is an evolutionary algorithm which decomposes the multi-objective optimization problems to several single-objective sub-problems. Each sub-problem has its own best solution ever found which is determined comparing all of solutions found by algorithm. MOEA/D is proposed by Qingfu Zhang and Hui Li, in 2007 (here).

In this post, we are going to share with you, the open source MATLAB implementation of MOEA/D. The algorithm is implemented in a structured manner and if you are familiar with MATLAB programming language, you will find it easy, to use the codes in your research projects.

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The download link of this project follows.

Implementation of Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) in MATLAB

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2 comments

  1. First I would like to thank you for sharing this information which is very useful. As a researcher in production scheduling, I am very much concerned with the application of MOEA algorithms.
    I have a simple question, are the meta-heuristics developed according to the very primitive version? Have the origin paper where the algorithm is first proposed being mentioned?
    Thank you so much.

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