| Summary: | 碩士 === 國立暨南國際大學 === 資訊管理學系 === 100 === MOEA/D is an emerging method for tackling multi-objective optimization problem (MOOP). Using the concept of decomposition, MOEA/D improves the multi-objective evolutionary algorithm (MOEA) by decomposing an MOOP into a number of single-objective sub-problems. MOEA/D develops a group of candidate solutions by using a set of weight vectors uniformly spread in the objective space. Then, MOEA/D optimizes each sub-problem by reference to corresponding candidate solutions. In this paper we propose adaptive memory programming strategies based on the concept of slop reference and projection reference. On the one hand, slop reference update uses the line connecting the origin and reference points in object space to instruct the direction of evolution. On the other hand, projection reference update implements the decomposition using the ɛ-constraint programming technique which optimizes the objective function in a single object while the remaining objects are constrained by a small ɛ value. Furthermore, we employed the Path-relinking strategy to improve the diversity of the solution front. Experimental results show that our methods perform better than the traditional MOEA/D on seven benchmark functions widely used in the MOOP literature.
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