Abstract
This paper proposed a new adaptive control mechanism of aggregation functions (scalarizing functions) in MOEA/D, “ADaptive control of Aggregation function dePending on a search condiTion (ADAPT)”. Although MOEA/D has been well known as one of the most powerful EMO algorithms, it hasn’t been resolved which aggregation function should be choose. It is strongly depended on characteristics of the problem which aggregation function of MOEA/D is best suited and very difficult to predict which one is best suited in advance. Our proposed ADAPT changes adaptively an aggregation function of MOEA/D according to the search condition. ADAPT uses multiple aggregation functions and multiple archives corresponding to each aggregation function. The important points of ADAPT is that the number of function calls is same as that of original MOEA/D.
In numerical examples, the characteristics and effectiveness of ADAPT were verified by comparing the performance of ADAPT with that of original MOEA/D (using a fixed aggregation function). The results of experiments indicated that ADAPT could obtain the solutions as same quality as that of original MOEA/D with the best suited aggregation function.
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Watanabe, S., Sato, T. (2017). Study of an Adaptive Control of Aggregate Functions in MOEA/D. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_26
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DOI: https://doi.org/10.1007/978-3-319-68759-9_26
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