Abstract
In the real world, it often has multiple objectives (the objectives are often conflicting) in optimization problems. In many cases, a single solution is not being optimized with regards to all objectives. Dealing the problems, Multi-objective Evolutionary Algorithms (MOEAs) is known as a great potential. It is a hot trend in getting suitable solutions and making up the convergence of MOEAs, when the Decision-Maker’s (DM) consideration during the search (the interacting with a DM) to check, analyze the results, and give the preference. Recently, there are many researchers who focused on interactive methods for MOEAs, in [9], the authors proposed a multi-point methods to interactive with MOEAs, and MOEA/D is selected to build up the proposal. In [10], and its updated version in [11] based on DMEA-II [8], interactive ways with concept of rays were introduced. We found out some issues in these proposals and it raised to use a buffer instead of rays to improve the algorithm. The new method was confirmed on some experiments with popular benchmark sets.
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The work is acknowledged by MOD project with code: 2018.76.040.
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Nguyen, L., Duc, D.N., Thanh, H.N. (2020). An Enhanced Multi-point Interactive Method for Multi-objective Evolutionary Algorithms. In: Satapathy, S., Bhateja, V., Nguyen, B., Nguyen, N., Le, DN. (eds) Frontiers in Intelligent Computing: Theory and Applications. Advances in Intelligent Systems and Computing, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-32-9186-7_5
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DOI: https://doi.org/10.1007/978-981-32-9186-7_5
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