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Cellular teaching-learning-based optimization approach for dynamic multi-objective problems

KNOWLEDGE-based SYSTEMS 2018年 141卷 148-177页

作者： Birashk, Amin Kordestani, Javidan Kazemi Meybodi, Mohammad Reza Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran Amirkabir Univ Technol Tehran Polytech Comp Engn & Informat Technol Dept Soft Comp Lab 424 Hafez Ave Tehran Iran

Many real-world optimization problems involve several conflicting objectives that must be optimized simultaneously. Furthermore, most optimization problems have a dynamic structure and change over time. In addition to trying to establish trade-offs among conflicting objectives and explore a diverse set of solutions on a Pareto-optimal front, a dynamic multi-objective optimization (DMOO) algorithm tries to detect changes and track them, using the knowledge of prior environments to converge to the new Pareto-optimal front more quickly. In this paper, a cellular automata-based approach is first proposed for managing and evaluating solutions during the optimization process. Then, using the above approach and the teaching-learning-based optimization algorithm, two new algorithms are introduced for DMOO problems. The first algorithm works to optimize the objectives all at once in a multi-objective manner, while the second algorithm uses the vector evaluated technique to evolve solutions in collaborative single-objective optimization units, and then analyzes them from a multi-objective perspective. These algorithms have been evaluated and compared with some other DMOO algorithms for some standard benchmark problems. The results indicate their superiority in many of the experiments. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Dynamic multi-objective optimization problems multi-objective teaching-learning-based optimization TLBO Vector evaluated

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