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An Open Framework for Constructing Continuous Optimization Problems

Li, C, Nguyen, TT, Wu, M, Yang, M and Zeng, S (2018) An Open Framework for Constructing Continuous Optimization Problems. IEEE Transactions on Cybernetics. ISSN 2168-2275

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Many artificial benchmark problems have been proposed for different kinds of continuous optimization, e.g., global optimization, multi-modal optimization, multi-objective optimization, dynamic optimization, and constrained optimization. However, there is no unified framework for constructing these types of problems and possible properties of many problems are not fully tunable. This will cause difficulties for researchers to analyze strengths and weaknesses of an algorithm. To address these issues, this paper proposes a simple and intuitive framework, which is able to construct different kinds of problems for continuous optimization. The framework utilizes the k-d tree to partition the search space and sets a certain number of simple functions in each subspace. The framework is implemented into global/multimodal optimization, dynamic single objective optimization, multiobjective optimization, and dynamic multi-objective optimization, respectively. Properties of the proposed framework are discussed and verified with traditional evolutionary algorithms.

Item Type: Article
Subjects: T Technology > T Technology (General)
Divisions: Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher: IEEE
Date Deposited: 10 Apr 2018 11:03
Last Modified: 04 Sep 2021 10:35
DOI or ID number: 10.1109/TCYB.2018.2825343
URI: https://researchonline.ljmu.ac.uk/id/eprint/8434
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