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Hybrid non-dominated sorting genetic algorithm with adaptive operators selection

Khan Mashwani, Wali and Salhi, Abdellah and Yeniay, Ozgur and Hussian, H and Jan, MA (2017) 'Hybrid non-dominated sorting genetic algorithm with adaptive operators selection.' Applied Soft Computing, 56. pp. 1-18. ISSN 1568-4946

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Multiobjective optimization entails minimizing or maximizing multiple objective functions subject to a set of constraints. Many real world applications can be formulated as multi-objective optimization problems (MOPs), which often involve multiple conflicting objectives to be optimized simultaneously. Recently, a number of multi-objective evolutionary algorithms (MOEAs) were developed suggested for these MOPs as they do not require problem specific information. They find a set of non-dominated solutions in a single run. The evolutionary process on which they are based, typically relies on a single genetic operator. Here, we suggest an algorithm which uses a basket of search operators. This is because it is never easy to choose the most suitable operator for a given problem. The novel hybrid non-dominated sorting genetic algorithm (HNSGA) introduced here in this paper and tested on the ZDT (Zitzler-Deb-Thiele) and CEC’09 (2009 IEEE Conference on Evolutionary Computations) benchmark problems specifically formulated for MOEAs. Numerical results prove that the proposed algorithm is competitive with state-of-the-art MOEAs.

Item Type: Article
Uncontrolled Keywords: Multiobjective optimization; Evolutionary computation; Multiobjective evolutionary algorithms (MOEAs); Pareto optimality; Adaptive operator selection
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 10 Mar 2017 14:53
Last Modified: 18 Aug 2022 10:45

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