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. DOI https://doi.org/10.1016/j.asoc.2017.01.056
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. DOI https://doi.org/10.1016/j.asoc.2017.01.056
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. DOI https://doi.org/10.1016/j.asoc.2017.01.056
Abstract
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 |
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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 > Mathematics, Statistics and Actuarial Science, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 10 Mar 2017 14:53 |
Last Modified: | 30 Oct 2024 19:18 |
URI: | http://repository.essex.ac.uk/id/eprint/19322 |
Available files
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