Mashwani, Wali Khan and Salhi, Abdellah and Yeniay, Ozgur and Jan, Muhammad Asif and Khanum, Rasheeda Adeeb (2017) 'Hybrid adaptive evolutionary algorithm based on decomposition.' Applied Soft Computing, 57. 363 - 378. ISSN 1568-4946
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Abstract
The performance of search operators varies across the different stages of the search/optimization process of evolutionary algorithms (EAs). In general, a single search operator may not do well in all these stages when dealing with different optimization and search problems. To mitigate this, adaptive search operator schemes have been introduced. The idea is that when a search operator hits a difficult patch (under-performs) in the search space, the EA scheme “reacts” to that by potentially calling upon a different search operator. Hence, several multiple-search operator schemes have been proposed and employed within EA. In this paper, a hybrid adaptive evolutionary algorithm based on decomposition (HAEA/D) that employs four different crossover operators is suggested. Its performance has been evaluated on the well-known IEEE CEC’09 test instances. HAEA/D has generated promising results which compare well against several well-known algorithms including MOEA/D, on a number of metrics such as the inverted generational distance (IGD), the hyper-volume, the Gamma and Delta functions. These results are included and discussed in this paper.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Multi-objective optimization, Adaptive operator selection, MOEA, MOEA/D |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Science and Health > Mathematical Sciences, Department of |
| Depositing User: | Jim Jamieson |
| Date Deposited: | 18 Apr 2017 09:06 |
| Last Modified: | 21 Sep 2018 14:15 |
| URI: | http://repository.essex.ac.uk/id/eprint/19475 |
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