Abstract
Hybrid multi-objective evolutionary algorithms have recently become a hot topic in the domain of metaheuristics. Introducing new algorithms that inherit other algorithms' operators and structures can improve the performance of the algorithm. Here, we proposed a hybrid multi-objective algorithm based on the operators of the genetic algorithm (GA) and teaching learning-based optimization (TLBO) and the structures of reference point-based (from NSGA-III) and R2 indicators methods. The new algorithm (R2-HMTLBO) improves diversity and convergence by using NSGA-III and R2-based TLBO, respectively. Also, to enhance the algorithm performance, an elite archive is proposed. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems and compared to four state-of-the-art algorithms. IGD metric is applied for comparison, and the results reveal that the proposed R2-HMTLBO outperforms MOEA/D, MOMBI-II, and MOEA/IGD-NS significantly in 16/19 tests, 14/19 tests and 13/19 tests, respectively. Furthermore, R2-HMTLBO obtained considerably better results compared to all other algorithms in 4 test problems, although it does not outperform NSGA-III on a number of tests.
Original language | English |
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Title of host publication | ISMSI 2022 - 2022 6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence |
Publisher | Association for Computing Machinery |
Pages | 19-23 |
Number of pages | 5 |
ISBN (Electronic) | 9781450396288 |
ISBN (Print) | 9781450396288 |
DOIs | |
Publication status | Published (in print/issue) - 24 Jun 2022 |
Event | International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence - Republic South Korea, Seoul, Korea, Republic of Duration: 23 Apr 2022 → 24 Apr 2022 http://www.ismsi.org/ |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence |
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Abbreviated title | ISMSI2022 |
Country/Territory | Korea, Republic of |
City | Seoul |
Period | 23/04/22 → 24/04/22 |
Internet address |
Bibliographical note
Publisher Copyright:© 2022 Owner/Author.
Keywords
- Evolutionary algorithm
- Multi-objective optimization
- Genetic Algorithm
- Teaching Learning-based optimization
- R2 indicator
- Reference directions
- Multi-objective evolutionary algorithm (MOEA)
- Optimization algorithm
- Reference point-based method
- NSGA-III
- Teaching learning-based optimization (TLBO)