Battle Royale Optimizer (BRO) is a recently proposed optimization algorithm that has added a new category named game-based optimization algorithms to the existing categorization of optimization algorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of BRO (MOBRO). Although there are some multi-objective optimization algorithms in the literature, according to the no-free-lunch theorem, no optimization algorithm can efficiently solve all optimization problems. We applied the proposed algorithm to four benchmark datasets: CEC 2009, CEC 2018, ZDT, and DTLZ. We measured the performance of MOBRO based on three aspects: convergence, spread, and distribution, using three performance criteria: inverted generational distance, maximum spread, and spacing. We also compared its obtained results with those of three state-of-the-art optimization algorithms: the multi-objective Gray Wolf optimization algorithm (MOGWO), the multi-objective particle swarm optimization algorithm (MOPSO), the multi-objective artificial vulture's optimization algorithm (MOAVAO), the optimization algorithm for multi-objective problems (MAOA), and the multi-objective non-dominated sorting genetic algorithm III (NSGA-III). The obtained results approve that MOBRO outperforms the existing optimization algorithms in most of the benchmark suites and operates competitively with them in the others.
Eser Adı (dc.title) | MOBRO: multi-objective battle royale optimizer |
Yazar (dc.contributor.author) | Taymaz Akan |
Yayın Yılı (dc.date.issued) | 2023 |
Tür (dc.type) | Makale |
Özet (dc.description.abstract) | Battle Royale Optimizer (BRO) is a recently proposed optimization algorithm that has added a new category named game-based optimization algorithms to the existing categorization of optimization algorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of BRO (MOBRO). Although there are some multi-objective optimization algorithms in the literature, according to the no-free-lunch theorem, no optimization algorithm can efficiently solve all optimization problems. We applied the proposed algorithm to four benchmark datasets: CEC 2009, CEC 2018, ZDT, and DTLZ. We measured the performance of MOBRO based on three aspects: convergence, spread, and distribution, using three performance criteria: inverted generational distance, maximum spread, and spacing. We also compared its obtained results with those of three state-of-the-art optimization algorithms: the multi-objective Gray Wolf optimization algorithm (MOGWO), the multi-objective particle swarm optimization algorithm (MOPSO), the multi-objective artificial vulture's optimization algorithm (MOAVAO), the optimization algorithm for multi-objective problems (MAOA), and the multi-objective non-dominated sorting genetic algorithm III (NSGA-III). The obtained results approve that MOBRO outperforms the existing optimization algorithms in most of the benchmark suites and operates competitively with them in the others. |
Açık Erişim Tarihi (dc.date.available) | 2023-10-31 |
Yayıncı (dc.publisher) | SPRINGER |
Dil (dc.language.iso) | En |
Konu Başlıkları (dc.subject) | Optimization |
Konu Başlıkları (dc.subject) | Battle-royale-game-based optimization algorithms |
Konu Başlıkları (dc.subject) | Battle royale optimization algorithm |
Konu Başlıkları (dc.subject) | Multi-objective problems |
Tek Biçim Adres (dc.identifier.uri) | https://hdl.handle.net/20.500.14081/1935 |
ISSN (dc.identifier.issn) | 0920-8542 |
Dergi (dc.relation.journal) | JOURNAL OF SUPERCOMPUTING |
Esere Katkı Sağlayan (dc.contributor.other) | Akan, Taymaz |
Esere Katkı Sağlayan (dc.contributor.other) | Alp, Sait |
Esere Katkı Sağlayan (dc.contributor.other) | Dehkharghani Rahim |
Esere Katkı Sağlayan (dc.contributor.other) | Bhuiyan, Mohammad A. N. |
DOI (dc.identifier.doi) | 10.1007/s11227-023-05676-4 |
Orcid (dc.identifier.orcid) | 0000-0003-4070-1058 |
wosquality (dc.identifier.wosquality) | Q1 |
wosauthorid (dc.contributor.wosauthorid) | JLV-1318-2023 |
Department (dc.contributor.department) | Yazılım Mühendisliği |
Wos No (dc.identifier.wos) | WOS:001084223600005 |
Veritabanları (dc.source.platform) | Wos |
Veritabanları (dc.source.platform) | Scopus |