Dehkharghani, Rahim

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Dehkharghani, Rahim
Dehkharghani,Rahim
Rahim, Dehkharghani
Rahim Dehkharghani
Dehkharghani, RAHIM
D., Rahim
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Dehkharghani, R.
Rahim DEHKHARGHANI
RAHIM DEHKHARGHANI
DEHKHARGHANI, RAHIM
R. Dehkharghani
Dehkharghani,R.
DEHKHARGHANI, Rahim
Job Title
Dr. Öğr. Üyesi
Email Address
rahim.dehkharghani@khas.edu.tr
Main Affiliation
Computer Engineering
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ORCID ID
0000-0002-9619-8247
Scopus Author ID
24528505600
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Google Scholar ID
WoS Researcher ID
JXY-0317-2024
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Author: Bhuiyan,M.A.N.

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    Citation - Scopus: 1
    MOBRO: multi-objective battle royale optimizer
    (Springer, 2024) Dehkharghani, Rahim; Dehkharghani,R.; Akan,T.; Bhuiyan,M.A.N.
    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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.