Abstract
There are various problems in the engineering field which can be modeled as an optimization problem and can be solved through metaheuristic optimization algorithms. In this paper, a new metaheuristics algorithm named as Fission Fusion Behavior-based Rao Algorithm (FFBBRA) has been proposed. This new algorithm utilizes the concepts of metaphor-less Rao algorithm and the idea of fission–fusion social behavior of Spider Monkey Optimization (SMO). The proposed methodology divides the entire population into subpopulations and then these subpopulations are independently updated concerning their local worst and global best candidates using the FFBBRA equations. The proposed method has been tested over 20 unconstrained functions including multimodal, unimodal, and fixed dimensions benchmark functions along with 2 constrained benchmark functions. The performance of the proposed algorithm is evaluated through observations of the worst solution, the best solution, standard deviation, and mean solution. Convergence analysis is also performed. The proposed FFBBRA has shown good performance as observed by Friedman Test. This proposed algorithm is also easily utilized on parallel systems due to its parallel working nature.
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Pawar, S., Ahirwal, M.K. A new fission fusion behavior-based Rao algorithm (FFBBRA) for solving optimization problems. Evol. Intel. 16, 1309–1323 (2023). https://doi.org/10.1007/s12065-022-00741-y
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DOI: https://doi.org/10.1007/s12065-022-00741-y