Marine predators algorithm: A comprehensive review

Mugemanyi, Sylvere; Qu, Zhaoyang; Rugema, François Xavier; Dong, Yunchang; Wang, Lei; Bananeza, Christophe; Nshimiyimana, Arcade; Mutabazi, Emmanuel

doi:10.1016/j.mlwa.2023.100471

Cited by 9 publications

(2 citation statements)

References 98 publications

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“…MPA has been used to solve many problems since it was first developed. Especially successful results have increased the popularity of the MPA [4,13,14]. Abd Elminaam et al [15] proposed a hybrid method based on MPA and k-Nearest Neighbors (k-NN).…”

Section: Related Workmentioning

confidence: 99%

On Performance of Marine Predators Algorithm in Training of Feed-Forward Neural Network for Identification of Nonlinear Systems

Baştemur Kaya

2023

Symmetry

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Artificial neural networks (ANNs) are used to solve many problems, such as modeling, identification, prediction, and classification. The success of ANN is directly related to the training process. Meta-heuristic algorithms are used extensively for ANN training. Within the scope of this study, a feed-forward artificial neural network (FFNN) is trained using the marine predators algorithm (MPA), one of the current meta-heuristic algorithms. Namely, this study is aimed to evaluate the performance of MPA in ANN training in detail. Identification/modeling of nonlinear systems is chosen as the problem. Six nonlinear systems are used in the applications. Some of them are static, and some are dynamic. Mean squared error (MSE) is utilized as the error metric. Effective training and testing results were obtained using MPA. The best mean error values obtained for six nonlinear systems are 2.3 × 10−4, 1.8 × 10−3, 1.0 × 10−4, 1.0 × 10−4, 1.2 × 10−5, and 2.5 × 10−4. The performance of MPA is compared with 16 meta-heuristic algorithms. The results have shown that the performance of MPA is better than other algorithms in ANN training for the identification of nonlinear systems.

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Section: Related Workmentioning

confidence: 99%

On Performance of Marine Predators Algorithm in Training of Feed-Forward Neural Network for Identification of Nonlinear Systems

Baştemur Kaya

2023

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“…The Marine Predators Algorithm (MPA) operates as a population-based algorithm, a characteristic shared with other metaheuristic algorithms [6]. One of its distinctive features involves periodically disseminating the initial solution across the search space for initial demonstration purposes, as expressed in the following equation in Eq.…”

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confidence: 99%

Improved marine predators algorithm for engineering design optimization problems

chun,

hua,

et al. 2024

Preprint

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The Marine Predators Algorithm (MPA) is recognized as one of the optimization method in population-based algorithm that mimics the foraging optimization strategy dominated by the optimal foraging theory, which encounter rate policy between predator and prey in marine ecosystems for solving optimization problems. However, MPA presents weak point towards premature convergence, stuck into local optima, lack of diversity, specifically, which is in the real-world niche problems within different industrial engineering design domains. To get rid of such limitations, this paper presents an Improved Marine Predators Algorithm (IMPA) to mitigate above mentioned limitations by deploying the self-adaptive weight and dynamic social learning mechanism that performs well and challenges tough multimodal benchmark-functions and CEC 2021 benchmark suite, compared with the state-of-the-art hybrid optimization algorithms and the recently modified MPA. The experimental results show that the IMPA outperforms with better precision attainment and better robustness due to its enjoying equalized exploration and exploitation feature over other methods. In order to provide a promising solution for industrial engineering design problems and highlight the potential of the IMPA as a useful tool for solving real-world problems. This study has implemented four highly representative engineering design problems, including Welded Beam Design, Tension/Compression Spring Design, Pressure Vessel Design and Three Bar Design. The experimental results also proved its efficiency to successfully solve the complex industrial engineering design problems.

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The study of optimal reactive power dispatch in power systems based on further improved membrane search algorithm

Lai,

Song,

Zheng

et al. 2025

Applied Energy

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Marine predators algorithm: A comprehensive review

Cited by 9 publications

References 98 publications

On Performance of Marine Predators Algorithm in Training of Feed-Forward Neural Network for Identification of Nonlinear Systems

On Performance of Marine Predators Algorithm in Training of Feed-Forward Neural Network for Identification of Nonlinear Systems

Improved marine predators algorithm for engineering design optimization problems

The study of optimal reactive power dispatch in power systems based on further improved membrane search algorithm

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