Marine Predators Algorithm: A Review

Al-Betar, Mohammed Azmi; Awadallah, Mohammed A.; Makhadmeh, Sharif Naser; Alyasseri, Zaid Abdi Alkareem; Al‐Naymat, Ghazi; Mirjalili, Seyedali

doi:10.1007/s11831-023-09912-1

Cited by 21 publications

(5 citation statements)

References 182 publications

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“…Researchers from various domains have taken note of the MPA's robustness and impressive performance, as it has demonstrated considerable efficacy in addressing a wide range of optimization problems [40]. However, similar to other optimization methods, the MPA is not without its limitations and drawbacks, which can hinder its search performance and effectiveness when applied to real-world optimization problems.…”

Section: Limitations Of Mpa Optimizationmentioning

confidence: 99%

Cross-Coupled Dynamics and MPA-Optimized Robust MIMO Control for a Compact Unmanned Underwater Vehicle

Tanveer

Ahmad

2023

JMSE

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A compact, 3-degrees-of-freedom (DoF), low-cost, remotely operated unmanned underwater vehicle (UUV), or MicroROV, is custom-designed, developed, instrumented, and interfaced with a PC for real-time data acquisition and control. The nonlinear equations of motion (EoM) are developed for the under-actuated, open-frame, cross-coupled MicroROV utilizing the Newton-Euler approach. The cross-coupling between heave and yaw motion, an important dynamic of a class of compact ROVs that is barely reported, is investigated here. This work is thus motivated towards developing an understanding of the physics of the highly coupled compact ROV and towards developing model-based stabilizing controllers. The linearized EoM aids in developing high-fidelity experimental data-driven transfer function models. The coupled heave-yaw transfer function model is improved to an auto-regressive moving average with exogenous input (ARMAX) model structure. The acquired models facilitate the use of the multi-parameter root-locus (MPRL) technique to design baseline controllers for a cross-coupled multi-input, multi-output (MIMO) MicroROV. The controller gains are further optimized by employing an innovative Marine Predator Algorithm (MPA). The robustness of the designed controllers is gauged using gain and phase margins. In addition, the real-time controllers were deployed on an onboard embedded system utilizing Simulink′s automatic C++ code generation capabilities. Finally, pool tests of the MicroROV demonstrate the efficacy of the proposed control strategy.

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Section: Limitations Of Mpa Optimizationmentioning

confidence: 99%

Cross-Coupled Dynamics and MPA-Optimized Robust MIMO Control for a Compact Unmanned Underwater Vehicle

Tanveer

Ahmad

2023

JMSE

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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%

“…This success has increased the popularity of the algorithm. Energy, power systems, networking, engineering applications, classification and clustering, feature selection, image and signal processing, maths, global optimization, and scheduling are some of the areas where MPA is used [4].…”

Section: Introductionmentioning

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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“…Membership values found in the previous layer are used in the calculation of firing strengths. Firing strength ( w i ) values are found by multiplying the membership values as in (11):…”

Section: Adaptive Network Fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…The main reason for its popularity is that it gives successful results. Since its development, the MPA has been used to solve many real-world problems [11]. Abd Elminaam et al [12] proposed an approach named MPA-KNN by hybridizing the MPA with k-Nearest Neighbors (k-NN) to evaluate dimension reduction in feature selection.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Method Based on the Marine Predators Algorithm and Adaptive Neuro-Fuzzy Inference System for the Identification of Nonlinear Systems

Baştemur Kaya

2023

Symmetry

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In this study, a hybrid method based on the marine predators algorithm (MPA) and adaptive neuro-fuzzy inference system (ANFIS) is presented to identify nonlinear systems exhibiting symmetrical or asymmetrical behavior. In other words, the antecedent and conclusion parameters of the ANFIS are adjusted by the MPA. The performance of the MPA is evaluated on eight nonlinear systems. The mean squared error is used as the error metric. Successful results were obtained on the eight systems. The best mean training error values belonging to the eight systems are 1.6 × 10−6, 3.2 × 10−3, 1.5 × 10−5, 9.2 × 10−6, 3.2 × 10−5, 2.3 × 10−3, 1.7 × 10−5, and 8.7 × 10−6. In the ANFIS training carried out to solve the related problems, the performance of the MPA was compared with the butterfly optimization algorithm, the flower pollination algorithm, moth–flame optimization, the multi-verse optimizer, the crystal structure algorithm, the dandelion optimizer, the RIME algorithm, and the salp swarm algorithm. The results have shown that the performance of the MPA mostly outperforms other algorithms in both training and testing processes.

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Marine Predators Algorithm: A Review

Cited by 21 publications

References 182 publications

Cross-Coupled Dynamics and MPA-Optimized Robust MIMO Control for a Compact Unmanned Underwater Vehicle

Cross-Coupled Dynamics and MPA-Optimized Robust MIMO Control for a Compact Unmanned Underwater Vehicle

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

A Novel Hybrid Method Based on the Marine Predators Algorithm and Adaptive Neuro-Fuzzy Inference System for the Identification of Nonlinear Systems

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