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marine predator algorithm and Related Variants: A Systematic Review

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 544-568页

作者： Philibus, Emmanuel Zain, Azlan Mohd Prasetya, Didik Dwi Bahari, Ahadi bin Yusup, Norfadzlan Jalil, Rozita Abdul Majid, Mazlina Abdul Samah, Azurah A. Univ Teknol Malaysia Dept Comp Sci Johor Baharu Malaysia Kaduna State Coll Educ Dept Comp Sci Kafanchan Nigeria State Univ Malang Dept Elect Engn & Informat Malang Indonesia Univ Teknol Malaysia Dept Informat Syst Johor Baharu Malaysia Univ Malaysia Sarawak Dept Software Engn Kota Samarahan Malaysia Univ Tun Hussein Onn Malaysia Dept Software Engn Batu Pahat Malaysia Univ Malaysia Pahang Al Sultan Abdullah Ctr Artificial Intelligence & Data Sci Kuantan Malaysia

marine predators algorithm (MPA) is classified under swarm intelligence methods based on its type of inspiration. It is a population-based metaheuristic optimization algorithm inspired by the general foraging behavior exhibited in the form of Levy and Brownian motion in ocean predators supported by the policy of optimum success rate found in the biological relationship between prey and predators. The algorithm is easy to implement and robust in searching, yielding better solutions to many real-world problems. It is attracting huge and growing interest. This paper provides a systematic review of the research progress and applications of the MPA by analyzing more than 100 articles sourced from Scopus and Web of Science databases using the PRISMA approach. The study expounded the classical MPA's workflow. It also unveiled a steady upward trend in the use of the algorithm. The research presented different improvements and variants of MPA including parameter-tuning, enhancement of the balance between exploration and exploitation, hybridization of MPA with other techniques to harness the strengths of each of the algorithms towards complementing the weaknesses of the other, and more recently proposed advances. It further underscores the application of MPA in various areas such as Engineering, Computer Science, Mathematics, and Energy. Findings reveal several search strategies implemented to improve the algorithm's performance. In conclusion, although MPA has been widely accepted, other areas remain yet to be applied, and some improvements are yet to be covered. These have been presented as recommendations for future research direction.

关键词： Exploitation-exploration marine predator algorithm metaheuristic algorithms metaheuristic-hybridization meta-heuristics optimization predator prey systems

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Real-time adaptable fault analysis of rotating machines based on marine predator algorithm optimised LightGBM approach

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NONDESTRUCTIVE TESTING AND EVALUATION 2025年第3期40卷 831-866页

作者： Bagci Das, Duygu Ege Univ Dept Comp Programming Izmir Turkiye Univ Massachusetts Lowell Dept Mech Engn Lowell MA 01854 USA

Intelligent fault diagnosis (IFD) techniques are important for fault analysis. It is challenging to constitute a real-time adaptable technique that can handle different types of fault conditions and fault severity values under variable machine settings. Based on this challenge, this study proposes an effective approach called marine predator algorithm optimised Light Gradient Boosting Machine (MPA-LightGBM) for fault diagnosis and severity analysis of rotating machines regarding both single and compound faults. For the fault diagnosis, the MPA-LightGBM classifier is employed. The suitability of the proposed approach for real-world applications is validated by performing a real-time analysis using unseen data collected from a rotating machine operated at different rotational speeds. The computational load is alleviated by eliminating the redundant features up to 85.00% via MPA. According to experimental findings, the suggested method was able to accurately identify the individual cases for the testing set by 99.59% and 94.88% for compound faults. Regarding real-time analysis, the identification performance is 99.48% for individual faults while it is 92.33% for compound faults. The fault severity prediction error of the proposed approach considering the testing set is 0.55% and 1.15% for the individual and compound faults, while it is 0.50% and 1.88% regarding the real-time analysis.

关键词： Fault diagnosis rotating machine machine learning marine predator algorithm LightGBM

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Wireless Sensor Network Coverage Optimization Using a Modified marine predator algorithm

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SENSORS 2025年第1期25卷 69-69页

作者： Wang, Guohao Li, Xun Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China

To solve the coverage problem caused by the random deployment of wireless sensor network nodes in the forest fire-monitoring system, a modified marine predator algorithm (MMPA) is proposed. Four modifications have been made based on the standard marine predator algorithm (MPA). Firstly, tent mapping is integrated into the initialization step to improve the searching ability of the early stage. Secondly, a hybrid search strategy is used to enhance the ability to search and jump out of local optimum. Thirdly, the golden sine guiding mechanism is applied to accelerate the convergence of the algorithm. Finally, a stage-adjustment strategy is proposed to make the transition of stages more smoothly. Six specific test functions chosen from the CEC2017 function and the benchmark function are used to evaluate the performance of MMPA. It shows that this modified algorithm has good optimization capability and stability compared to MPA, grey wolf optimizer, sine cosine algorithm, and sea horse optimizer. The results of coverage tests show that MMPA has a better uniformity of node distribution compared to MPA. The average coverage rates of MMPA are the highest compared to the commonly used metaheuristic-based algorithms, which are 91.8% in scenario 1, 95.98% in scenario 2, and 93.88% in scenario 3, respectively. This demonstrates the superiority of this proposed algorithm in coverage optimization of the wireless sensor network.

关键词： wireless sensor network marine predator algorithm coverage optimization

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A novel marine predator algorithm for path planning of UAVs

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-34页

作者： Gong, Rong Gong, Huaming Hong, Lila Li, Tanghui Xiang, Changcheng ABA Teachers Coll Dept Comp Sci Wenchuan 623002 Peoples R China ABA Teachers Coll Virtualizat & Big Data Lab Wenchuan 623002 Peoples R China Guizhou Police Coll Adm Off Teaching Affairs Guiyang 550005 Peoples R China

Currently, unmanned aerial vehicle (UAV) technology is widely employed across various industries owing to its inherent advantages. In terms of UAV technology, exploring and optimizing path planning for UAVs occupy a prominent research position. Thus, a constrained optimization model for the UAV path planning was developed, and then, the marine predator algorithm (MPA) was applied to effectively solve this model. Nevertheless, the MPA encounters limitations, including the tendency to become trapped in local optima and suffer from premature convergence. Therefore, a modified version of MPA, which is called MMPA, was developed. Firstly, circle chaotic mapping is introduced into MPA to address non-uniform initial search agents' distribution in the algorithm. Secondly, the neighborhood perturbation strategy is introduced to bolster MPA's performance, enabling it to escape from local optima. Thirdly, in the later iterations of MPA, the lens-imaging-based learning strategy is implemented as a means to enrich search agents' diversity and further improve the algorithm's optimization capabilities. From the experimental reports, it is known that the performance of MMPA is better than that of the comparison algorithm, both in the benchmark functions and in UAV path planning. When it comes to path planning, the routes generated by MMPA are smoother and safer than those generated by the comparison algorithm.

关键词： marine predator algorithm Path planning Neighborhood perturbation strategy Lens-imaging-based learning UAV

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LEDs placement in indoor VLC system based on an improved marine predator algorithm

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JOURNAL OF OPTICS-INDIA 2025年 1-34页

作者： Benayad, Abdelbaki Boustil, Amel Meraihi, Yassine Mirjalili, Seyedali Yahia, Selma Mekhmoukh, Sylia Saadi, Amylia Ait MHamed Bougara Univ Boumerdes LIMOSE Lab Independence Ave Boumerdes 35000 Algeria Univ MHamed Bougara Boumerdes LIST Lab Ave Independence Boumerdes 35000 Algeria Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Fortitude Valley Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Paris Saclay LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France

Visible Light Communication (VLC) is a promising and emerging technology that allows both illumination and communication between users using Light Emitting Diodes (LEDs) as transmitters and Photo-Detectors (PDs) as receivers. In indoor VLC systems, the deployment of various number of LEDs with various number of users is a challenging issue. This issue is considered to be an NP-Hard problem, so approximation approaches, especially meta-heuristics, are appropriate to solve it. In this paper, we propose a hybrid swarm intelligence approach (IMPA-FA) based on the combination of the Improved marine predators algorithm (IMPA) with Firefly algorithm (FA) for solving the LEDs placement problem in an indoor VLC system. The effectiveness of the proposed IMPA-FA is tested on several scenarios under different settings, taking into account the throughput and user coverage metrics. Simulation results demonstrate the accuracy and superiority of the IMPA-FA approach in finding optimal LEDs positions when compared with the standard MPA, FA, Particle Swarm Optimization (PSO), Genetic algorithm (GA), Coronavirus Herd Immunity Optimizer (CHIO), Whale Optimization algorithm (WOA), Manta Ray Foraging Optimization (MRFO), Bat algorithm (BAT), and Grey Wolf Optimizer (GWO).

关键词： LEDs placement problem Visible light communications marine predator algorithm Optimization

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marine predator algorithm-based Sliding Mode Control of a Novel Motion Simulator for High Column Sloshing Experiments

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船舶力学 2024年第12期28卷 1835-1848页

作者： DU Zun-feng CHEN Xiang-yu BAI Hao ZHU Hai-ming HAN Mu-xuan State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation School of Civil EngineeringTianjin UniversityTianjin 300350China Power China Northwest Engineering Corporation Limited Xi’an 710065China Nanhai Engineering Branch CNOOC Energy Development Equipment Technology Co.Ltd.Zhanjiang 524057China

Sloshing experiment is crucial to determine the reaction performance of regeneration columns on an offshore floating platform.A novel type of column motion simulating device and a marine predator algorithm-based Sliding Mode Controller(MPA-SMC)are proposed for such sloshing *** simulator consists of a Stewart platform and a steel *** Stewart platform is located at the column's center of gravity(CoG)and supported by the steel *** platform's hydraulic servo system is controlled by a sliding mode controller with parameters optimized by MPA to improve robustness and precision.A numerical sloshing experiment is conducted using the proposed device and *** results show that the novel motion simulator has lower torque during the column sloshes,and the proposed controller performs better than a well-tuned PID controller in terms of target tracking precision and anti-interference capability.

关键词： regeneration column sloshing experiment motion simulator Stewart platform sliding mode control marine predator algorithm

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Load Voltage Balancing Using marine predator algorithm for Power System Quality Improvement

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第1期19卷 73-82页

作者： Anitha, P. Kamaraja, A. S. Kumar, K. Karthik Bhuvanesh, A. Univ VOC Coll Engn Dept Elect & Elect Engn Thoothukudi Tamil Nadu India Kalasalingam Acad Res & Educ Dept Elect & Elect Engn Krishnankoil India PSN Coll Engn & Technol Dept Elect & Elect Engn Tirunelveli Tamil Nadu India Natl Engn Coll Dept Elect & Elect Engn Kovilpatti Tamil Nadu India

This paper presents an intelligent control strategy-based DVR unit integrated with a solar Photovoltaic system. The controlling of DVR voltage injection is done by marine predator algorithm-based Interval Type -2 Fuzzy Logic Controller (MPA-IT2FC). In this proposed method, the IT2FC is used to tune the gains of the PI controller. Also, the marine predator algorithm is used to control the parameter of the fuzzy membership function. Here, the proposed control method is employed on a Synchronous Reference Frame theory basis. The simulations are performed using the MATLAB/Simulink tool. To demonstrate the effectiveness of the proposed DVR, which is compared with the existing techniques such as traditional PI controller, Fuzzy Logic controller (FLC), and Grasshopper optimization algorithm-PID (GOA-PID) controller-based DVR. The proposed DVR outperforms the existing techniques in terms of efficient compensation of PQ issues. The THD value of the proposed DVR system is 3.85%, 2.81%, and 1.08% less than the PI, FLC, and GOA-PID controller-based DVR.

关键词： Voltage injection DVR regulation Synchronous reference frame marine predator algorithm Interval type-2 fuzzy logic controller

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Task scheduling in cloud computing environment based on enhanced marine predator algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第1期27卷 1109-1123页

作者： Gong, Rong Li, DeLun Hong, LiLa Xie, NingXin Guangxi Minzu Univ Sch Artificial Intelligence Nanning 530006 Guangxi Peoples R China Guangxi Minzu Univ Coll Elect Informat Nanning 530006 Guangxi Peoples R China

Cloud computing has experienced extraordinary development across a wide range of industries by giving customers the flexibility to employ computing resources as needed. The task scheduling problem is one of several major challenges in cloud computing, and it should be scheduled effectively to minimize makespan and maximize resource utilization. Therefore, this paper put forward an improved scheduling efficiency algorithm called Enhanced marine predator algorithm (EMPA). Firstly, task scheduling model with makespan and resource utilization is constructed. Secondly, each individual represents a result of task scheduling, and the purpose of algorithms is to find the optimal scheduling result, therefore the operator of WOA, nonlinear inertia weight coefficient and golden sine strategy are introduced into marine predator algorithm. In the simulation experiment, EMPA is compared with Grey Wolf Optimizer (GWO), Sine Cosine algorithm (SCA), Particle Swarm Optimization (PSO), and Whale Optimization algorithm (WOA) under different number of tasks in synthetic datasets and GoCJ *** experimental results show that the EMPA algorithm has more advantages in terms of makespan, degree of imbalance, and resource utilization.

关键词： marine predator algorithm Task scheduling Cloud computing Meta-heuristic Golden sine strategy

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Enhancing rotor angle stability of power systems using marine predator algorithm based cascaded PID control

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AIN SHAMS ENGINEERING JOURNAL 2021年第2期12卷 1849-1857页

作者： Yakout, Ahmed Sabry, Waheed Hasanien, Hany M. Ain Shams Univ Fac Engn Cairo Egypt Mil Tech Coll Cairo Egypt Future Univ Egypt Fac Engn & Technol Elect Engn Dept Cairo Egypt

In this paper, the cascaded control scheme is introduced for the first time to the rotor stability problem. Cascaded excitation and governing control schemes are introduced to damp rotor oscillations. PID cascaded control is used for its popularity and effectiveness. These controllers will be fine-tuned using a novel metaheuristic optimization technique, namely the marine predator algorithm;which mimics the movement of marine predators when foraging. This technique is popular for always converging to a constant stable point. Our study will be performed on two systems, the simple single machine to infinite bus power system and the popular IEEE-39 bus system to study both local and global oscillations, respectively. Results show the effectiveness of the new proposed cascaded control scheme compared to the conventional scheme in improving the damping of the rotor oscillations without the need of a power system stabilizer. Results are provided as time domain simulation via MATLAB/SIMULINK. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, AM Shams University.

关键词： Rotor angle stability marine predator algorithm Cascaded PID control

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A Multi-Strategy Enhanced marine predator algorithm for Global Optimization and UAV Swarm Path Planning

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IEEE ACCESS 2024年 12卷 112095-112115页

作者： Gu, Gaoquan Li, Haitao Zhao, Cunsheng Naval Univ Engn Coll Naval Architecture & Ocean Engn Wuhan 430033 Peoples R China

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, the path planning problem in UAV cooperative operations has become increasingly prominent. This paper studies the path planning problem of UAV swarms, aiming to propose an effective method to improve the efficiency and safety of task execution. The marine predator algorithm (MPA) is a popular optimization algorithm known for its high performance. However, it sometimes encounters issues such as low efficiency in spiral flight and slow predator speed, leading to slow convergence and susceptibility to local optima. To address these limitations, this paper proposes the Multi-strategy Enhanced marine predator algorithm (MEMPA). The four enhancement strategies are: random spiral flight strategy, crisscross strategy, centroid boundary control strategy, and improved update rules for eddy formation and the FADs' effect. These strategies collectively enhance the algorithm's global search ability and its capacity to escape local optima. Compared with FDB-PPSO, AGWO, HPHHO, LSHADE, LSHADE-cnEpSin, MadDE, NMPA, QQLMPA, and the original MPA across four different dimensions of the 30 CEC2017 test functions, the Wilcoxon rank sum test results show that MEMPA outperforms the original MPA by 21, 19, 22, and 25 on the four dimensions, respectively. According to the results of the Friedman mean rank test, the top three algorithms are LSHADE, MPA, and MEMPA, with scores of 2.91, 2.77, and 2.14, respectively. Additionally, the proposed MEMPA was used to solve the UAV swarm path planning problem. The results show that the solutions obtained by MEMPA are 10%, 10%, and 13% better than those obtained by MPA, NMPA, and QQLMPA, respectively. It is superior to the selected comparison algorithms in terms of solution effectiveness and stability, demonstrating its potential in practical applications.

关键词： marine predator algorithm centroid boundary control strategy spiral flight strategy global optimization UAV swarm path planning marine predator algorithm centroid boundary control strategy spiral flight strategy global optimization UAV swarm path planning

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