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Multi-strategy enhanced marine predators algorithm with applications in engineering optimization and feature selection problems

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APPLIED SOFT COMPUTING 2024年 159卷

作者： Rezaei, Kamran Fard, Omid Solaymani Ferdowsi Univ Mashhad Fac Math Sci Dept Appl Math Mashhad Iran

This paper introduces a novel meta -heuristic optimization algorithm named Clustering Wavelet Oppositionbased marine predators algorithm (CWOMPA) to address some limitations present in the well -established marine predators algorithm (MPA). CWOMPA incorporates three key strategies: a fuzzy clustering approach for escaping local optima, using wavelet basis function -based impact coefficient adjustment for elites to prevent premature convergence, and finally an adaptive opposition -based learning strategy for maintaining population diversity. Compared with some recent meta -heuristic algorithms, extensive evaluations conducted affirm that CWOMPA achieves the best Friedman rank, 4.30 and 1.95 respectively, on 23 benchmark functions and the CEC 2017 benchmark set. Not only does CWOMPA demonstrate significant effectiveness on six constrained problems from the CEC 2020 real -world benchmarks, but also when applied to 14 medical datasets, it gains superior Friedman ranks in terms of selected features, classification accuracy, F -Score, and objective function value, 2.5, 2.25, 2.96, 2.93 respectively, outperforming other common methods. Finally, CWOMPA exhibits the highest classification accuracy across all datasets and the best F -Score performance in nine datasets compared to traditional feature selection algorithms. The obtained results reveal that CWOMPA is a powerful and versatile optimization algorithm with significant potential in various real -world applications, including feature selection for medical datasets.

关键词： marine predators algorithm Fuzzy clustering Wavelet basis function Opposition-based learning Constrained optimization problems

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Boosting aquila optimizer by marine predators algorithm for combinatorial optimization

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第2期11卷 37-69页

作者： Wang, Shuang Jia, Heming Hussien, Abdelazim G. Abualigah, Laith Lin, Guanjun Wei, Hongwei Lin, Zhenheng Dhal, Krishna Gopal Putian Univ New Engn Ind Coll Putian 351100 Fujian Peoples R China Fujian Prov Univ Engn Res Ctr Big Data Applicat Private Hlth Med Putian 351100 Fujian Peoples R China Sanming Univ Coll Informat Engn Sanming 365004 Fujian Peoples R China Fayoum Univ Fac Sci Fayyum 63514 Egypt Linkoping Univ Dept Comp & Informat Sci SE-58183 Linkoping Sweden Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Tabuk 71491 Saudi Arabia Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Yuan Ze Univ Coll Engn Taoyuan 32003 Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Midnapore Coll Autonomous Dept Comp Sci & Applicat Midnapore W Bengal India

In this study, an improved version of aquila optimizer (AO) known as EHAOMPA has been developed by using the marine predators algorithm (MPA). MPA is a recent and well-behaved optimizer with a unique memory saving and fish aggregating devices mechanism. At the same time, it suffers from various defects such as inadequate global search, sluggish convergence, and stagnation of local optima. However, AO has contented robust global exploration capability, fast convergence speed, and high search efficiency. Thus, the proposed EHAOMPA aims to complement the shortcomings of AO and MPA while bringing new features. Specifically, the representative-based hunting technique is incorporated into the exploration stage to enhance population diversity. At the same time, random opposition-based learning is introduced into the exploitation stage to prevent the optimizer from sticking to local optima. This study tests the performance of EHAOMPA's on 23 standard mathematical benchmark functions, 29 complex test functions from the CEC2017 test suite, six constrained industrial engineering design problems, and a convolutional neural network hyperparameter (CNN-hyperparameter) optimization for Corona Virus Disease 19 (COVID-19) computed tomography-image detection problem. EHAOMPA is compared with four existing optimization algorithm types, achieving the best performance on both numerical and practical issues. Compared with other methods, the test function results demonstrate that EHAOMPA exhibits a more potent global search capability, a higher convergence rate, increased accuracy, and an improved ability to avoid local optima. The excellent experimental results in practical problems indicate that the developed EHAOMPA has great potential in solving real-world optimization problems. The combination of multiple strategies can effectively improve the performance of the algorithm. The source code of the EHAOMPA is publicly available at https://***/WangShuang92/EHAOMPA. Graphical Abstr

关键词： aquila optimizer marine predators algorithm hybrid algorithm CNN-hyperparameter optimization COVID-19 CT-image detection

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Improved marine predators algorithm Optimized BiGRU for Strip Exit Thickness Prediction

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IEEE ACCESS 2024年 12卷 56719-56729页

作者： Luo, Hao Chen, Jiaxuan Sun, Zhan Zhang, Ying Zhang, Li Liaoning Univ Coll Informat Shenyang 110036 Peoples R China

The strip exit thickness is an important indicator for measuring the production quality of steel rolling products. Controlling its accuracy is crucial in producing high-quality rolling products in the steel industry. This study proposed an improved marine predators algorithm (IMPA) optimized bidirectional gated recurrent unit (BiGRU) for predicting the strip exit thickness. Firstly, an adaptive weight factor was introduced into the marine predators algorithm (MPA) to overcome the drawbacks of slow early convergence speed and limited search range of the algorithm, addressing the issue of local optimization. Secondly, an average fitness strategy was developed to enhance the quality of the algorithm population, improve optimization accuracy, and strengthen algorithm stability. In addition, IMPA was used to optimize parameters, reducing the impact of hyper-parameters such as the number of hidden layer neurons, learning rate, and batch size on the prediction accuracy of BiGRU, thereby establishing IMPA-BiGRU model. The experiments were conducted on an actual rolling process dataset. The experimental results showed that IMPA outperformed traditional MPA, particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), and recent variants of MPA. The IMPA-BiGRU also demonstrated excellent prediction capabilities for strip exit thickness, meeting the practical production requirements for high-quality rolling products.

关键词： Prediction algorithms Statistics Social factors Predictive models Optimization Vectors Parameter estimation Feature detection marine animals Heuristic algorithms Whale optimization algorithms BiGRU parameter optimization feature selection marine predators algorithm heuristic algorithm strip thickness

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Fuel constrained combined heat and power dispatch problem using marine predators algorithm

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ELECTRICAL ENGINEERING 2023年第6期105卷 4011页

作者： Keskin, Kemal Urazel, Burak Eskisehir Osmangazi Univ Dept Elect & Elect Engn TR-26040 Eskisehir Turkiye

In this manuscript, the conventional combined heat and power economic dispatch (CHPED) problem is reconsidered under fuel constraint with take-or-pay contract. The aim of the problem is to find the optimal active power generations and heat productions which provides the minimum fuel cost while satisfying all operational constraints. A recently presented metaheuristic approach, marine predators algorithm (MPA), is chosen for solving the problem addressed. Unlike the previous studies dealing with the CHPED problem, ramp rate, valve-points effect, prohibited operation zones for power-only units, feasible region constraints of combined heat and power units, take-or-pay constraint for limited fuel supply units are included all at once. Furthermore, power transmission losses are calculated using B-matrix loss formula. Case studies are conducted on a 24-unit test system with a 24-h operation period. The results obtained for the cases with and without the take-or-pay constraint are compared. When the take-or-pay constraint is taken into account, a 3.5% saving is achieved in the total cost.

关键词： Take-or-pay contract marine predators algorithm Non-convex optimization Combined heat and power dispatch Power loss

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Range-Free Localization Scheme Based on a Reconstructed marine predators algorithm With Adaptive Enhancement for Wireless Sensor Networks

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IEEE ACCESS 2024年 12卷 59702-59715页

作者： Yu, Dan Yuan, Ting Li, Pan Liu, Xinzhong Xie, Wenwu Yang, Zhihe Hunan Inst Sci & Technol Sch Informat Sci & Engn Yueyang 414006 Peoples R China

Wireless sensor networks (WSN) are widely utilized in all walks of industries, and node localization is still essential as one of the fundamental functionalities. Moreover, the marine predators algorithm (MPA), as one of the swarm intelligence optimization algorithms, has proven to possess strong search capability and high convergence speed. Therefore, for the poor optimization ability of the least squares method in the DV-Hop method, a reconstructed marine predator algorithm with adaptive enhancement (RMPA-AE) for WSN node localization is proposed in this paper. Firstly, a population diversity expression based on Euclidean distance is proposed to reconstruct the phases of the algorithm. Then, an adaptive enhancement strategy is proposed to improve the local exploitation ability of the algorithm with respect to its tendency to fall into local optimality. Subsequently, a global perturbation strategy based on the iteration number is proposed for the re-generation of the population at FADs to achieve a wide range of individual jumps. Finally, the experimental results for 26 benchmark functions demonstrate the search capability of the proposed RMPA-AE algorithm, and the superiority of the proposed algorithm compared to the state-of-the-art algorithms. The feasibility of the proposed algorithm is also demonstrated in the node localization simulation experiments.

关键词： Location awareness Wireless sensor networks Wireless communication Protocols Statistics Sociology Particle swarm optimization marine predators algorithm DV-hop Euclidean distance phase reconstruct

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Color constancy computation for dyed fabrics via improved marine predators algorithm optimized random vector functional-link network

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COLOR RESEARCH AND APPLICATION 2021年第5期46卷 1066-1078页

作者： Liu, Xiangqi Yang, Donghe Hangzhou Dianzi Univ Coll Mech Engn Informat Engn Sch Hangzhou 311305 Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou Peoples R China

Aiming at the color characteristics of dyed fabrics that are easily affected by changes in light, which affects the correctness of the color difference classification of dyed fabrics, this article proposes the color constancy calculation of dyed fabrics based on improved marine predators algorithm optimized random vector functional-link. First, in order to obtain the excellent initial population of the marine predators algorithm, this article uses two update strategies of the sine and cosine algorithm to screen the randomly initialized population of the marine predators algorithm. Then, the MPA algorithm that initializes the population using the sine and cosine algorithm optimizes the input weights and hidden layer bias parameters of random vector functional-link, thereby improving the prediction accuracy of random vector functional-link. Finally, using the image features extracted by the Gray-Edge framework, the sine and cosine algorithm-marine predators algorithm-random vector functional-link model proposed in this article is used to calculate the color constancy of dyed fabrics, to eliminate the influence of illumination changes on the color difference classification of dyed fabrics. Compared with the other eight algorithms, the dyed fabric image restored by the sine and cosine -marine predators -random vector functional-link algorithm proposed in this article is closest to the image under standard illumination, that is, the color constancy evaluation effect of the dyed fabric is the best.

关键词： color constancy computation marine predators algorithm random vector functional‐ link network sine and cosine algorithm

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Comparison of the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, the salp swarm algorithm for real-world engineering applications

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MATERIALS TESTING 2021年第5期63卷 448-452页

作者： Gurses, Dildar Bureerat, Sujin Sait, Sadiq M. Yildiz, Ali Riza Uludag Univ Dept Mech Engn Bursa Turkey Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia Bursa Uludag Univ Dept Automot Engn Bursa Turkey

This paper focuses on a comparision of recent algorithms such as the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, and the salp swarm algorithm. The slime mold algorithm (SMA) is a recent optimization algorithm. In order to strengthen its exploitation and exploration abilities, in this paper, a new hybrid slime mold algorithm-simulated annealing algorithm (HSMA-SA) has been applied to structural engineering design problems. As a result of the rules and practices that have become mandatory for fuel emissions by international organizations and governments, there is increasing interest in the design of vehicles with minimized fuel emissions. Many scientific studies have been conducted on the use of metaheuristic methods for the optimum design of vehicle components, especially for reducing vehicle weight. With the inspiration obtained from the above-mentioned methods, the HSMA-SA has been studied to solve the shape optimization of a design case to prove how the HSMA-SA can be used to solve shape optimization problems. The HSMA-SA provides better results as an arithmetic optimization algorithm than the slime mold optimization algorithm, the marine predators algorithm, and the salp swarm algorithm.

关键词： Design optimization arithmetic optimization algorithm slime mould optimization algorithm marine predators algorithm salp swarm algorithm

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An Efficient marine predators algorithm for Solving Multi-Objective Optimization Problems: Analysis and Validations

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IEEE ACCESS 2021年 9卷 42817-42844页

作者： Abdel-Basset, Mohamed Mohamed, Reda Mirjalili, Seyedali Chakrabortty, Ripon K. Ryan, Michael Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia UNSW Canberra Sch Engn & Informat Technol Capabil Syst Ctr Campbell ACT 2612 Australia

Recently, a new strong optimization algorithm called marine predators algorithm (MPA) has been proposed for tackling the single-objective optimization problems and could dramatically fulfill good outcomes in comparison to the other compared algorithms. Those dramatic outcomes, in addition to our recently-proposed strategies for helping meta-heuristic algorithms in fulfilling better outcomes for the multi-objective optimization problems, motivate us to make a comprehensive study to see the performance of MPA alone and with those strategies for those optimization problems. Specifically, This paper proposes four variants of the marine predators' algorithm (MPA) for solving multi-objective optimization problems. The first version, called the multi-objective marine predators' algorithm (MMPA) is based on the behavior of marine predators in finding their prey. In the second version, a novel strategy called dominance strategy-based exploration-exploitation (DSEE) recently-proposed is effectively incorporated with MMPA to relate the exploration and exploitation phase of MPA to the dominance of the solutions-this version is called M-MMPA. DSEE counts the number of dominated solutions for each solution-the solutions with high dominance undergo an exploitation phase;the others with small dominance undergo the exploration phase. The third version integrates M-MMPA with a novel strategy called Gaussian-based mutation, which uses the Gaussian distribution-based exploration and exploitation strategy to search for the optimal solution. The fourth version uses the Nelder-Mead simplex method with M-MMPA (M-MMPA-NMM) at the start of the optimization process to construct a front of the non-dominated solutions that will help M-MMPA to find more good solutions. The effectiveness of the four versions is validated on a large set of theoretical and practical problems. For all the cases, the proposed algorithm and its variants are shown to be superior to a number of well-known multi-objectiv

关键词： Optimization Evolutionary computation Heuristic algorithms Pareto optimization Search problems Convergence Australia Multi-objective optimization problem dominance strategy-based exploration-exploitation Gaussian-based mutation Nelder-Mead simplex marine predators algorithm

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Reinforcement learning marine predators algorithm for global optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第7期27卷 9137-9190页

作者： Wang, Jianlan Wang, Zhendong Zhu, Donglin Yang, Shuxin Wang, Junling Li, Dahai Jaingxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321004 Peoples R China

Given the weak convergence, limited balance capacity, and optimization limitations observed in the marine predators algorithm (MPA), which draws inspiration from the predatory behavior of marine organisms during evolutionary processes, this study introduces a Reinforcement Learning marine predators algorithm (RLMPA). Firstly, based on the predatory characteristics at different stages, we have designed three location update strategies for search agents aimed at creating high-quality candidate solutions from three perspectives. In particular, ranking paired mutually beneficial learning is specifically designed to expand the scope of exploration to generate as many high-quality candidate solutions as possible for future generations. The Gaussian random walk learning is specifically designed to achieve better optimization in the transitional phase by adjusting the step-size control parameters, successfully completing the transition from exploration to local exploitation phase. Additionally, modified somersault foraging strategy is introduced to accelerate local convergence and perform more extensive local exploitation. Secondly, we integrate reinforcement learning into MPA and use Q-learning mechanism to adaptively select location update strategies. Agents fully utilize the collected information to evaluate the next action of the agents, coordinate the exploration phase and exploitation phase, and enhance the global optimization ability. Finally, compared with 10 competitive algorithms, RLMPA achieves better comprehensive performance in global optimization ability, search efficiency and convergence speed on 41 test functions and 5 practical engineering problems. In the Friedman rank sum tests, RLMPA achieves a preferable overall ranking, and has certain ascendant preponderances in solving practical problems with stability, effectiveness and robustness.

关键词： Global optimization Reinforcement learning Q-learning marine predators algorithm Practical engineering problems

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Hybrid modified marine predators algorithm with teaching-learning-based optimization for global optimization and abrupt motion tracking

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期82卷 19793-19828页

作者： Gao, Zeng Zhuang, Yi Chen, Chen Wang, Qiuhong Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangjun Rd Nanjing 211106 Jiangsu Peoples R China

marine predators algorithm (MPA) has solved many challenging optimization problems since proposed. However, corresponding to specific optimization tasks (e.g., visual tracking), it is usually hard to select correct multiple parameters in MPA, which will greatly limit the exploitation and exploration performance. As a result, MPA could be misled to a local minima or even did not converge. To solve this issue, we advise an enhanced version of MPA based on teaching-learning-based optimization (MMPA-TLBO) which can concurrently improve the solution accuracy and the convergence speed. Specifically, first, we propose a modified MPA (MMPA) that leverages chaotic map and opposition-based learning strategy in the initialization stage to generate high-quality individuals. Second, we introduce a parameter-free teaching-learning-based optimization method with strong exploitation operator into MPA, called MMPA-TLBO, which effectively trade-off between the exploitation and exploration procedures. Finally, extensive experiments over 23 benchmark functions, CEC2017 benchmark problems and two engineering design problems show that MMPA-TLBO is better than other algorithms. Furthermore, we perform a thought-provoking case study of MMPA-TLBO on visual tracking. The experimental results show that the MMPA-TLBO tracker can outperform other trackers with a satisfied margin, especially for abrupt motion tracking.

关键词： marine predators algorithm Teaching-learning-based optimization Benchmark functions Engineering design problems Visual tracking

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