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16th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW)

作者： Ma, Zhaobin Ding, Bowen Zhang, Xin Qian, Pengjiang Zhan, Zhihui Jiangnan Univ Wuxi 214000 Jiangsu Peoples R China South China Univ Technol Guangzhou 510006 Peoples R China

ISBN: (纸本)9789811945465;9789811945458

Green supply chain network design (GSCND) has become a hot topic in many manufacturing industries today, since it is an environmentally friendly manufacturing mode and is significantly related to the sustainable development of enterprises. TheGSCND problem always has multiple complex constraints, which makes it difficult for traditional methods to search for feasible solutions within the limited time. Therefore, this paper designs a four-echelon green supply chain network model, and proposes amarine predators algorithm with the Stage-based Repairment (MPA-SR) to solve the complex constrained problem. For infeasible solutions that violate the constraints, MPA-SR repairs its logistics routes firstly, and then allocates the freight volume to the routes. Besides, an adaptive repair probability variable is designed to control the probability of the repair operator. In order to prove the efficacy of MPA-SR, the experiment is carried out on nine instances with three different scales. The experimental results show that MPA-SR performs better than other compared algorithms in terms of both the solving speed and the solution quality.

关键词： Green supply chain network marine predators algorithm Constrained optimization

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Illumination correction with optimized kernel extreme learning machine based on improved marine predators algorithm

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COLOR RESEARCH AND APPLICATION 2022年第3期47卷 630-643页

作者： Yang, Junyi Zheng, Minhui Chen, Sheng Hangzhou Dianzi Univ Sch Mech Engn Hangzhou 310018 Peoples R China Minist Nat Resources Inst Oceanog 2 Hangzhou Peoples R China

Aiming at the problem of poor image illumination correction accuracy, a kernel extreme learning machine optimized based on the differential evolution-improved marine predators algorithm is proposed to estimate the scene illumination information and restore the image. First, in order to solve the problem of randomization of the initial population of the marine predators algorithm, differential evolutions is used to provide a set of suitable initial populations to the algorithm. Then, an improved algorithm is used to optimize the weight and bias of the kernel extreme learning machine to solve the problem of the randomness of the weight and bias, so that the learning machine converges to the global optimal value and avoids the unstable predictions. Finally, after obtaining the predicted illumination information, the image model is corrected to the image effect under standard illumination through diagonal transformation. It can be seen from the experimental results that the best predictive value of the differential evolution marine predator algorithm learning machine is 0.0135915, and the quasi-bias is only 0.001687. Compared with traditional illumination estimation algorithms such as random vector functional link and extreme learning machine, the differential evolution marine predator algorithm learning machine algorithm proposed in this article has better stability, higher accuracy of calculated predicted values, and better image restoration effect.

关键词： differential evolution illumination correction kernel extreme learning machine marine predators algorithm

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A Hybrid Heartbeats Classification Approach Based on marine predators algorithm and Convolution Neural Networks

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IEEE ACCESS 2021年 9卷 86194-86206页

作者： Houssein, Essam H. Abdelminaam, Diaa Salama Ibrahim, Ibrahim E. Hassaballah, M. Wazery, Yaser M. Minia Univ Fac Comp & Informat Al Minya 61519 Egypt Benha Univ Fac Comp & Artificial Intelligence Banha 12311 Egypt Misr Int Univ Fac Comp Sci Cairo 11341 Egypt Luxor Univ Fac Comp & Informat Armant Egypt South Valley Univ Fac Comp & Informat Dept Comp Sci Qena 83523 Egypt

The electrocardiogram (ECG) is a non-invasive tool used to diagnose various heart conditions. Arrhythmia is one of the primary causes of cardiac arrest. Early ECG beat classification plays a significant role in diagnosing life-threatening cardiac arrhythmias. However, the ECG signal is very small, the anti-interference potential is low, and the noise is easily influenced. Thus, clinicians face challenges in diagnosing arrhythmias. Thus, a method to automatically identify and distinguish arrhythmias from the ECG signal is invaluable. In this paper, a hybrid approach based on marine predators algorithm (MPA) and convolutional neural network (CNN) called MPA-CNN is proposed to classify the non-ectopic, ventricular ectopic, supraventricular ectopic, and fusion ECG types of arrhythmia. The proposed approach is a combination of heavy feature extraction and classification techniques;hence, outperforms other existing classification approaches. Optimal characteristics were derived directly from the raw signal to decrease the time required for and complexity of the computation. Precision levels of 99.31%, 99.76%, and 99.47% were achieved by the proposed approach on the MIT-BIH,EDB, and INCART databases, respectively.

关键词： Electrocardiography Feature extraction Heart beat Heart Optimization Support vector machines Rhythm Heart disorder classification marine predators algorithm deep neural networks CNN feature fusion

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Modified marine predators algorithm hybridized with teaching- learning mechanism for solving optimization problems

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第1期20卷 93-127页

作者： Ma, Yunpeng Chang, Chang Lin, Zehua Zhang, Xinxin Song, Jiancai Chen, Lei Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China Tianjin Univ Commerce Coll Sci Tianjin 300134 Peoples R China

marine predators algorithm (MPA) is a newly nature-inspired meta-heuristic algorithm, which is proposed based on the Levy flight and Brownian motion of ocean predators. Since the MPA was proposed, it has been successfully applied in many fields. However, it includes several shortcomings, such as falling into local optimum easily and precocious convergence. To balance the exploitation and exploration ability of MPA, a modified marine predators algorithm hybridized with teaching-learning mechanism is proposed in this paper, namely MTLMPA. Compared with MPA, the proposed MTLMPA has two highlights. Firstly, a kind of teaching mechanism is introduced in the first phase of MPA to improve the global searching ability. Secondly, a novel learning mechanism is introduced in the third phase of MPA to enhance the chance encounter rate between predator and prey and to avoid premature convergence. MTLMPA is verified by 23 benchmark numerical testing functions and 29 CEC-2017 testing functions. Experimental results reveal that the MTLMPA is more competitive compared with several state-of-the-art heuristic optimization algorithms.

关键词： meta-heuristics optimization marine predators algorithm exploitation and exploration modified marine predators algorithm teaching-learning-based optimization

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A parallel compact marine predators algorithm applied in time series prediction of Backpropagation neural network (BNN) and engineering optimization

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 220卷 65-88页

作者： Pan, Jeng-Shyang Zhang, Zhen Chu, Shu-Chuan Zhang, Si-Qi Wu, Jimmy Ming-Tai Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung 41349 Taiwan

This study introduces a novel approach for integrating a compact mechanism into the marine Predator algorithm (MPA), subsequently proposing innovative parallel and communication strategies. The synergistic combination of these methodologies substantially augments the global search efficiency and accelerates the convergence rate of the original MPA. The paper culminates in presenting an enhanced version of the marine Predator algorithm, termed PCMPA, which leverages compact parallel technology. The performance of PCMPA, alongside a range of comparative algorithms, is rigorously evaluated using the CEC2013 benchmark test functions. These comparative algorithms encompass recent variants of MPA, PSO, DE, and other newly developed algorithms. Evaluation results reveal that PCMPA outperforms its counterparts in a more extensive array of test functions. To corroborate PCMPA's efficacy in real -world scenarios, the algorithm is applied to parameter optimization in Backpropagation neural network (BNN) and targeted engineering optimization challenges. This application demonstrates that PCMPA consistently achieves enhanced performance in practical implementations.

关键词： marine predators algorithm BNN (Backpropagation neural network) Compact strategy Parallel communication strategy Engineering optimization

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A fractional order PID tuning tool for automatic voltage regulator using marine predators algorithm

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ENERGY REPORTS 2023年 9卷 416-421页

作者： Tumari, Mohd Zaidi Mohd Ahmad, Mohd Ashraf Rashid, Muhammad Ikram Mohd Univ Teknikal Malaysia Melaka Fac Elect Technol & Engn Hang Tuah Jaya Melaka Malaysia Univ Malaysia Pahang Al Sultan Abdullah Fac Elect & Elect Engn Technol Pekan Pahang Malaysia

The fractional-order proportional-integral-derivative (FOPID) controller stands as a widely embraced choice for the task of automatic voltage regulation (AVR) when it comes to maintaining the voltage output of synchronous generators. Nevertheless, fine-tuning the FOPID controller presents a formidable challenge, mainly because it possesses five tuning gains, in contrast to the conventional PID controller, which has three gains. Consequently, this paper introduces a novel tuning tool tailored to the AVR system by utilizing the marine predators algorithm (MPA). To gauge the effectiveness of the proposed approach, two key evaluation criteria are employed: step response analysis and trajectory tracking analysis. The results of this research reveal that the MPA-FOPID controller demonstrates exceptional performance criteria, notably enhancing the AVR transient response in comparison to other FOPID controllers optimized through recent metaheuristic algorithms.

关键词： Automatic voltage regulator Metaheuristic algorithms Fractional order PID controller marine predators algorithm Optimization

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Multi-Stage Improvement of marine predators algorithm and Its Application

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 3097-3119页

作者： Chuandong Qin Baole Han School of Mathematics and Information Science North Minzu UniversityYinchuan750021China

The metaheuristic algorithms are widely used in solving the parameters of the optimization *** marine predators algorithm(MPA)is a novel population-based intelligent *** MPA has shown a talented foraging strategy,it still needs a balance of exploration and ***,a multi-stage improvement of marine predators algorithm(MSMPA)is proposed in this *** algorithm retains the advantage of multistage search and introduces a linear flight strategy in the middle stage to enhance the interaction between *** further away from the historical optimum are required to move,increasing the exploration capability of the *** the middle and late stages,the searchmechanism of particle swarmoptimization(PSO)is inserted,which enhances the exploitation capability of the *** means that the stochasticity is decreased,that is the optimal region where predators jumping out is effectively *** the same time,self-adjusting weight is used to regulate the convergence speed of the algorithm,which can balance the exploration and exploitation capability of the *** algorithm is applied to different types of CEC2017 benchmark test functions and threemultidimensional nonlinear structure design optimization problems,compared with other recent *** results show that the convergence speed and accuracy of MSMPA are significantly better than that of the comparison algorithms.

关键词： marine predators algorithm multi-stage strategy structural design optimization

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DMT-OMPA: Innovative applications of an efficient adversarial marine predators algorithm based on dynamic matrix transformation in engineering design optimization

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年 431卷

作者： Zhang, Zhen Chu, Shu-Chuan Nguyen, Trong-The Wang, Xiaopeng Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Nanjing Univ Informat Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung 41349 Taiwan Univ Informat Technol VNUHCM Multimedia Commun Lab Ho Chi Minh City Vietnam VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic

This paper introduces an innovative variant of the marine predators algorithm (MPA), termed the Dynamic Matrix Transformation-based Oppositional marine predators algorithm (DMTOMPA), aimed at enhancing the efficiency of engineering optimization strategies. Traditional MPAs have several shortcomings, including insufficient solution diversity and coverage in the initialization phase, a tendency to become trapped in local optima, and inadequate search capabilities in the later stages of iteration, all of which negatively impact the algorithm's efficiency and effectiveness. To address these issues, the DMT-OMPA incorporates oppositional learning mechanisms and dynamic matrix transformation strategies, significantly enhancing global search capabilities and accelerating convergence speed, particularly in handling complex multidimensional optimization *** results on the CEC2013 and CEC2017 test suites demonstrate that DMT-OMPA outperforms other recent MPA variants, various classical algorithm variants, and newly proposed algorithms, verifying its advantages in precision and reliability. Furthermore, the application of this algorithm to various real-world engineering problems substantiates its broad applicability and high efficiency. The study's findings not only deepen our understanding of swarm intelligence optimization algorithms but also provide a new efficient tool for solving complex engineering problems. The results indicate a promising potential for wider application in diverse fields, suggesting that the DMT-OMPA algorithm could become an effective tool for tackling complex optimization problems in the future.

关键词： marine predators algorithm Transformation matrix Dynamic search DMT-OMPA Engineering optimization

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Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第41期30卷 94312-94333页

作者： Adnan, Rana Muhammad Dai, Hong-Liang Kisi, Ozgur Heddam, Salim Kim, Sungwon Kulls, Christoph Zounemat-Kermani, Mohammad Guangzhou Univ Sch Econ & Stat Guangzhou 510006 Peoples R China Lubeck Univ Appl Sci Dept Civil Engn D-23562 Lubeck Germany Ilia State Univ Sch Technol Dept Civil Engn Tbilisi 0162 Georgia Hydraul Div Univ Fac Sci Agron Dept 20 Aout 1955Route HadaikBP 26 Skikda 21024 Algeria Dongyang Univ Dept Railroad Construct & Safety Engn Yeongju 36040 South Korea Shahid Bahonar Univ Kerman Dept Water Engn Kerman Iran

Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality assessment. Alternative methods are essential for accurately prediction of this parameter because the traditional method in predicting the BOD is time-consuming and it is inaccurate due to inconstancies in microbial multiplicity. In this study, the applicability of four hybrid neuro-fuzzy (ANFIS) methods, ANFIS with genetic algorithm (GA), ANFIS with particle swarm optimization (PSO), ANFIS with sine cosine algorithm (SCA), and ANFIS with marine predators algorithm (MPA), was investigated in predicting BOD using distinct input combinations such as potential of hydrogen (pH), dissolved oxygen (DO), electrical conductivity (EC), water temperature (WT), suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (T-P) acquired from two river stations, Gongreung and Gyeongan, South Korea. The applicability of multi-variate adaptive regression spline (MARS) in determination of the best input combination was examined. The ANFIS-MPA was found to be the best model with the lowest root mean square error and mean absolute error and the highest determination coefficient. It improved the root mean square error of ANFIS-PSO, ANFIS-GA, and ANFIS-SCA models by 13.8%, 12.1%, and 6.3% for Gongreung Station and by 33%, 25%, and 6.3% for Gyeongan Station in the test stage, respectively.

关键词： Biochemical oxygen demand Water quality Prediction Neuro-fuzzy marine predators algorithm

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An Improved marine predators algorithm for Optimal Reactive Power Dispatch With Load and Wind-Solar Power Uncertainties

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IEEE ACCESS 2022年 10卷 126664-126673页

作者： Zhao, Jian Li, Liying Yu, Jiaxin Li, Zhiwu Univ Sci & Technol Liaoning Sch Sci Anshan 114051 Liaoning Peoples R China Hitachi Bldg Technol Guangzhou Co Ltd Guangzhou 500650 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macau Peoples R China

With the growing usage of renewable energy resources, the fluctuation and randomness of solar and wind power output have a significant influence on the safe and stable operation of a power system. Deciding how to deal with these uncertainties has become a hot topic for researchers and practitioners. In this paper, a hybrid improved marine predators algorithm is proposed to solve the optimal reactive power dispatch problem with load demand, and wind-solar power uncertainties. The epsilon-constraint handling technique is adopted to deal with the constraints in the optimization problem. The uncertainty factors in the system are modeled using appropriate probability density functions. The backward reduction method is used to determine a representative set of scenarios from a large number of scenarios produced by a Monte Carlo simulation method. IEEE 30-bus test system is utilized to verify the proposed algorithm. The simulation results demonstrate that the algorithm can produce better results than other advanced metaheuristic algorithms.

关键词： The e-constraint handling marine predators algorithm optimal reactive power dispatch renewable energy uncertainty

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