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Parameter optimization of PID controller based on an enhanced whale optimization algorithm for AVR system

OPERATIONAL RESEARCH 2023年第3期23卷 44页

作者： Zhang, Jinzhong Zhang, Tan Zhang, Gang Kong, Min West Anhui Univ Sch Elect & Optoelect Engn Luan 237012 Peoples R China

This paper proposes an enhanced whale optimization algorithm based on a ranking-based mutation operator (EWOA) to stabilize the PID controller's parameters in the AVR system, and the intention is to arrive at the ideal objective function value by modifying the control parameters. The whale optimization algorithm is based on the humpback whale's bubble-net attacking behavior, which imitates shrinkage encircling prey, bubble-net attacking prey and random searching for prey to address the complicated optimization issue. The ranking-based mutation operator can maximize the selection probability, screen out the best search individual, eliminate premature convergence, promote the convergence speed and elevate the exploitation ability. The EWOA not only has substantial robustness and stability to strengthen the optimization efficiency and recognize the optimal solution but also combines exploration with exploitation to expand the convergence rate and calculation precision. The EWOA is contrasted with other algorithms to validate the practicability and usefulness. The experimental results demonstrate that the EWOA has a quicker convergence rate, higher computation precision, shorter execution time and greater stability, which is a remarkable and practical method for addressing the parameter optimization of the PID controller in the AVR system.

关键词： whale optimization algorithm Ranking-based mutation operator Objective function value Control parameters Experimental results

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A Novel Combination of whale optimization algorithm and Support Vector Machine with Different Kernel Functions for Prediction of Blasting-Induced Fly-Rock in Quarry Mines

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NATURAL RESOURCES RESEARCH 2021年第1期30卷 191-207页

作者： Nguyen, Hoang Bui, Xuan-Nam Choi, Yosoon Lee, Chang Woo Armaghani, Danial Jahed Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Hanoi Univ Min & Geol Min Fac Dept Surface Min Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Electromech Res Ctr Min Duc Thang Ward 18 Vien St Hanoi Vietnam Pukyong Natl Univ Dept Energy Resources Engn Busan 48513 South Korea Dong A Univ Dept Energy & Mineral Resources Coll Engn Busan 49315 South Korea Univ Malaya Dept Civil Engn Fac Engn Kuala Lumpur 50603 Malaysia

This study proposed a novel data-driven model for estimating distance of fly-rock in bench blasting in open-pit mines using a robust combination of the whale optimization algorithm (WOA), support vector machine (SVM) and kernel functions. Four kernel functions were investigated for embedding in the SVM model, including linear (L), radius basis function (RBF), polynomial (P), and hyperbolic tangent (HT) functions. Then, the WOA was applied to optimize the kernel-based SVM models, namely WOA-SVM-L, WOA-SVM-P, WOA-SVM-RBF, and WOA-SVM-HT. A variety of conventional data-driven models were also developed for predicting fly-rock distance, including adaptive neuro-fuzzy inference system (ANFIS), gradient boosting machine (GBM), random forest (RF), classification and regression tree (CART), and artificial neural network (ANN). The blasting parameters and maximum fly-rock distance, as well as their relationship, were carefully investigated for this aim. The predictive results of the models were evaluated through two performance indices: root-mean-squared error (RMSE) and correlation coefficient (R-2). These indices indicated that the linear function-based WOA-SVM model (i.e., WOA-SVM-L) seems to be not fit for predicting fly-rock with the largest error (i.e., RMSE = 9.080 andR(2) = 0.937). In contrast, the WOA-SVM-RBF model yielded the highest accuracy in predicting the distance of fly-rock (i.e., RMSE = 5.241,R-2 = 0.977). Meanwhile, the WOA-SVM-P and WOA-SVM-HT models provided lower performances than those of the WOA-SVM-RBF model, but they are acceptable. The conventional models (i.e., ANFIS, GBM, RF, CART, and ANN) are pretty well (i.e., RMSE in the range of 5.804 to 6.567;R(2)in the range of 0.965 to 0.973);however, their performance is lower than those of the WOA-SVM-RBF model as well. Based on these results, the WOA-SVM model was proposed as a useful data-driven model for predicting fly-rock with high reliability in practical engineering.

关键词： Bench blasting Fly-rock Hybrid intelligent model whale optimization algorithm Support vector machine Kernel functions

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MSWOA: Multi-strategy whale optimization algorithm for Engineering Applications

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ENGINEERING LETTERS 2024年第8期32卷 1603-1615页

作者： Zhou, Ronghe Zhang, Yong Sun, Xiaodong Liu, Haining Cai, Yingying Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Liaoning Peoples R China Dagushan Pelletizing Mill Anshan Steel Grp Min Co Anshan 114046 Liaoning Peoples R China Anshan Coll Technol Anshan 114013 Liaoning Peoples R China

The whale optimization algorithm (WOA) is novel algorithm that was motivated by the prey behavior of humpback whales. WOA has attracted a lot of interest due to its few parameters and easy implementation, but it also has sluggish convergence speed, poor convergence accuracy, and is is easy to get a local optima. In this paper, a multi-strategy WOA called MSWOA is constructed to address these drawbacks. It includes dimensional updating, nonlinear convergence factor, global perturbation factor, firefly perturbation, and vertical and horizontal crossover learning strategy. First, a strategy was developed to update each dimension differently to avoid MSWOA from reaching a local optima. Second, a nonlinear convergence factor is devised to better balance the MSWOA's search ability. Third, a global perturbation factor is considered during the exploration phase, to enrich the whale population. Fourth, a firefly perturbation strategy is employed to increase convergence accuracy. Fifth, a vertical and horizontal strategy is applied to accelerate convergence. Finally, twelve CEC2022 benchmark functions and three engineering cases are adopted to evaluate the performance of MSWOA. The results confirm that MSWOA is superior and competitive.

关键词： whale optimization algorithm dimensional updating firefly perturbation vertical and horizontal crossover strategy engineering applications

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Potato Yield Prediction Research Based on Improved Artificial Neural Networks Using whale optimization algorithm

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POTATO RESEARCH 2024年 1-10页

作者： Lei, Xue Xu, Xueguo Zhou, Shiyu Shanghai Univ Sch Management Shanghai 200444 Peoples R China

Potato, as a crucial global staple crop, plays a pivotal role in ensuring global food security. In China, both the cultivation area and yield of potatoes rank among the highest globally, highlighting its significance in agricultural production. Accurate prediction of potato yield is essential for guiding cultivation management and making related decisions. The objective of this study is to develop an improved potato yield prediction model to help address supply gaps in the fresh potato market domestically, particularly between northern and southern regions. Considering the limitations of traditional Backpropagation (BP) neural networks in terms of prediction accuracy and robustness, this study optimizes the BP neural network using the whale optimization algorithm (WOA). By analysing meteorological factors, field hydrothermal factors, and potato yield data collected from field Internet of Things (IoT) systems between 2010 and 2022, we constructed and compared three different models: a traditional BP neural network model, a BP neural network model optimized by Genetic algorithm (GA), and a BP neural network model optimized by WOA. The research findings indicate that the WOA-BP model significantly outperforms the other two models in prediction accuracy, with an R2 (coefficient of determination) value of 0.9764. Moreover, the high degree of fit between predicted and observed values validates the scientific validity and accuracy of the WOA-BP model in potato yield prediction.

关键词： Backpropagation neural network Cultivation management Potato yield prediction whale optimization algorithm

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Hybridizing whale optimization algorithm With Particle Swarm optimization for Scheduling a Dual-Command Storage/Retrieval Machine

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IEEE ACCESS 2023年 11卷 21264-21282页

作者： Hsu, Hsien-Pin Wang, Chia-Nan Natl Kaohsiung Univ Sci & Technol Dept Supply Chain Management Kaohsiung 81157 Taiwan Natl Kaohsiung Univ Sci & Technol Dept Ind Engn & Management Kaohsiung 807618 Taiwan

whale optimization algorithm (WOA) and particle swarm optimization (PSO) have been used individually usually. However, a separate use of them has a limitation. Hybridizing WOA with PSO is expected to evolve solutions better due to the cooperation between whales and seabirds. Developing such kind of model is the focus of this research. A framework has been further proposed to best utilize such hybridizations for developing simulation-based optimization approaches. The framework has the advantage of integrating metaheuristic, simulation, and optimization seamlessly. It can waive the rigorous and labor-intensive optimization procedure required for traditional simulation. In this research, simulation-based optimization approaches are used to deal with the dual-command block scheduling problem of a manufacturing firm's storage/retrieval (S/R) machine in an automated storage/retrieval system. The S/R machine is mainly used to store/retrieve stock-keeping units in an automated storage/retrieval system. Three simulation-based optimization approaches, Hybrid1 (WOA+PSO), Hybrid2 (WOA+PSO), and Hybrid3 (WOA+PSO), have been developed. To investigate their effectiveness, experiments have been conducted to compare them with their base models, WOA and PSO, as well as the genetic algorithm (GA) and PWOA. The PWOA is an abbreviation of a hybridization of PSO and WOA proposed in a previous study. The experimental results show that Hybrid3 (WOA+PSO) outperforms Hybrid2 (WOA+PSO), Hybrid1 (WOA+PSO), WOA, PSO, PWOA, and GA. The uses of techniques such as hybridization, Neighborhood heuristic, and adaptive movements of whales empower Hybrid3 (WOA+PSO) the most.

关键词： Metaheuristics Manufacturing Genetic algorithms Job shop scheduling Analytical models Particle swarm optimization whale optimization algorithms whale optimization algorithm particle swarm optimization storage retrieval machine scheduling

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Optimal chiller loading for energy conservation using a hybrid whale optimization algorithm based on population membrane systems

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INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2022年第1期42卷 101-116页

作者： Mohammadbeigi, Amir Maroosi, Ali Hemmati, Mohammad Univ Torbat Heydarieh Dept Elect Engn Torbat Heydarieh Iran Univ Torbat Heydarieh Dept Comp Engn Torbat Heydarieh Iran

The optimal chiller loading (OCL) is one of the most essential issues for saving energy and costs. Because of the pervasive use of chiller systems in the world, even saving a small amount of energy consumption in a chiller system can be significant. This study introduces a hybrid algorithm that could achieve similar or better results than those presented in previous studies for OCL problem. The whale optimization algorithm (WOA) is a recent promising algorithm for solving optimization problems with the small number of tuning parameters. However, it suffers from its inefficient exploitation around the best solution. To address this shortcoming, WOA-SQP was introduced that uses a sequential quadratic programming (SQP) method to exploit efficiently the search space around the best solution obtained by WOA. Although, WOA-SQP could achieve good results, but the variance of its solutions is high for different runs on the same problem. The non-deterministic distributed parallel framework of the population P system (PPS) enhances the diversity of WOA-SQP to minimize the variance of the solutions that shows the stability of the algorithm. Simulation results of the proposed algorithm on several case studies show the better performance of the proposed algorithm in comparison with the recent approaches.

关键词： whale optimization algorithm sequential Quadratic Programming population membrane systems optimal Chiller Loading

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An Efficient Improved whale optimization algorithm for optimization Tasks

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ENGINEERING LETTERS 2024年第2期32卷 392-411页

作者： Wang, Jiayin Wang, Yukun Univ Sci & Technol Liaoning Sch Elect & Engn Anshan 114051 Liaoning Peoples R China

The whale optimization algorithm (WOA) is an efficient meta -heuristic algorithm inspired by the feeding behavior of whales. Although it has successfully solved many optimization problems, it still suffers from premature convergence and poor accuracy when solving complex problems. To address these issues, this paper proposes an improved whale optimization algorithm called EIWOA to improve the search efficiency and accuracy. First, we introduce a new global search mechanism and encircling prey strategy in EIWOA, which utilizes differential evolution, and sine -cosine search strategy to improve the global search efficiency and avoid premature convergence. Second, we use a levy flight -based spiral update position strategy to improve the local search efficiency of the algorithm, thus improving the convergence speed and accuracy. Again, we introduce a balancing factor with fluctuating decay properties into EIWOA to better balance exploration and exploitation. Finally, we introduce a dynamic opposite learning -based whale -fall strategy in EIWOA to equip the algorithm with the ability to jump out of the local optimum. The qualitative analysis of the algorithm shows that the EIWOA algorithm converges quickly, is highly accurate, and has the ability to jump out of the local optimum. In order to validate the performance of the proposed EIWOA algorithm, we evaluated the algorithm on CEC2017 benchmark function and four real world engineering problems. We also conduct a comparative study of the EIWOA algorithm with EWOA, an excellent variant of WOA, as well as some excellent meta- heuristics developed recently. The results of numerical experiments demonstrate the superiority of the proposed EIWOA, which is further confirmed statistically by the results of Friedman's test and Wilcoxon signed rank test. The proposed EIWOA algorithm is significantly better than WOA and other competing algorithms in terms of convergence speed, accuracy and optimization ability in dealing with comp

关键词： whale optimization algorithm exploration and expolitation whale-fall strategy balancing factor

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Modified whale optimization algorithm for Infinitive Impulse Response System Identification

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第3期45卷 2163-2176页

作者： Luo, Qifang Ling, Ying Zhou, Yongquan Guangxi Univ Nationalities Coll Informat Sci Engn Nanning Peoples R China Key Labs Guangxi High Schools Complex Syst Computat Intelligence Nanning Guangxi Peoples R China

System identification based on infinite impulse response (IIR) models has received much attention because it is used in a variety of real-world applications. However, an IIR model might have a multimodal error surface. To attain an ideal identification, an efficient and robust method is necessary. In this study, a modified whale optimization algorithm (WOA) with a ranking-based mutation operator, called the RWOA, is presented to solve the IIR system identification problem. The RWOA integrates a ranking-based mutation operator into the basic WOA to enhance performance by speeding up the convergence rate and then enhances the exploitation capability. The experimental results of actual and reduced-order identification for a standard system using our proposed RWOA were superior to those of five state-of-the-art algorithms (including the basic WOA), in terms of improving the quality and stability of the results, in most cases, and significantly speeding up convergence.

关键词： Ranking-based mutation operator whale optimization algorithm RWOA IIR system System identification

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Chaotic whale optimization algorithm for optimal solution of combined heat and power economic dispatch problem incorporating wind

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RENEWABLE ENERGY FOCUS 2020年 35卷 56-71页

作者： Paul, Chandan Roy, Provas Kumar Mukherjee, V. Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Indian Inst Technol Indian Sch Mines Dept Elect Engn Dhanbad Bihar India

In this article, a new practical approach is implemented to analyze combined heat and power economic dispatch (CHPED) and wind based CHPED (CHPEDW) problems with a chaotic based whale optimization algorithm (WOA). The conflict objectives of this problem are used to minimize fuel costs as well as emission. The proposedWOAworks on the searching behavior of humpback whales, which helps to satisfy the problem's objective functions. The chaotic nature has been integrated with WOA to enhance the convergence speed of the problem. The presence of valve point loading, prohibited operating zone, and uncertainty of wind has expanded the problem nonlinearity. It has been tested on two different nonlinear realistic power areas to judge the satisfactory results of the proposed algorithm. The superiority of the proposed algorithm is judged by comparing it with presently developed some other meta-heuristic optimization techniques.

关键词： Chaotic whale optimization algorithm Combined heat and power economic dispatch whale optimization algorithm Wind based combined heat and power economic dispatch.

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PV Output Power Enhancement using whale optimization algorithm under Normal and Shading Conditions

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2020年第3期10卷 1536-1543页

作者： Maniraj, B. Fathima, A. Peer Vellore Inst Technol Sch Elect Engn Chennai 600127 Tamil Nadu India

This paper deals with whale hunting behaviour inspired whale optimization algorithm (WOA) for tracking maximum power from the solar photovoltaic (PV) system. Maximum power point tracking (MPPT) controller has become an essential requirement for tracking the actual power present in solar PV system. In literature, various optimization techniques have been proposed for normal and partial shading conditions (PSC). But the problem in the conventional methods is tracking peak power under shading conditions is not assurance due to the presence of many peaks in a shading conditions. Because the local peaks are presented very close to global peaks. The results of conventional algorithms get failed with local peaks instead of tracking global peak power particularly in shading conditions. In this paper, a new WOA algorithm has been proposed which has the ability to reach the peak power presented in solar PV panel under different climatic conditions. In further, the proposed WOA algorithm has been investigated in MATLAB/Simulink model and comparison has been made with different MPPT algorithms, namely, Perturb and Observe (PO), grey wolf optimization (GWO). The results clearly demonstrated the proposed WOA algorithm giving more than 99.6% efficiency with high tracking speed and minimum payback period under PSC.

关键词： Maximum power point tracking Photovoltaic Partial shading conditions whale optimization algorithm

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