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A modified whale optimization algorithm for exploitation capability and stability enhancement

HELIYON 2022年第10期8卷 e11027页

作者： Reddy, Kumeshan Saha, Akshay K. Univ KwaZulu Natal Elect Elect & Comp Engn 238 Mazisi Kunene Rd ZA-4041 Durban South Africa

Swarm-based Metaheuristic optimization Techniques (MOT) are the dominant among all techniques, particularly owing to their simple nature and robust performance. The whale optimization algorithm (WOA), a swarm-based MOT inspired by the hunting strategy of the humpback whale, has thus far shown promising results. However, like all MOT, the WOA is not without drawbacks. These demerits are a slow convergence rate and poor exploitation capability. This may prove to be problematic when applied to optimization problems requiring high precision results. Over the past few years, there has been proposed modifications to the conventional algorithm. However, experimental analysis highlights the need to further enhance the properties of the algorithm. This work proposes an enhanced WOA for exploitation capability and stability enhancement. The proposed algorithm introduces various modifications to the position update equations of the conventional algorithm, as well as a modified algorithm structure. The proposed algorithm was compared to various state-of-the-art MOT, as well as modified WOA proposed in recent literature. When applied to the CEC 2019 benchmark functions, the proposed algorithm produced the best result in 7 of the 10 test and had the most superior overall placement. When applied to practical problems, the algorithm once again demonstrated superiority. In addition, it was observed that the proposed algorithm exhibited a superior convergence rate to the other compared techniques.

关键词： algorithms Convergence Exploitation optimization whale optimization algorithm

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Research on automatic detection of gradual fault of high voltage electric energy metering transformer based on fuzzy rough set and whale optimization algorithm

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JOURNAL OF VIBROENGINEERING 2024年第3期26卷 551-566页

作者： Wang, Chunguang Wu, Zhiwu Wu, Xiang Huang, Tianfu Huang, Hanbin State Grid Fujian Elect Power Co Ltd Mkt Serv Ctr Fuzhou 350013 Fujian Peoples R China

Based on fuzzy rough set and whale optimization algorithm, the automatic fault detection method of high -voltage electric energy metering transformer is studied to improve the fault diagnosis effect and efficiency. On the basis of constructing the mathematical model of gradual fault of high -voltage electric energy metering transformer, the fuzzy rough set theory is used to reduce the data attributes of fault samples, eliminate similar attributes, determine the minimum fault feature set, and complete the fault feature selection, which is used as the input of the fault detection model based on whale optimization algorithm-based Support Vector Machine (WOA-SVM). After the kernel parameters and penalty factors of SVM are optimized by whale optimization algorithm, the type of gradual fault of high -voltage electric energy metering transformer is identified. The experimental results show that the reduced fault attributes are distributed differently in the sample data, and the fault detection accuracy can be improved by 9.5 % through fault feature selection. The fault diagnosis model with Gaussian radial basis function, kernel parameter of 0.05 and penalty factor of 10 has the best performance. This method can identify the gradual fault types of high -voltage electric energy metering transformers, and the fault diagnosis effect is outstanding.

关键词： fuzzy rough set whale optimization algorithm transformer gradual failure penalty factor attribute reductio

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Cyclic Path Planning of Hyper-redundant Manipulator using whale optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第8期12卷 677-686页

作者： Machmudah, Affiani Parman, Setyamartana Abbasi, Aijaz Solihin, Mahmud Iwan Abd Manan, Teh Sabariah Beddu, Salmia Ahmad, Amiruddin Rasdi, Nadiah Wan Univ Airlangga Fac Adv Technol & Multidisciplinary Ind Engn Kampus C Jalan Mulyorejo Surabaya Indonesia Univ Teknikal Malaysia Melaka Fak Teknol Kejuruteraan Mekanikal & Pembuatan Ayer Keroh 75450 Melaka Indonesia Quaid E Awam Univ Engn Sci & Technol Mech Engn Nawabshah Pakistan UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur 56000 Malaysia Univ Malaysia Terengganu Inst Trop Biodivers & Sustainable Dev Kuala Nerus 21030 Terengganu Daru Malaysia Univ Tenaga Nas Dept Civil Engn Jalan Ikram Uniten Kajang 43000 Selangor Darul Malaysia Univ Malaysia Terengganu Fac Sci & Marine Environm Kuala Nerus 21030 Terengganu Daru Malaysia Univ Malaysia Terengganu Fac Fisheries & Food Sci Kuala Nerus 21030 Terengganu Daru Malaysia

This paper develops a path planning algorithm of hyper-redundant manipulators to achieve a cyclic property. The basic idea is based on a geometrical analysis of a 3-link planar series manipulator in which there is an orientation angle boundary of a prescribed path. To achieve the repetitive behavior, for hyper-redundant manipulators consisting of 3-link components, an additional path is chosen in such away so that it is a repetitive curve which has the same curve frequency with the prescribed end-effector path. To solve the redundancy resolution, meta-heuristic optimizations, namely Genetic algorithm (GA) and whale optimization algorithm (WOA), are applied to search optimal trajectories inside local orientation angle boundaries. Results show that using constant of the local orientation angle trajectories for the 3-link component, the cyclic properties can be achieved. The performance of the WOA shows very promising result where generally it obtains the lowest fitness value as compare with the GA. Depending on the complexity of the path planning, dividing the path into several stages via intermediate points may be necessary to achieve the good posture. The performance of the swarm based meta-heuristic optimization, namely the WOA, shows very promising result where generally it obtains the lowest fitness value as compare with the GA. Using the developed approach, not only the cyclic property is obtained but also the optimal movement of the hyper-redundant manipulator is achieved.

关键词： Hyper-redundant path planning whale optimization algorithm sustainable manufacturing

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Application of spiral enhanced whale optimization algorithm in solving optimization problems

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SCIENTIFIC REPORTS 2024年第1期14卷 1-27页

作者： Qu, ShiZheng Liu, Huan Xu, Yinghang Wang, Lu Liu, Yunfei Zhang, Lina Song, Jinfeng Li, Zhuoshi Jilin Agr Univ Coll Plant Protect Changchun 130118 Jilin Peoples R China Jilin Agr Univ Jilin Prov Key Lab Fungal Phen Changchun 130118 Jilin Peoples R China Jilin Agr Univ Coll Fungi Changchun 130118 Jilin Peoples R China Jilin Univ Comp Sci & Technol Changchun 130118 Peoples R China Jilin Engn Normal Univ Sch Data Sci & Artificial Intelligence Changchun Jilin Peoples R China

The whale optimization algorithm (WOA) is regarded as a classic metaheuristic algorithm, yet it suffers from limited population diversity, imbalance between exploitation and exploration, and low solution accuracy. In this paper, we propose the Spiral-Enhanced whale optimization algorithm (SEWOA), which incorporates a nonlinear time-varying self-adaptive perturbation strategy and an Archimedean spiral structure into the original WOA. The Archimedean spiral structure enhances the diversity of the solution space, aiding the algorithm in escaping local optima. The nonlinear time-varying self-adaptive optimization dynamic perturbation strategy improves the algorithm's local search capability and enhances solution accuracy. The effectiveness of the proposed algorithm is validated from multiple perspectives using CEC2014 test functions, CEC2017 test functions, and 23 benchmark test functions. The experimental results demonstrate that the enhanced whale optimization algorithm significantly improves population diversity, balances global and local search, and enhances solution accuracy. Additionally, SEWOA exhibits excellent performance in solving three engineering design problems, showcasing its value and wide range of potential applications.

关键词： whale optimization algorithm Swarm intelligence Fluctuation factor Archimedean spiral structure Engineering design

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Research of Improved whale optimization algorithm Based on Variable Convergence Factor and Forced Global Search

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电脑学刊 2021年第5期32卷 148-160页

作者： Cheng Zhu Xu-Hua Pan Qi Chen Yong Zhang Xin-Yi Gao

optimization algorithm is new meta-heuristic optimization algorithm which mimicking the hunting behavior of humpback whales. It enjoys the advantages of simple principle, less parameters, remarkable search ability and good global convergence, also suffer many defects, such as slow convergence speed, low convergence precision and easy to fall into the local optimum. This paper analyzes the problems of original WOA, making use of Good- Point Set to generate initial population. Through variable convergence factor, the progress of search is more flexible and pertinent. At the same time, mechanism of forced global search makes the ability of jumping out local optimum is promoted in substance. Results and convergence curves of benchmark functions indicate that exploration, exploitation, local optima avoidance of algorithm in this paper are competitive with original WOA.

关键词： whale optimization algorithm good-point set nonlinear variable convergence factor meta-heuristic swarm optimization

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A modified whale optimization algorithm for parameter estimation of software reliability growth models

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JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2021年 15卷

作者： Lu, Kezhong Ma, Zongmin Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Chizhou Univ Coll Comp Sci Chizhou Peoples R China

Software reliability growth models are nonlinear in nature, so it is difficult to estimate the proper parameters. An estimation method based on a modified whale optimization algorithm in which parameters are estimated is discussed in this paper. The whale optimization algorithm is a new swarm intelligence optimization algorithm. This algorithm is not perfect enough. Based on the analysis of whale optimization algorithm, we point out the disadvantages of whale optimization algorithm, and propose a modified whale optimization algorithm algorithm from four aspects: choice regarding the dimension, exploration control, encircling prey modified, and candidate solution selection. The experimental results based on 34 benchmark functions demonstrate that the proposed modified whale optimization algorithm has better accuracy. The modified whale optimization algorithm is used to predict software reliability by predicting the faults during the software testing process using software faults' historical data. The proposed modified whale optimization algorithm shows significant advantages in handling a variety of modeling problems such as the exponential model, power model, delayed s-shaped model, and modified sigmoid model. Experimental results show that the fitting accuracy of the modified sigmoid model model is minimal on three data sets. The modified whale optimization algorithm with the modified sigmoid model can provide a better estimate of the software faults.

关键词： Software reliability growth models parameter estimation whale optimization algorithm modified sigmoid model

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An innovative approach for QoS-aware web service composition using whale optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-14页

作者： Dahan, Fadl Prince Sattam bin Abdulaziz Univ Coll Business Adm Hawtat Bani Tamim Dept Management Informat Syst Al Kharj 11942 Saudi Arabia Taiz Univ Taizi 9674 Yemen

With the proliferation of services and the vast amount of data produced by the Internet, numerous services with comparable functionalities but varying Quality of Service (QoS) attributes are potential candidates for meeting user needs. Consequently, the selection of the most suitable services has become increasingly challenging. To address this issue, a synthesis of multiple services is conducted through a composition process to create more sophisticated services. In recent years, there has been a growing interest in QoS uncertainty, given its potential impact on determining an optimal composite service, where each service is characterized by multiple QoS properties (e.g., response time and cost) that are frequently subject to change primarily due to environmental factors. Here, we introduce a novel approach that depends on the Multi-Agent whale optimization algorithm (MA-WOA) for web service composition problem. Our proposed algorithm utilizes a multi-agent system for the representation and control of potential services, utilizing MA-WOA to identify the optimal composition that meets the user's requirements. It accounts for multiple quality factors and employs a weighted aggregation function to combine them into a cohesive fitness function. The efficiency of the suggested method is evaluated using a real and artificial web service composition dataset (comprising a total of 52,000 web services), with results indicating its superiority over other state-of-the-art methods in terms of composition quality and computational effectiveness. Therefore, the proposed strategy presents a feasible and effective solution to the web service composition challenge, representing a significant advancement in the field of service-oriented computing.

关键词： Multi-agent whale optimization algorithm Service-oriented computing Web service composition whale optimization algorithm

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Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

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JOURNAL OF VIBROENGINEERING 2024年第4期26卷 936-947页

作者： Han, Fang Liu, Yingjie Peng, Wen Weifang Univ Sch Machinery & Automat Weifang 261061 Shandong Peoples R China Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Peoples R China

In order to solve the problem of accurate vehicle path tracking and address the issues of low convergence accuracy and susceptibility to local optima in whale optimization algorithm (WOA), a nonlinear convergence factor is proposed and nonlinear inertia weights are introduced to improve the basic WOA. Firstly, the convergence factor in WOA is changed to a nonlinear convergence factor, and a nonlinear inertia weight is introduced to improve the convergence accuracy, local development ability, and global search ability. Then, this algorithm is combined with a fifth-degree polynomial. The simulation results show that the proposed method can solve the problem of vehicle path tracking effectively. And also, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and has stronger applicability. The study can help drivers easily identify safe lane-changing trajectories and area.

关键词： vehicle dynamics path tracking optimal control whale optimization algorithm

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A parameter estimation method for fractional-order nonlinear systems based on improved whale optimization algorithm

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MODERN PHYSICS LETTERS B 2019年第7期33卷

作者： Ren, Gong Yang, Renhuan Yang, Renyu Zhang, Pei Yang, Xiuzeng Xu, Chuangbiao Hu, Baoguo Zhang, Huatao Lu, Yaosheng Cai, Yanning Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Guangdong Peoples R China Sun Yat Sen Univ Sch Engn Guangzhou 511400 Guangdong Peoples R China Guangxi Normal Univ Nationalites Dept Phys & Elect Engn Chongzuo 532200 Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Jiangsu Peoples R China Chinese Acad Sci Key Lab Astron Opt & Technol Nanjing Inst Astron Opt & Technol Nanjing 210042 Jiangsu Peoples R China

Compared to the integer-order systems, the system characteristics of the fractional system are closer to the system characteristics of the real engineering system, the study found beyond that, strictly speaking, various physical phenomena in nature are nonlinear. The problem of parameter estimation problem of fractional-order nonlinear systems can be transformed into the problem of parameter optimization problem by constructing an appropriate fitness function. This paper proposes a hybrid improvement algorithm based on whale optimization algorithm (WOA) to solve this problem and verify it both in Lorenz system and Lu system. The simulation result shows that the hybrid improved algorithm is superior to genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA) and WOA in convergence speed and accuracy.

关键词： Fractional-order Lu nonlinear systems Lorenz nonlinear system whale optimization algorithm

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A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 116卷 454-471页

作者： Ghahremani-Nahr, Javid Kian, Ramez Sabet, Ehsan ACECR Tabriz Iran Nottingham Trent Univ Nottingham Business Sch Nottingham NG1 4FQ England Univ Tabriz Dept Ind Engn Tabriz Iran Loughborough Univ Technol Wolfson Sch Mech Elect & Mfg Engn Loughborough LE11 3TU Leics England

The closed-loop supply chain (CLSC) management as one of the most significant management issues has been increasingly spotlighted by the government, companies and customers, over the past years. The primary reasons for this growing attention mainly down to the governments-driven and environmental related regulations which has caused the overall supply cost to reduce while enhancing the customer satisfaction. Thereby, in the present study, efforts have been made to propose a facility location/allocation model for a multi-echelon multi-product multi-period CLSC network under shortage, uncertainty, and discount on the purchase of raw materials. To design the network, a mixed-integer nonlinear programming (MINLP) model capable of reducing total costs of network is proposed. Moreover, the model is developed using a robust fuzzy programming (RFP) to investigate the effects of uncertainty parameters including customer demand, fraction of returned products, transportation costs, the price of raw materials, and shortage costs. As the developed model was NP-hard, a novel whale optimization algorithm (WOA) aimed at minimizing the network total costs with application of a modified priority-based encoding procedure is proposed. To validate the model and effectiveness of the proposed algorithm, some quantitative experiments were designed and solved by an optimization solver package and the proposed algorithm. Comparison of the outcomes provided by the proposed algorithm and exact solution is indicative of high quality performance of the applied algorithm to find a near-optimal solution within the reasonable computational time. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Closed-loop supply chain network design Modified priority-based encoding Uncertainty whale optimization algorithm

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