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whale optimization algorithm for Ship Path optimization in Large-Scale Complex Marine Environment

IEEE ACCESS 2020年 8卷 57168-57179页

作者： Han, Qilong Yang, Xiao Song, Hongtao Sui, Shanshan Zhang, Hui Yang, Zaiqiang Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

With the formulation of United Nations Convention on the Law of the Sea, marine pollution has received widespread attention from various countries. Green navigation is an important requirement for route planning, in which energy consumption is its primary focus. Ships are affected by complex marine meteorological environments, so it is difficult to plan a reasonable route. Some methods have been proposed to solve this problem, but there are some shortcomings, such as no consideration of the effect of wind direction, wind speed and wave. To solve this problem, we introduce a meta-heuristic whale optimization algorithm (WOA), which helps ships find a low-energy-consumption and safe route in a large-scale complex marine environment. The results of our simulation experiments indicate that WOA is more competitive than other state-of-the-art algorithms for route planning.

关键词： Marine pollution marine meteorological meta-heuristic whale optimization algorithm

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whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第3期40卷 4027-4038页

作者： Yan, Zheping Zhang, Jinzhong Tang, Jialing Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of theWOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

关键词： whale optimization algorithm lateral inhibition image matching AUV docking

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whale optimization algorithm based on dynamic pinhole imaging and adaptive strategy

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JOURNAL OF SUPERCOMPUTING 2022年第5期78卷 6090-6120页

作者： Li, Maodong Xu, Guanghui Fu, Bo Zhao, Xilin Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Ener Sch Elect & Elect Engn Wuhan 430068 Peoples R China

To solve the problems of premature convergence and easily falling into local optimum, a whale optimization algorithm based on dynamic pinhole imaging and adaptive strategy is proposed in this paper. In the exploitation phase, the dynamic pinhole imaging strategy allows the whale population to approach the optimal solution faster, thereby accelerating the convergence speed of the algorithm. In the exploration phase, adaptive inertial weights based on dynamic boundaries and dimensions can enrich the diversity of the population and balance the algorithm's exploitation and exploration capabilities. The local mutation mechanism can adjust the search range of the algorithm dynamically. The improved algorithm has been extensively tested in 20 well-known benchmark functions and four complex constrained engineering optimization problems, and compared with the ones of other improved algorithms presented in literatures. The test results show that the improved algorithm has faster convergence speed and higher convergence accuracy and can effectively jump out of the local optimum.

关键词： whale optimization algorithm Pinhole imaging Dynamic boundary Adaptive inertial weight Local mutation

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whale optimization algorithm Based on Lamarckian Learning for Global optimization Problems

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IEEE ACCESS 2019年 7卷 36642-36666页

作者： Zhang, Qiang Liu, Lijie Northeast Petr Univ Coll Comp & Informat Technol Daqing 163318 Peoples R China Heilongjiang Bayi Agr Univ Coll Elect & Informat Daqing 163319 Peoples R China

whale optimization algorithm (WOA) is a population-based meta-heuristic imitating the hunting behavior of humpback whales, which has been successfully applied to solve many real-world problems. Although WOA has a good convergence rate, it cannot achieve good results in finding the global optimal solution of high-dimensional complex optimization problems. The learning mechanism of Lamarckian evolutionism has the advantages of speeding up and strengthening local search. Through this learning mechanism, solutions with certain conditions can acquire higher adaptability with a higher probability by active learning. To enhance the global convergence speed and get better performance, this paper presents a WOA based on Lamarckian learning (WOALam) for solving high-dimensional function optimization problems. First, the population is initialized by good point set theory so that individuals can be evenly distributed in the solution space. Second, the upper confidence bound algorithm is used to calculate the development potential of the individual. Finally, based on the evolutionary theory of Lamarck, individuals with more development potentials are selected to perform the local enhanced search to improve the performance of the algorithm. The WOALam was compared with six variants of WOA on 44 benchmark functions. The experiments proved that the proposed algorithm can balance the global exploring ability and the exploiting ability well. It could obtain better results with fewer iterations and had good convergence speed and accuracy.

关键词： whale optimization algorithm Lamarckian learning good point set upper confidence bound optimization

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whale optimization algorithm based MPPT control of a fuel cell system

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第60期48卷 23230-23241页

作者： Percin, Hasan Bektas Caliskan, Abuzer Firat Univ TR-23119 Elazig Turkiye Firat Univ Fac Engn Dept Elect & Elect Engn TR-23119 Elazig Turkiye

Renewable energy sources have provided a great contribution to global energy demand;However, their intermittent characteristics can cause sustainability and efficiency problems. To handle these, alternative systems are utilized. Among these, proton exchange membrane fuel cells (PEMFCs) stand out with their longer lifecycle, efficient, and costeffective features. However, their performance depends on operating conditions such as temperature, gas pressure, and membrane water content. These nonlinear features require instant and proper control for maximizing efficiency and longer working life. In this study, a whale optimization algorithm (WOA) based maximum power point tracking (MPPT) controller is utilized for a PEMFC system. To validate the proposed controller, the PEMFC system has been analyzed under changing conditions in the MATLAB/Simulink environment. The proposed method has been compared with the other MPPT methods. The results indicate that the proposed controller can provide accurate and fast MPPT performance, less power fluctuations, and higher production efficiency.& COPY;2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： whale optimization algorithm (WOA) Fuel cell PEMFC MPPT MATLAB Simulink

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whale optimization algorithm Using Pinhole Imaging Reverse Learning and Golden Sine Strategy

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2024年第1期18卷

作者： Yue, Xuezhi Jiang, Linfeng Zeng, Yuan Cheng, Yating Liao, Yihang Jiangxi Univ Sci & Technol Ganzhou Peoples R China Guangdong Commun Polytech Guangzhou Peoples R China

While handling problems of certain complex scene optimization, the whale optimization algorithm (WOA) algorithm may be affected by precocious convergence or local optimal solutions, resulting in the accuracy of low convergence and stagnation of dimensional population. To address these limitations, this research presents a whale optimization algorithm, which is established on pinhole imaging reverse learning and the golden sine strategy (LWOAG). Firstly, LWOAG employs pinhole imaging reverse learning to determine the reverse solution for optimal individual in the population, thereby improving the population's quality and algorithm convergence ability. Secondly, LWOAG utilizes the golden sine operator to perform greedy selection after the whale completes the search update, thus extending the search range and increasing the algorithm's global search capacity. Finally, after conducting comprehensive tests on 12 benchmark functions, LWOAG outperforms other enhanced whale optimization algorithms and intelligent algorithms in terms of accuracy and stability.

关键词： Global Search Golden Sine Operator Meta-Heuristic algorithm Pinhole Imaging Reverse Learning whale optimization algorithm

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whale optimization algorithm: a systematic review of contemporary applications, modifications and developments

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NEURAL COMPUTING & APPLICATIONS 2020年第20期32卷 16245-16277页

作者： Rana, Nadim Abd Latiff, Muhammad Shafie Abdulhamid, Shafi'i Muhammad Chiroma, Haruna Univ Teknol Malaysia Fac Comp Skudai 81310 Johor Malaysia Jazan Univ Coll Comp Sci & Informat Technol Jazan Saudi Arabia Fed Univ Technol Dept Cyber Secur Sci Minna Niger State Nigeria Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Yunlin Taiwan

whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique-of humpback whales-for solving the complex optimization problems. It has been widely accepted swarm intelligence technique in various engineering fields due to its simple structure, less required operator, fast convergence speed and better balancing capability between exploration and exploitation phases. Owing to its optimal performance and efficiency, the applications of the algorithm have extensively been utilized in multidisciplinary fields in the recent past. This paper investigates further into WOA of its applications, modifications, and hybridizations across various fields of engineering. The description of the strengths, weaknesses and opportunities to support future research are also explored. The Systematic Literature Review is opted as a method to disseminate the findings and gap from the existing literature. The authors select eighty-two (82) articles as a primary studies out of nine hundred and thirty-nine (939) articles between 2016 and 2020. As per our result, WOA-based techniques are applied in 5 fields and 17 subfields of various engineering domains. 61% work has been found on modification, 27% on hybridization and 12% on multi-objective variants of WOA techniques. The growing research trend on WOA is expected to continue into the future. The review presented in the paper has the potential to motivate expert researchers to propose more novel WOA-based algorithms, and it can serve as an initial reading material for a novice researcher.

关键词： whale optimization algorithm Meta-heuristic Swarm based Bubble-net hunting

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whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification

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SN APPLIED SCIENCES 2019年第5期1卷 390页

作者： Shuaib, Maryam Abdulhamid, Shafi'i Muhammad Adebayo, Olawale Surajudeen Osho, Oluwafemi Idris, Ismaila Alhassan, John K. Rana, Nadim Fed Univ Technol Dept Cyber Secur Sci Minna Nigeria Jazan Univ Coll Comp Sci & Informat Syst Jazan Saudi Arabia

Email has continued to be an integral part of our lives and as a means for successful communication on the internet. The problem of spam mails occupying a huge amount of space and bandwidth, and the weaknesses of spam filtering techniques which includes misclassification of genuine emails as spam (false positives) are a growing challenge to the internet *** research work proposed the use of a metaheuristic optimization algorithm, the whale optimization algorithm (WOA), for the selection of salient features in the email corpus and rotation forest algorithm for classifying emails as spam and non-spam. The entire datasets were used, and the evaluation of the rotation forest algorithm was done before and after feature selection with *** results obtained showed that the rotation forest algorithm after feature selection with WOA was able to classify the emails into spam and non-spam with a performance accuracy of 99.9% and a low FP rate of 0.0019. This shows that the proposed method had produced a remarkable improvement as compared with some previous methods.

关键词： whale optimization algorithm Metaheuristic algorithm Email spam Classification algorithms Rotation forest Feature selection

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whale optimization algorithm applied to load frequency control of a mixed power system considering nonlinearities and PLL dynamics

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ENERGY SYSTEMS-optimization MODELING SIMULATION AND ECONOMIC ASPECTS 2020年第3期11卷 699-728页

作者： Guha, Dipayan Roy, Provas Kumar Banerjee, Subrata Motilal Nehru Natl Inst Technol Dept Elect Engn Allahabad Uttar Pradesh India Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

In this work, an attempt has been made to implement a nature-inspired stochastic evolutionary algorithm, namely whale optimization algorithm (WOA) for exploring optimum and practical solutions of load frequency control (LFC) problem in power system. The proposed WOA mimics the 'bubble-net feeding' strategy of 'humpback whales' in the oceans. The optimization technique is individually applied to a two-area thermal power plant and two-area hydro-thermal-gas power plant with AC-DC tie-line for fine-tuning of the controller parameters. The study further houses the consequences of frequency measurement and the dynamics of a phase-locked loop (PLL) with power system nonlinearities. To establish the efficacy of WOA, the obtained results are compared with results of success history based adaptive differential evolution (SHADE), krill herd algorithm (KHA), and some other well-known control algorithms. Finally, statistical analysis is performed to affirm robustness of the proposed WOA in LFC area.

关键词： Load frequency control whale optimization algorithm Phase-locked loop Transient responses Statistical analysis

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whale optimization algorithm (WOA) for Task Scheduling Problem in Multi-processors Environment

Whale Optimization Algorithm (WOA) for Task Scheduling Probl...

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IEEE Symposium on Industrial Electronics and Applications (ISIEA)

作者： Suliman, Saiful Izwan Khalish, Mohammad Nur Yusof, Yuslinda Wati Mohamad Rahman, Farah Yasmin Abdul Univ Teknol MARA UiTM Coll Engn Sch Elect Engn Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9798350386875;9798350386868

Use of multiprocessors applications to solve complex problems in computer's system contributes to the increase in performance and speed of today's computer. In multiprocessors environment, it is very important to have a great strategy or method on scheduling the processors that are utilized to handle a specific task in the computer system. As each scheduling problem involves problem-specific variables, multiprocessors need different and appropriate algorithm for their problems. This study introduces the whale optimization algorithm (WOA) for task scheduling. Comparisons with other existing methods were also conducted to highlight the efficiency of the proposed WOA in performing task scheduling.

关键词： Multiprocessors task scheduling whale optimization algorithm algorithm

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