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Designing framework of hybrid photovoltaic-biowaste energy system with hydrogen storage considering economic and technical indices using whale optimization algorithm

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ENERGY 2022年第PartA期238卷 121555-121555页

作者： Sun, Hongyue Ebadi, Abdol Ghaffar Toughani, Mohsen Nowdeh, Saber Arabi Naderipour, Amirreza Abdullah, Aldrin Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China Islamic Azad Univ Jouybar Branch Dept Agr Jouybar Iran Islamic Azad Univ Dept Fishery Babol Branch Babol Iran Golestan Tech & Vocat Training Ctr Gorgan Golestan Iran Univ Sains Malaysia Sch Housing Bldg & Planning George Town 11800 Malaysia

In this study economic, reliable and environmentally friendly designing of a hybrid photovoltaic-biowaste-fuel cell (PV-Biowaste-FC) system based on hydrogen storage energy is presented using whale optimization algorithm (WOA) considering the availability of components for 20 years useful lifespan of the project. The WOA is a robust meta-heuristic method for solving optimization problems with high convergence speed and accuracy. The fuel cell system includes an electrolyzer, a hydrogen storage tank, and a fuel cell stack. The objective function is defined as minimization of total net present cost (TNPC) include investment, maintenance and repair and replacement cost and reliability constraint is considered as loss of power supply probability (LPSP). The optimization variables include the area occupied by photovoltaic (PV) panels, number of biowaste units, electrolyzers, hydrogen storage tanks, fuel cells, and inverters that are optimally determined by the WOA considering the TNPC and LPSP. To validate the WOA method in the PV-Biowaste-FC designing, its performance is compared with the particle swarm optimization (PSO) method. Simulations have been carried out for different scenarios including designing of Biowaste-FC and PV-Biowaste-FC system, evaluating the effect of PV, Biowaste and inverter availability and also effect of investment cost of FC on the system designing. Simulation results show that the hybrid system with the participation of all resources and hydrogen storage system as the PV-Biowaste-FC presents the minimum TNPC (2.820 M$) and the lower LPSP (0.0029) compared to the other combinations. Also, the cost of energy (COE) value for the hybrid PV-Biowaste-FC system is ob-tained at 0.5238 $/kWh. The proposed method for designing the hybrid system provides better perfor-mance than the PSO in achieving less TNPC and better reliability with higher convergence speed and accuracy. Further, the effects of the availability of PV units, biowaste, and inverter on

关键词： Hybrid PV-Biowaste-fuel cell system Total net present cost Reliability Environmental pollution Hydrogen storage whale optimization algorithm

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Parameter Reduction in Fuzzy Soft Set Based on whale optimization algorithm

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IEEE ACCESS 2020年 8卷 217268-217281页

作者： Kong, Zhi Zhao, Jie Yang, Qingfeng Ai, Jianwei Wang, Lifu Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China

Parameter reduction is one of the hot topics in soft set theory. Deleting redundant parameters can reduce data and make decision effectively. In this article parameter reduction in fuzzy soft sets based on score decision-making method is discussed. The parameter reduction becomes more and more difficult with the number of parameters increasing. In order to find reduction quickly, a mathematical model of parameter reduction based on the score decision-making method is constructed, and the whale optimization algorithm is improved and tested. Finally, the reduction is obtained by using the improved whale optimization algorithm. The simulation results show that the method is effective.

关键词： Fuzzy soft set parameter reduction whale optimization algorithm score method

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Two-dimensional optimal path planning for autonomous underwater vehicle using a whale optimization algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第9期33卷 e6140-e6140页

作者： Yan, Zheping Zhang, Jinzhong Yang, Zewen Tang, Jialing Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

This paper presents a whale optimization algorithm (WOA) based on forward looking sonar to achieve two-dimensional optimal path planning for an autonomous underwater vehicle. The purpose of path planning is not only to effectively avoid threat regions and safely reach the intended target with minimum fuel cost but also to obtain an optimal or near-optimal path in a complex ocean battlefield environment. The WOA, based on the bubble-net attacking behavior of humpback whales, mimics encircling the prey, attacks with a bubble-net method, and search for prey to effectively determine the global optimal solution in the search space. The WOA not only has fast convergence speed and high calculation accuracy but can also effectively balance exploration and exploitation to avoid falling into a local optimum and obtain the global optimal solution. Five sets of experiments are applied to verify the superiority and stability of the WOA. Compared with other algorithms, such as artificial bee colony, bat algorithm, cuckoo search, flower pollination algorithm, moth-flame optimization algorithm, particle swarm optimization, and water wave optimization, the WOA exhibits better optimization performance and stronger robustness. The experimental results reveal that the WOA can find the shortest path compared with all the other algorithms, and it is an effective and feasible method for solving the path planning problem.

关键词： autonomous underwater vehicle forward looking sonar path planning whale optimization algorithm

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A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm

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APPLIED SOFT COMPUTING 2019年 81卷 105520-000页

作者： Petrovic, Milica Miljkovic, Zoran Jokic, Aleksandar Univ Belgrade Fac Mech Engn Dept Prod Engn Lab Ind Robot & Artificial Intelligence Kraljice Marije 16 Belgrade 11120 35 Serbia

One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired whale optimization algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm optimization algorithm (PSO and cPSO), and hybrid GA-SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Mobile robot Scheduling whale optimization algorithm Biologically inspired algorithms Intelligent manufacturing systems

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Improved surrogate-assisted whale optimization algorithm for fractional chaotic systems' parameters identification

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期110卷 104685-104685页

作者： Wang, Shuhui Hu, Wei Riego, Ignacio Yu, Yongguang Beijing Jiaotong Univ Sch Sci Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Univ Chile Dept Math Ave Beauchef 851 Santiago Chile

Accurate identification of the unknown parameters in fractional chaotic systems is crucial for their precise control. However, the evaluation of these systems is relatively expensive in the sense that its numerical process demands considerable time. Thus, it is essential to design a less time-demanding algorithm with high accuracy. Motivated by this, this paper aims to propose an algorithm with high accuracy and quick convergence speed, leading to a smaller computational budget. To achieve this goal, an Improved Surrogate-Assisted whale optimization algorithm, denoted as ISAWOA, is proposed. A surrogate-assisted model is employed to approximate the fitness function, then, both the Levy flight and the quadratic interpolation techniques are used to improve the exploration and exploitation capacity of the whale optimization algorithm. The simulation results on 20 classical benchmark functions demonstrate that ISAWOA is able to locate an optimal solution with high accuracy and much faster than 14 other algorithms found in the literature. Finally, the proposed algorithm is validated on several representative fractional chaotic systems where ISAWOA is again able to outperform other methods, both in precision and CPU time. The overall test results show that ISAWOA is a promising algorithm with high accuracy, quick convergence, and that it requires a moderate amount of CPU time when dealing with parameter estimation problems on fractional chaotic systems.

关键词： whale optimization algorithm Radial basis function Quadratic interpolation Fractional-order chaotic system

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An improved hybrid whale optimization algorithm for global optimization and engineering design problems

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1557页

作者： Rahimnejad, Abolfazl Akbari, Ebrahim Mirjalili, Seyedali Gadsden, Stephen Andrew Trojovsky, Pavel Trojovska, Eva McMaster Univ Dept Mech Engn Hamilton ON Canada Univ Hradec Kralove Dept Math Hradec Kralove Czech Republic Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

The whale optimization algorithm (WOA) is a widely used metaheuristic optimization approach with applications in various scientific and industrial domains. However, WOA has a limitation of relying solely on the best solution to guide the population in subsequent iterations, overlooking the valuable information embedded in other candidate solutions. To address this limitation, we propose a novel and improved variant called Pbest-guided differential WOA (PDWOA). PDWOA combines the strengths of WOA, particle swarm optimizer (PSO), and differential evolution (DE) algorithms to overcome these shortcomings. In this study, we conduct a comprehensive evaluation of the proposed PDWOA algorithm on both benchmark and real-world optimization problems. The benchmark tests comprise 30-dimensional functions from CEC 2014 Test Functions, while the real-world problems include pressure vessel optimal design, tension/compression spring optimal design, and welded beam optimal design. We present the simulation results, including the outcomes of non-parametric statistical tests including the Wilcoxon signed-rank test and the Friedman test, which validate the performance improvements achieved by PDWOA over other algorithms. The results of our evaluation demonstrate the superiority of PDWOA compared to recent methods, including the original WOA. These findings provide valuable insights into the effectiveness of the proposed hybrid WOA algorithm. Furthermore, we offer recommendations for future research to further enhance its performance and open new avenues for exploration in the field of optimization algorithms. The MATLAB Codes of FISA are publicly available at https:/***/ebrahimakbary/PDWOA.

关键词： Differential evolution algorithm Friedman test Metaheuristic optimization Pbest-guided algorithm Statistical tests whale optimization algorithm Wilcoxon signed-rank test

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Enhanced Fractional Chaotic whale optimization algorithm for Parameter Identification of Isolated Wind-Diesel Power Systems

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IEEE ACCESS 2020年 8卷 140862-140875页

作者： Mousavi, Yashar Alfi, Alireza Kucukdemiral, Ibrahim Beklan Glasgow Caledonian Univ Sch Comp Engn & Built Environm Dept Appl Sci Glasgow G4 0BA Lanark Scotland Shahrood Univ Technol Fac Elect & Robot Engn Shahrood *** Iran

Parameters identification of isolated wind-diesel power systems (WDPS) is a significant issue in stability analysis of the power system as well as guaranteeing the power generation through the control system. In this article, enhanced whale optimization algorithms (EWOA) are proposed to deal with the parameter identification problem of a WDPS system. The proposed EWOA effectively tackles the premature convergence problem of WOA by splitting the population into two subpopulations and updating the position of each whale according to the position of the best agent in its current subpopulation, the position of the other subpopulation's best agent, and the position of the best neighboring agent. Furthermore, fractional chaotic maps are embedded in the search process of EWOA to increase its performance in terms of accuracy. For validation purposes, the proposed algorithms are applied to identify the unknown parameters of WDPS, where different statistical analyzes and comparisons are carried out with other recent state-of-the-art algorithms. Simulation results confirm that the algorithms have less deviation in parameter estimation, more convergence speed, and higher precision in comparison with other algorithms.

关键词： optimization Static VAr compensators Parameter estimation whales Convergence Reactive power Power system stability optimization parameter identification whale optimization algorithm wind-diesel power system

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A Novel Brownian Motion-based Hybrid whale optimization algorithm

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JOURNAL OF INTERNET TECHNOLOGY 2023年第3期24卷 795-808页

作者： Zhang, Liyi Wang, Xiaolin Liu, Ting Zhang, Yong Hu, Yongsheng Tianjin Univ Commerce Informat Engn Coll Tianjin Peoples R China Binzhou Univ Sch Informat Engn Binzhou Peoples R China

The whale optimization algorithm (WOA) has the characteristics of simple implementation and few adjustment parameters, which is remarkable in the optimization algorithm. However, there are shortcomings such as premature convergence, slow convergence in the later period, and low search accuracy. For these shortcomings, a novel Brownian motion-based hybrid whale optimization algorithm (HWOA) is proposed. The search strategy in the Harris hawk optimization algorithm (HHO) is adopted to improve the global search ability of the algorithm, and a soft besiege with progressive rapid dives is introduced to solve the problems of premature convergence and slow convergence. Besides, the Brownian motion model is used to replace WOA. The random parameters in the distance formula are calculated to better simulate the prey's escape during the predation process, and help to jump out of the local optimum. The simulation of 23 benchmark functions shows that compared with the classic and HWOA and metaheuristic, the convergence accuracy and speed have been improved, and the local optimum can be effectively jumped out. At the same time, 10 CEC06-2019 test functions are used to test and analyze it. Compared with WOA, HWOA has better search results, which verifies the superiority of the improved algorithm.

关键词： whale optimization algorithm Harris hawk optimization algorithm Brownian motion Metaheuristic CEC06-2019

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Parameter identification and sensitivity analysis of lithium-ion battery via whale optimization algorithm

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ELECTROCHIMICA ACTA 2022年 404卷 139574-139574页

作者： Pan, Ting-Chen Liu, En-Jui Ku, Hung-Chih Hong, Che-Wun Natl Tsing Hua Univ Dept Power Mech Engn Hsinchu Taiwan Natl Chung Shan Inst Sci & Technol Dept Mech Syst Taoyuan Taiwan

The reaction mechanism of lithium-ion batteries is directly affect system safety and performance. Un-derstanding the actual battery status through model simulation has become an important issue in bat -tery management systems. This research proposes a non-destructive parameter identification method that uses whale optimization algorithm with unique global searching to identify the parameters of the elec-trochemical model and analyze the sensitivity of important parameters in the battery model. First, we establish an experimental platform and conduct four conditions, including 1C, 0.5C, 2C and one driving cycle. Moreover, 1C charge and discharge are taken as the benchmark for the parameter identification. After obtaining the key parameters of the battery, the battery performance prediction is carried out for the remaining three types. The terminal voltage of 0.5C, 2C and the road driving prediction errors are less than 15.45 mV, and the battery SOC errors are less than 1.21%. The results of parameter sensitivity studies show that the electrode-related parameters for the migration of lithium ions are the key to calcu-lating the accuracy of the performance, and the porosity of the electrode has the greatest influence. The accurate parameter identification and the sensitivity provide a future methodology to design a proper battery model for battery management systems. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Lithium-ion battery whale optimization algorithm Parameter identification Sensitivity analysis

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Smooth Exploration System: A novel ease-of-use and specialized module for improving exploration of whale optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2023年第1期272卷

作者： Wu, Lei Chen, Erqi Guo, Qiang Xu, Dengpan Xiao, Wensheng Guo, Jingjing Zhang, Mowen China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China China Univ Petr East China Natl Engn Res Ctr Offshore Geophys & Explorat Equi Qingdao 266580 Peoples R China Nanyang Technol Univ Maritime Inst NTU Sch Civil & Environm Engn Singapore 639798 Singapore Macao Polytech Univ Fac Appl Sci Taipa 999078 Macao Peoples R China Univ Tokyo Grad Sch Frontier Sci Dept Environm Syst Kashiwa 2778563 Japan

whale optimization algorithm (WOA) is a swarm-based optimization algorithm that has recently attracted extensive interest and attention because of its excellent exploitation ability. However, exploration process of WOA needs to be more valued and requires specialized mechanisms to maximize exploration ability. Therefore, based on the insight into the underlying logic of exploration process, a novel smooth exploration system (SES) is proposed to improve exploration process of WOA, and a variant of WOA is proposed called smooth WOA (SWOA). The SES comprises three mechanisms: unordered dimension sampling, random crossover, and sequential mutation. In detail, inspired by the sampling theory, unordered dimension sampling is used to adjust the sparsity of population and the proportion between exploration and exploitation. The random crossover and the sequential mutation complement each other to cover a vast search space. The performance of the SWOA is verified by comparing it with seven variants of WOA and six advanced algorithms based on 31 benchmark functions from CEC2015, CEC2021, and CEC2022. A total of six qualitative methods are introduced to analyze the performance of the SES comprehensively. Coupling coordination evaluation is introduced to present the synergy of mechanisms within the SES, which is an improvement of the ablation experiment and facilitates the consideration of rationality of each evolutionary strategy. Comprehensive qualitative analyses and fair comparisons demonstrate the remarkable performance of the SWOA.(c) 2023 Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm Exploration and exploitation Unordered sampling Qualitative performance analysis Synergy analysis of evolutionary strategies

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