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Deficiencies of the whale optimization algorithm and its validation method

EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期237卷

作者： Deng, Lingyun Liu, Sanyang Xidian Univ Sch Math & Stat Xian 710126 Peoples R China

In the context of urgent requirements for efficient metaheuristics, the whale optimization algorithm (WOA) is tailored for tackling sophisticated optimization problems and has gained extensive momentum since its emergence. By drawing inspiration from the living habits of whales involving bubble-net attacking, encircling prey and discovering prey, WOA seems powerful to handle challenging problems owing to its unique mechanism. Nevertheless, in this work, through comparative experiments on several standard benchmarks and their shifted versions, we discuss the design flaws of WOA and exhibit the related cause analysis. Furthermore, we employ a useful validation method to test the deficiencies of WOA. Simulation outcomes suggest that WOA integrates a center-bias operator, which makes the algorithm shift-variant and limits its performances to tackle shifted benchmarks.

关键词： whale optimization algorithm Design flaw Benchmark Useful validation method Center-bias operator

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Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2021年第5期17卷

作者： Yue, Yinggao You, Hairong Wang, Shuxin Cao, Li Wenzhou Univ Oujiang Coll Wenzhou 325035 Zhejiang Peoples R China Hubei Univ Arts & Sci Comp Sch Xiangyang Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang Peoples R China

Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine-cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

关键词： Heterogeneous wireless sensor networks whale optimization algorithm coverage optimization sine and cosine algorithm energy balance

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Fundus image segmentation based on random collision whale optimization algorithm

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JOURNAL OF COMPUTATIONAL SCIENCE 2024年 80卷

作者： Zhu, Donglin Zhu, Xingyun Zhang, Yuemai Li, Weijie Hu, Gangqiang Zhou, Changjun Jin, Hu Jeon, Sang-Woon Zhong, Shan Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Hanyang Univ Dept Elect & Elect Engn ERICA Campus Ansan 15588 South Korea Jiangxi Univ Sci & Technol Coll Software Engn Nanchang 330013 Peoples R China

Medical image segmentation is an important technical tool, OTSU algorithm is a common method in threshold segmentation, but with the increase of the number of threshold segmentation, the selection of its threshold is a big problem, and the segmentation effect is difficult to be guaranteed. In order to solve this problem, this paper proposes a random collision whale optimization algorithm to optimize OTSU for reliable image segmentation. The algorithm is called RCWOA for short. Firstly, the Halton sequence is used to uniformly initialize the population to make the population position distribution uniform, and then the dimensional Opposition -based learning of small -hole imaging is introduced to update the whale position and find out the missing feasible solution. Finally, the random collision theory is used to update the position of the optimal individual to improve the quality of the solution, At the same time, it also improves the search ability of the algorithm. In 12 test functions, RCWOA was compared with 6 other algorithms, demonstrating the feasibility and novelty of RCWOA. In 8 experiments of fundus image segmentation, RCWOA was compared with 9 other algorithms. The results showed that RCWOA had a Friedman test composite ranking of 1.3516, ranking at the forefront, and exhibited significantly improved segmentation quality.

关键词： OTSU algorithm whale optimization algorithm Random collision Halton sequence Dimensional Opposition -based learning of small -hole imaging Fundus image

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An improved whale optimization algorithm based on multi-population evolution for global optimization and engineering design problems

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Shen, Ya Zhang, Chen Gharehchopogh, Farhad Soleimanian Mirjalili, Seyedali Hefei Univ Dept Artificial Intelligence & Big Data Hefei 230601 Anhui Peoples R China Guochuang Software Co Ltd Hefei 230094 Anhui Peoples R China Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

The whale optimization algorithm (WOA) tends to suffer from slow convergence speed and quickly falling into the local optimum. In this work, a WOA variant is proposed based on multi-population evolution (MEWOA) to address these problems. Firstly, individuals are classified into three equal-sized sub-populations: exploratory sub -population, exploitative sub-population, and modest sub-population, according to their fitness. Secondly, the moving strategies of each sub-population are assigned using different mechanisms. The exploratory and exploitative sub-populations perform global and local search, respectively, while the modest sub-population randomly explores or exploits the search space. Finally, we introduce a novel population evolution strategy to help MEWOA improve its global optimization ability and avoid local optimum. MEWOA is compared with five state-of-the-art WOA variants and seven basic metaheuristic algorithms over 30 benchmark functions with di-mensions of 100, 500, 1000, and 2000 respectively. It is observed that MEWOA achieves faster convergence speed, shows shorter runtime, and provides higher solution accuracy than other algorithms on the majority of benchmark functions. In addition, we tested MEWOA's ability to solve challenging real-world and constrained optimization problems on the CEC 2019 test suite and four engineering design problems. The experimental re-sults demonstrate the competitiveness and merits of the proposed MEWOA algorithm.

关键词： Metaheuristic whale optimization algorithm Multi -population Global optimization Engineering design optimization algorithm Benchmark WOA

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Hybrid whale optimization algorithm-Levy flight approach for multilevel thresholding image segmentation

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JOURNAL OF ELECTRONIC IMAGING 2022年第5期31卷 051420-051420页

作者： Shivahare, Basu Dev Gupta, Sanjai Kumar Dr APJ Abdul Kalam Tech Univ Dept Comp Sci & Engn Lucknow Uttar Pradesh India Bundelkhand Inst Engn & Technol Jhansi Dept Comp Sci & Engn Jhansi Uttar Pradesh India

Determining the optimal threshold for multilevel image segmentation is a stimulating task. Image segmentation has many applications, such as content-based image retrieval, medical imaging, object detection, object recognition, machine vision, etc. In multilevel thresholding, multiple thresholds are used to segment complex images, and the images are segmented into multiple levels to extract meaningful information for further image analysis. Where threshold number is small, p-tile, Otsu, moment preserving, and entropy thresholding methods attain good accuracy. These classical thresholding methods are time-consuming, computation expensive, and unable to produce good segmentation accuracy with increased threshold numbers. To overcome these problems, the classical thresholding methods are utilized as objective functions with nature-inspired metaheuristic algorithms such as whale optimization algorithm (WOA), simplified swarm optimization (SSO), sine cosine algorithm (SCA), BAT algorithm, WOA-thresholding, and black widow optimization (BWO) to determine optimal multiple thresholds. Nature-inspired metaheuristic algorithms are widely used to search for an optimal solution for global optimization problems. WOA is widely used to find the optimal solution in search space. WOA suffers from entrapment into local optima due to premature convergence behavior. We introduce hybridization of WOA with Levy flight trajectory and named as hybrid whale optimization algorithm-Levy flight (HWOAL), which is utilized to find optimal multiple thresholds for multilevel image segmentation. Levy flight trajectory is utilized to increase diversity in the swarm population. The efficacy of HWOAL is tested on 23 benchmark optimization functions (F1 to F23) and compared with WOA, SSO, SCA, and BAT algorithms. Experiment arms that HWOAL is efficient and makes WOA faster, enhances the ability of exploitation and exploration phase, and can avoid getting stuck into local optima. The segmentation perform

关键词： black widow optimization differential evolution Levy flight multilevel image segmentation optimization algorithms whale optimization algorithm

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A Mixed-Strategy-Based whale optimization algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect

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ENERGIES 2018年第9期11卷 2367页

作者： Ding, Tan Chang, Li Li, Chaoshun Feng, Chen Zhang, Nan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

For solving the parameter optimization problem of a hydraulic turbine governing system (HTGS) with a delayed water hammer (DWH) effect, a Mixed-Strategy-based whale optimization algorithm (MSWOA) is proposed in this paper, in which three improved strategies are designed and integrated to promote the optimization ability. Firstly, the movement strategies of WOA have been improved to balance the exploration and exploitation. In the improved movement strategies, a dynamic ratio based on improved JAYA algorithm is applied on the strategy of searching for prey and a chaotic dynamic weight is designed for improving the strategies of bubble-net attacking and encircling prey. Secondly, a guidance of the elite's memory inspired by Particle swarm optimization (PSO) is proposed to lead the movement of the population to accelerate the convergence speed. Thirdly, the mutation strategy based on the sinusoidal chaotic map is employed to avoid prematurity and local optimum points. The proposed MSWOA are compared with six popular meta-heuristic optimization algorithms on 23 benchmark functions in numerical experiments and the results show that the MSWOA has achieved significantly better performance than others. Finally, the MSWOA is applied on parameter identification problem of HTGS with a DWH effect, and the comparative results confirm the effectiveness and identification accuracy of the proposed method.

关键词： whale optimization algorithm meta-heuristic improved JAYA algorithm chaotic mutation HTGS parameter identification

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Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm

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APPLIED ENERGY 2022年 318卷

作者： Chen, Kui Badji, Abderrezak Laghrouche, Salah Djerdir, Abdesslem Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China Univ Bourgogne Franche Comte UMR CNRS 6174 FEMTO ST F-90000 Belfort France Univ Bourgogne Franche Comte FCLAB CNRS 3539 F-90000 Belfort France

Degradation and cost are the main factors affecting the commercial applications of Polymer Electrolyte Membrane Fuel Cells (PEMFC). This paper proposes a novel degradation prediction for PEMFC in various applications by using Multi-kernel Relevance Vector Regression (MRVR) and whale optimization algorithm (WOA). This method uses data from a vehicle operating under real driving conditions and laboratory data to derive a robust model that covers a wide range of operation. In order to learn degradation trends better, MRVR is adopted to establish the PEMFC degradation prediction model. WOA is used to automatically adjust and optimize the weight and kernel parameters for improving the prediction precision. Proposed method is experimentally verified under different operational conditions. The test results show that compared with a single kernel function, the multi-kernel function can predict degradation of PEMFC more accurately. Compared with other metaheuristic methods, WOA greatly improves the precision of degradation prediction.

关键词： Polymer electrolyte membrane fuel cells Fuel cell vehicle Degradation prediction Multi-kernel relevance vector regression whale optimization algorithm Operating conditions

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MFWOA: Multifactorial whale optimization algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Ye, Lei Ding, Hangqi Xu, Haoran Xiang, Benhua Wu, Yue Gong, Maoguo Natl Univ Def Technol Test Ctr Xian 710100 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Minist Educ Key Lab Collaborat Intelligence Syst Xian 710071 Peoples R China Inner Mongolia Normal Univ Acad Artificial Intelligence Coll Math Sci Hohhot 010022 Peoples R China

Multi-task optimization is an emerging research topic in the field of evolutionary computation, which can exploit the synergy between tasks to solve multiple optimization problems simultaneously and efficiently. However, the correlation and negative transfer problems between tasks are the main challenges faced by multi-task optimization. To this end, this paper proposes anew multi-task optimization algorithm, named Multifactorial whale optimization algorithm (MFWOA). MFWOA uses the whale optimization algorithm (WOA) as a search mechanism and designs an adaptive knowledge transfer strategy to effectively exploit the correlation between tasks. This strategy includes two ways: one is to exchange search experience by adding distance terms from other tasks;the other is to generate new random individuals or optimal individuals through crossover and mutation operations and use them to guide position updates. By combining these two methods, MFWOA can explore a wider area. In addition, in order to better balance the useful information transfer between and within tasks, MFWOA also designs a random mating probability parameter adaptive strategy. Experimental results show that MFWOA can achieve effective and efficient knowledge transfer, and outperforms other multi-task optimization algorithms in terms of convergence speed and accuracy. It is a promising multi-task optimization algorithm.

关键词： Multi-task optimization whale optimization algorithm Knowledge transfer Swarm intelligence

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Energy efficient routing protocol using exponentially-ant lion whale optimization algorithm in wireless sensor networks

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COMPUTER NETWORKS 2021年 197卷 108250-108250页

作者： SureshKumar, K. Vimala, P. Annamalai Univ Dept Elect & Commun Engn Chidambaram 608002 Tamil Nadu India

Energy efficiency is a key challenging task while designing routing model in Wireless Sensor Network (WSN). Various energy efficiency routing approaches are designed to transfer the data packets in a secure path through cluster head (CH), but to increase the lifespan of network and to maintain high scalability poses a complex task in WSN environment. Hence, an energy efficient and trust based routing model is designed using proposed Exponentially-Ant Lion whale optimization (E-ALWO) algorithm to route the data packets to receiver. However, the E-ALWO algorithm is derived by the integration of Exponentially Weighted Moving Average (EWMA) concept with Ant Lion optimization (ALO) and whale optimization algorithm (WOA), respectively. However, the proposed model performs the routing process through CH such that the selection of CH is made using ALWO algorithm based on the constraints, namely energy and delay. The optimal and the secure route used for the data transferring process is computed using proposed E-ALWO algorithm based on the fitness measure. The fitness function considers the factors, namely energy, trust, delay, and distance. Moreover, the path with maximal fitness value is accepted as the routing path, by which the data is forwarded to sink node through CH. The average performance measure achieved by the proposed method by considering without attack scenario in terms of delay, residual energy, throughput, and trust is 0.1484sec, 0.4998J, 42984kbps, and 0.5885.

关键词： Energy Efficiency Trust Based Routing Ant Lion optimization whale optimization algorithm Cluster Head

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A memory interaction quadratic interpolation whale optimization algorithm based on reverse information correction for high-dimensional feature selection

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APPLIED SOFT COMPUTING 2024年 164卷

作者： Miao, Fahui Wu, Yong Yan, Guanjie Si, Xiaomeng Shanghai Maritime Univ Coll Informat Engn 1550 Haigang Ave Shanghai 201306 Peoples R China Univ Macau Zhuhai UM Sci & Technol Res Inst Zhuhai 519000 Peoples R China Shanghai Urban Construct Vocat Coll Dept Basic Educ Shanghai 201415 Peoples R China Shanghai Polytech Univ Inst Artificial Intelligence Sch Comp & Informat Engn Shanghai 201209 Peoples R China

Feature selection is a key technique for data dimensionality reduction, and there are many challenges in facing the exponential expansion phenomenon of high-dimensional decision space. In order to improve the quality of feature selection, we propose a memory interaction quadratic interpolation whale optimization algorithm based on reverse information correction (RQWOA). First, in order to improve the convergence capability of the whale optimization algorithm (WOA) in high-dimensional space, we propose a quadratic interpolation mechanism with memory information interaction, improve the quality of the datum points used for quadratic interpolation through the proposed memory interaction mechanism, and accelerate the convergence speed and accuracy of the algorithm with the help of the quadratic interpolation on the algorithm exploitation capability. In addition, to address the invalid probability flip in the feature selection subset when mapping to the discrete space, we propose a reverse information correction mechanism to judge and correct each dimension in the feature subset by using the reverse complementary information provided by individuals with poor fitness values in the population, so as to better remove invalid or redundant features and improve the quality of feature selection. Finally, in the 15 high-dimensional feature datasets compared with the comparison methods, the accuracy of RQWOA is improved by 7.49 % on average, and the number of features is compressed by 80.42 % on average, which indicates that the RQWOA algorithm has stronger feature identification and redundancy removal capabilities and can better solve the feature selection problem.

关键词： whale optimization algorithm Feature selection Reverse information correction Quadratic interpolation Memory information interaction

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