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A comprehensive survey: whale optimization algorithm and its applications

SWARM AND EVOLUTIONARY COMPUTATION 2019年 48卷 1-24页

作者： Gharehchopogh, Farhad Soleimanian Gholizadeh, Hojjat Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran Amirkabir Univ Technol Dept Comp Engn & IT Tehran Iran

whale optimization algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

关键词： whale optimization algorithm Meta-heuristics Hybridization Improved optimization

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A whale optimization algorithm with chaos mechanism based on quasi-opposition for global optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 158卷 113612-113612页

作者： Chen, Hui Li, Weide Yang, Xuan Lanzhou Univ Ctr Data Sci Sch Math & Stat Lab Appl Math & Complex Syst Lanzhou 730000 Peoples R China

whale optimization algorithm (WOA), as a newly developed meta-heuristic algorithm, performs well in solving optimization problems. A WOA with chaos mechanism based on quasi-opposition (OBCWOA) is proposed in this paper to overcome the slow convergence speed of the original WOA and to avoid being trapped in local optimal solutions when dealing with high-dimensional problems. We applied two strategies to the original WOA: using chaos mechanism to generate initial value to improve convergence speed of the algorithm and using the opposition-based learning method to balance exploration and development ability of the algorithm to help the algorithm jump out of local optimal solutions. The proposed algorithm is compared with other algorithms on unimodal functions, multimodal functions and fixed dimensional multimodal functions, and is applied to a famous engineering design problem. Results show that combination of the two strategies can improve convergence speed and enhance global search ability of the original WOA. OBCWOA proposed in this paper performs better than the other existing algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： whale optimization algorithm Chaos mechanism Quasi-opposition based learning Global optimization problems

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A hybrid whale optimization algorithm based on modified differential evolution for global optimization problems

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APPLIED INTELLIGENCE 2019年第5期49卷 1982-2000页

作者： Luo, Jun Shi, Baoyu Chongqing Univ Coll Optoelect Engn Chongqing 400030 Peoples R China Chongqing Univ Minist Educ Key Lab Optoelect Technol & Syst Chongqing 400030 Peoples R China

whale optimization algorithm(WOA) is a biological-inspired optimization algorithm with the advantage of global optimization ability, less control parameters and easy implementation. It has been proven to be effective for solving global optimization problems. However, WOA can easily get stuck in the local optimum and may lose the population diversity, suffering from premature convergence. In this work, a hybrid whale optimization algorithm called MDE-WOA was proposed. Firstly, in order to enhance local optimum avoidance ability, a modified differential evolution operator with strong exploration capability is embedded in WOA with the aid of a lifespan mechanism. Additionally, an asynchronous model is employed to accelerate WOA's convergence and improve its accuracy. The proposed MDE-WOA is tested with 13 numerical benchmark functions and 3 structural engineering optimization problems. The results show that MDE-WOA has better performance than others in terms of accuracy and robustness on a majority of cases.

关键词： whale optimization algorithm Differential evolution Global optimization Benchmark functions

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Inversion of the surface duct from radar sea clutter using the improved whale optimization algorithm

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ELECTROMAGNETICS 2019年第8期39卷 611-627页

作者： Yang, Chao Wang, Yizhi Xian Univ Posts & Telecommun Sch Sci Xian Shaanxi Peoples R China

An improved whale optimization algorithm (IWOA) is presented to estimate the surface duct. First, the iteration and selection mechanism in whale optimization algorithm (WOA) are modified to avoid local optimum. Then, orthogonal crossover is embedded into WOA to improve its exploration ability. Finally, the IWOA is applied to benchmark functions and the optimization problem of surface duct to test the performance of IWOA, and the results are compared with those of genetic algorithm (GA), WOA and variant WOA. The comparison results show that IWOA has best accuracy and stability for almost all the benchmark functions and the inversion of the surface duct.

关键词： Surface duct inversion whale optimization algorithm radar sea clutter orthogonal crossover

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Improved whale optimization algorithm for feature selection in Arabic sentiment analysis

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APPLIED INTELLIGENCE 2019年第5期49卷 1688-1707页

作者： Tubishat, Mohammad Abushariah, Mohammad A. M. Idris, Norisma Aljarah, Ibrahim Univ Malaya Dept Artificial Intelligence Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia Univ Jordan Comp Informat Syst Dept King Abdullah II Sch Informat Technol Amman Jordan Univ Jordan Business Informat Technol Dept King Abdullah II Sch Informat Technol Amman Jordan

To help individuals or companies make a systematic and more accurate decisions, sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. whale optimization algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimization algorithms and tend to fall in local optima. To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite Opposition-Based Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. In addition, we also used Information Gain (IG) as a filter features selection technique with WOA using Support Vector Machine (SVM) classifier to reduce the search space explored by WOA. To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six well-known optimization algorithms and two deep learning algorithms. The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.

关键词： Arabic sentiment analysis Support vector machine Information gain whale optimization algorithm

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A balanced whale optimization algorithm for constrained engineering design problems

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APPLIED MATHEMATICAL MODELLING 2019年 71卷 45-59页

作者： Chen, Huiling Xu, Yueting Wang, Mingjing Zhao, Xuehua Wenzhou Univ Dept Comp Sci Wenzhou 325035 Peoples R China Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China

In this study, two novel effective strategies composed of Levy flight and chaotic local search are synchronously introduced into the whale optimization algorithm (WOA) to guide the swarm and further promote the harmony between the inclusive exploratory and neighborhood-informed capacities of the conventional technique and investigate the core searching capabilities of WOA in dealing with optimization tasks. However, the conventional WOA may simply be stuck at local optima or the global best may not be obtained successfully when tackling more complex optimization landscapes, including the multimodal and high dimensional scenarios. To substantiate the efficacy of the enhanced method, it is compared to a set of well-regarded variants of particle swarm optimization and differential evolution. The used benchmark problems are composed of unimodal, multimodal, and fixed-dimensions multimodal functions. Additionally, the proposed balanced method is applied to realize three practical, well-known mathematical models such as tension/compression spring, welded beam, pressure vessel design, three-bar truss design, and I-beam design problems. The experimental results and analysis reveal that the proposed algorithm can outperform other competitors in terms of the convergence speed and the quality of solutions. Promisingly, the proposed method can be treated as an effective and efficient auxiliary tool for more complex optimization models and scenarios. (C) 2019 Published by Elsevier Inc.

关键词： whale optimization algorithm Levy flight Chaotic local search Fixed-dimension functions Complex optimization tasks Welded beam

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Spectrum Sensing in Cognitive Radio Using Actor-Critic Neural Network with Krill Herd-whale optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2019年第1期105卷 335-354页

作者： Bhowmik, Mahua Malathi, P. SPPU PESs Modern Coll Engn Pune Maharashtra India SPPU DY Patil Coll Engn Pune Maharashtra India

Spectrum sensing is the active research area in the Cognitive radio networks that initiates the effective data sharing between the licensed and the unlicensed users of Cognitive Network. Maximizing the detection probability for a provided false alarm rate is a hectic challenge of most of the spectral sensing methods. The paper proposes a spectral sensing method, termed as Krill-Herd whale optimization-based actor critic neural network. The unoccupied spectrum is optimally determined using the proposed method that allocates the free spectrum bands to the primary users instantly such that the delay is minimized due to the effective functioning of the fusion center. For the effective sensing, the Eigen-value-based cooperative sensing is activated in the cognitive radio. The analysis of the proposed method is progressed based on the performance metrics, such as false alarm probability and detection probability. The proposed spectral sensing method outperforms the existing methods that yield a maximum probability of detection and minimum probability of false alarm at a rate of 0.9805 and of 0.009.

关键词： Spectrum sensing Cognitive radio Actor-critic neural network Krill Herd optimization whale optimization algorithm

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Hybrid whale optimization algorithm and Grey Wolf Optimizer algorithm for Optimal Coordination of Direction Overcurrent Relays

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2019年第6-7期47卷 644-658页

作者： Korashy, Ahmed Kamel, Salah Jurado, Francisco Youssef, Abdel-Raheem Aswan Univ Fac Engn Dept Elect Engn Aswan Egypt Univ Jaen Dept Elect Engn EPS Linares Jaen Spain Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China South Valley Univ Fac Engn Dept Elect Engn Qena Egypt

In this article, a new hybrid metaheuristic optimization algorithm is proposed to solve the coordination problem of directional overcurrent relays (DOCRs). The proposed algorithm is constructed using hybrid whale optimization algorithm and gray wolf optimizer (HWGO) that enhance the performance and reliability of the traditional whale optimization algorithm (WOA). The proposed method enhances the exploitative phase of the WOA using a leadership hierarchy of the gray wolf optimizer (GWO) to find the best optimum solution. The coordination problem of DOCRs is subject to numerous constraints. The goal function for optimal coordination of DOCRs aims to minimize total operation time for all primary relay without violation in constraints to maintain reliability and security of the electric power system. The effectiveness of the proposed algorithm has been investigated on four different interconnected networks. The results using HWGO algorithm are compared with the original WOA, GWO, and earlier reported results of other optimization techniques. The results prove the viability of the proposed algorithm to solve the DOCR coordination problem and the ability of the proposed algorithm to overcomes the drawbacks and cover the weakness of the conventional WOA.

关键词： optimal coordination whale optimization algorithm gray wolf optimizer direction overcurrent relays coordination time interval

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Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm

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RESOURCES POLICY 2019年 61卷 250-260页

作者： Alameer, Zakaria Abd Elaziz, Mohamed Ewees, Ahmed A. Ye, Haiwang Zhang Jianhua Wuhan Univ Technol Sch Resources & Environm Engn Wuhan 430070 Hubei Peoples R China Al Azhar Univ Min & Petr Dept Fac Engn Qena Egypt Zagazig Univ Dept Math Fac Sci Zagazig Egypt Damietta Univ Dept Comp Dumyat Egypt

Developing an accurate forecasting model for long-term gold price fluctuations plays a vital role in future investments and decisions for mining projects and related companies. Viewed from this perspective, this paper proposes a novel model for accurately forecasting long-term monthly gold price fluctuations. This model uses a recent meta-heuristic method called whale optimization algorithm (WOA) as a trainer to learn the multilayer perceptron neural network (NN). The results of the proposed model are compared to other models, including the classic NN, particle swarm optimization for NN (PSO-NN), genetic algorithm for NN (GA-NN), and grey wolf optimization for NN (GWO-NN). Additionally, we employ ARIMA models as the benchmark for assessing the capacity of the proposed model. Empirical results indicate the superiority of the hybrid WOA NN model over other models. Moreover, the proposed WOA NN model demonstrates an improvement in the forecasting accuracy obtained from the classic NN, PSO-NN, GA-NN, GWO-NN, and ARIMA models by 41.25%, 24.19%, 25.40%, 25.40%, and 85.84% decrease in mean square error, respectively.

关键词： whale optimization algorithm Multilayer perceptron neural network Gold price fluctuations Forecasting Neural network

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Power system voltage instability risk mitigation via emergency demand response-based whale optimization algorithm

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Protection and Control of Modern Power Systems 2019年第1期4卷 295-308页

作者： Mohammed Amroune Tarek Bouktir Ismail Musirin Department of Electrical Engineering University of Ferhat Abbas Setif 119000SetifAlgeria Faculty of Electrical Engineering Universiti Teknologi MARA40450Shah AlamMalaysia

In recent years, due to the economic and environmental issues, modern power systems often operate proximately to the technical restraints enlarging the probable level of instability risks. Hence, efficient methods for voltage instability prevention are of great importance to power system companies to avoid the risk of large blackouts. In this paper, an event-driven emergency demand response (EEDR) strategy based on whale optimization algorithm (WOA) is proposed to effectively improve system voltage stability. The main objective of the proposed EEDR approach is to maintain voltage stability margin (VSM) in an acceptable range during emergency situations by driving the operating condition of the power system away from the insecure points. The optimal locations and amounts of load reductions have been determined using WOA algorithm. To test the feasibility and the efficiency of the proposed method, simulation studies are carried out on the IEEE 14-bus and real Algerian 114-bus power systems.

关键词： Voltage stability Emergency demand response whale optimization algorithm Blackouts

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