whale optimization algorithm (WOA) is an optimizationalgorithm 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...
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whale optimization algorithm (WOA) is an optimizationalgorithm 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.
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 pape...
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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.
whale optimization algorithm(WOA) is a biological-inspired optimizationalgorithm with the advantage of global optimization ability, less control parameters and easy implementation. It has been proven to be effective ...
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whale optimization algorithm(WOA) is a biological-inspired optimizationalgorithm 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.
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, ...
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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.
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 lea...
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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 optimizationalgorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimizationalgorithms 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 optimizationalgorithms 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.
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 harmon...
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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.
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 proba...
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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 whaleoptimization-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.
In this article, a new hybrid metaheuristic optimizationalgorithm is proposed to solve the coordination problem of directional overcurrent relays (DOCRs). The proposed algorithm is constructed using hybrid whale opti...
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In this article, a new hybrid metaheuristic optimizationalgorithm 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.
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...
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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.
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...
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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.
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