scholarly journals Hybrid Whale Optimization Algorithm Based on Three Spiral Searching Strategies and Sine Cosine Operator with Convergence Factor

2021 ◽  
Author(s):  
Ji Zhang ◽  
Kai Yang ◽  
jiesheng wang
Keyword(s):  
Optimization Algorithm ◽  
Population Diversity ◽  
Whale Optimization Algorithm ◽  
Convergence Factor ◽  
Search Range ◽  
Hunting Behavior ◽  
Fireworks Algorithm ◽  
Cosine Operator ◽  
Whale Optimization ◽  
Sine Cosine Algorithm

Abstract Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm inspired by whale hunting behavior. Aiming at the defect that the spiral update mechanism in WOA may exceed the search range, three different spiral searching strategies are first proposed. The agents search with a more reasonable and broader route distribution so as to improve population diversity and traversal. Secondly, an improved sine cosine operator based on the convergence factor was proposed to improve the search efficiency of WOA, where sine search is used for global exploration and cosine search is used for local exploitation. The proposed convergence factor enables search agents to adaptively balance the exploration and exploitation phases with iterations. In the simulation experiment, the effectiveness of three spiral search strategies and sine cosine operator is verified. Then, the whale optimization algorithm (WOA), salp swarm algorithm (SSA), firefly algorithm (FA), moth-flame optimization (MFO) algorithm, fireworks algorithm (FWA), sine cosine algorithm (SCA) and improved WOA are selected for comparison experiments. Finally, the improved WOA is applied to two engineering problems (three-bar truss design problem and the welded beam optimization problem). The experimental results show that compared with other optimization algorithms, the improved WOA has the advantages of high search accuracy, fast convergence speed, and avoiding falling into local optimal values.

scholarly journals An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Relevant Literature ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Whale Optimization Algorithm ◽  
Neighborhood Search ◽  
Whale Optimization ◽  
The Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

Whale optimization algorithm fused with SVM to detect stress in EEG signals

2021 ◽  
Vol 15 (1) ◽  
pp. 87-97
Author(s):  
Richa Gupta ◽  
M. Afshar Alam ◽  
Parul Agarwal
Keyword(s):  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Support Vector ◽  
Eeg Signals ◽  
Hunting Behavior ◽  
Positron Emission ◽  
Whale Optimization ◽  
Signal Activity ◽  
Work Done

Identifying stress and its level has always been a challenging area for researchers. A lot of work is going on around the world on the same. An attempt has been made by the authors in this paper as they present a methodology for detecting stress in EEG signals. Electroencephalogram (EEG) is commonly used to acquire brain signal activity. Though there exist other techniques to extract the same like Functional magnetic resonance imaging (fMRI), positron emission tomography (PET) we have used EEG as it is economical. We have used an open-source dataset for EEG data. Various images are used as the target stressor for collecting EEG signals. After feature selection and extraction, a support vector machine (SVM) with a whale optimization algorithm (WOA) in its kernel function for classification is used. WOA is a bio-inspired meta-heuristic algorithm, based on the hunting behavior of humpback whales. Using this method, we had obtained 91% accuracy for detecting the stress. The paper also compared the previous work done in detecting stress with the work proposed in this paper.

scholarly journals An Improved Whale Algorithm for Setting Standard Scheduled Block Time Based on the Airline Fairness

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Qian Wang ◽  
Yong Tian ◽  
Lili Lin ◽  
Ratnaji Vanga ◽  
Lina Ma
Keyword(s):  
Mathematical Model ◽  
Mathematical Models ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Civil Aviation ◽  
Convergence Factor ◽  
Suggested Approach ◽  
Whale Optimization ◽  
Block Time ◽  
Better Than

Standard scheduled flight block time (SBT) setting is of great concern for Civil Aviation Administration of China (CAAC) and airlines in China. However, the standard scheduled flight block times are set in the form of on-site meetings in practice and current literature has not provided any efficient mathematical models to calculate the flight block times fairly among the airlines. The objective of this paper is to develop and solve a mathematical model for standard SBT setting with consideration of both fairness and reliability. We use whale optimization algorithm (WOA) and an improved version of the whale optimization algorithm (IWOA) to solve the SBT setting problem. A novel nonlinear update equation of convergence factor for random iterations is used in place of the original linear one in the proposed IWOA algorithm. Experimental results show that the suggested approach is effective, and IWOA performs better than WOA in the concerned problem, whose solutions are better compared to the flight block times released by CAAC. In particular, it is interesting to find that MSE, RMSE, MAE, MAPE and Theil of the reliability in 60%–70% range are always the smallest and the average fairness of airlines is better than that of 60%–75% range. The model and solving approach presented in this article have great potential to be applied by CAAC to determine the standard SBTs strategically.

scholarly journals Application of Whale Optimization Algorithm to Inverse Scattering of an Imperfect Conductor with Corners

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Kun-Chou Lee ◽  
Pai-Ting Lu
Keyword(s):  
Inverse Scattering ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Spline Interpolation ◽  
Optimal Solution ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Hunting Behavior ◽  
Whale Optimization ◽  
The Moment

In this paper, the whale optimization algorithm (WOA) is applied to the inverse scattering of an imperfect conductor with corners. The WOA is a new metaheuristic optimization algorithm. It mimics the hunting behavior of humpback whales. The inspiration results from the fact that a whale recognizes the location of a prey (i.e., optimal solution) by swimming around the prey within a shrinking circle and along a spiral-shaped path simultaneously. Initially, the inverse scattering is first transformed into a nonlinear optimization problem. The transformation is based on the moment method solution for scattering integral equations. To treat a target with corners and implement the WOA inverse scattering, the cubic spline interpolation is utilized for modelling the target shape function. Numerical simulation shows that the inverse scattering by WOA not only is accurate but also converges fast.

scholarly journals Improved Whale Optimization Algorithm Based on Inertia Weights for Solving Global Optimization Problems

2020 ◽  
Vol 5 (3) ◽  
pp. 147-155
Author(s):  
I-Ming Chao ◽  
Shou-Cheng Hsiung ◽  
Jenn-Long Liu
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Position Vector ◽  
Hunting Behavior ◽  
Inertia Weight ◽  
Whale Optimization ◽  
Solution Accuracy

Whale Optimization Algorithm (WOA) is a new kind of swarm-based optimization algorithm that mimics the foraging behavior of humpback whales. WOA models the particular hunting behavior with three stages: encircling prey, bubble-net attacking, and search for prey. In this work, we proposed a new linear decreasing inertia weight with a random exploration ability (LDIWR) strategy. It also compared with the other three inertia weight WOA (IWWOA) methods: constant inertia weight (CIW), linear decreasing inertia weight (LDIW), and linear increasing inertia weight (LIIW) by adding fixed or linear inertia weights to the position vector of the reference whale. The four IWWOAs are tested with 23 mathematical and theoretical optimization benchmark functions. Experimental results show that most of IWWOAs outperform the original WOA in terms of solution accuracy and convergence rate when solving global optimization problems. Accordingly, the LDIWR strategy produces a better balance between exploration and exploitation capabilities for multimodal functions.

Research of Improved Whale Optimization Algorithm Based on Variable Convergence Factor and Forced Global Search

電腦學刊 ◽  
2021 ◽  
Vol 32 (5) ◽  
pp. 148-160
Author(s):  
Cheng Zhu Cheng Zhu ◽  
Xu-Hua Pan Cheng Zhu ◽  
Qi Chen Xu-Hua Pan ◽  
Yong Zhang Qi Chen ◽  
Xin-Yi Gao Yong Zhang
Keyword(s):  
Optimization Algorithm ◽  
Global Search ◽  
Whale Optimization Algorithm ◽  
Convergence Factor ◽  
Whale Optimization

scholarly journals An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets

2021 ◽  
Vol 11 (2) ◽  
pp. 216-226
Author(s):  
Ayşe Nagehan Mat ◽  
Onur İnan ◽  
Murat Karakoyun
Keyword(s):  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Heuristic Algorithms ◽  
Clustering Algorithms ◽  
Whale Optimization Algorithm ◽  
Lévy Flight ◽  
Hunting Behavior ◽  
Levy Flight ◽  
Whale Optimization ◽  
Clustering Problems

Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems.

scholarly journals IWOA: An improved whale optimization algorithm for optimization problems

2019 ◽  
Vol 6 (3) ◽  
pp. 243-259 ◽  
Author(s):  
Seyed Mostafa Bozorgi ◽  
Samaneh Yazdani
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Adaptive Strategy ◽  
Function Optimization ◽  
Whale Optimization Algorithm ◽  
Exploration And Exploitation ◽  
Hunting Behavior ◽  
Local Optima ◽  
Adaptive Parameter ◽  
Whale Optimization

Abstract The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overcome this limitation of WOA, in this paper WOA is hybridized with differential evolution (DE) which has good exploration ability for function optimization problems. The proposed method is named Improved WOA (IWOA). The proposed method, combines exploitation of WOA with exploration of DE and therefore provides a promising candidate solution. In addition, IWOA+ is presented in this paper which is an extended form of IWOA. IWOA+ utilizes re-initialization and adaptive parameter which controls the whole search process to obtain better solutions. IWOA and IWOA+ are validated on a set of 25 benchmark functions, and they are compared with PSO, DE, BBO, DE/BBO, PSO/GSA, SCA, MFO and WOA. Furthermore, the effects of dimensionality and population size on the performance of our proposed algorithms are studied. The results demonstrate that IWOA and IWOA+ outperform the other algorithms in terms of quality of the final solution and convergence rate. Highlights The exploration ability of WOA is improved via hybridizing it with DE's mutation. A new adaptive strategy is utilized for balancing the exploration and exploitation abilities. Re-initialization is used to increase the diversity of population. Two improvements are presented for WOA through balancing its exploration and exploitation. The results show that the proposed algorithms can improve the performance of WOA significantly.

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