Improved whale optimization algorithm based on random hopping update and random control parameter

2021 ◽  
Vol 40 (1) ◽  
pp. 363-379
Author(s):  
Yanju Guo ◽  
Huan Shen ◽  
Lei Chen ◽  
Yu Liu ◽  
Zhilong Kang
Keyword(s):  
Optimization Algorithm ◽  
Control Parameter ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Original Algorithm ◽  
Multimodal Functions ◽  
Whale Optimization ◽  
Update Strategy ◽  
Random Control ◽  
Better Than

Whale Optimization Algorithm (WOA) is a relatively novel algorithm in the field of meta-heuristic algorithms. WOA can reveal an efficient performance compared with other well-established optimization algorithms, but there is still a problem of premature convergence and easy to fall into local optimal in complex multimodal functions, so this paper presents an improved WOA, and proposes the random hopping update strategy and random control parameter strategy to improve the exploration and exploitation ability of WOA. In this paper, 24 well-known benchmark functions are used to test the algorithm, including 10 unimodal functions and 14 multimodal functions. The experimental results show that the convergence accuracy of the proposed algorithm is better than that of the original algorithm on 21 functions, and better than that of the other 5 algorithms on 23 functions.

Improved whale optimization algorithm based on variable spiral position update strategy and adaptive inertia weight

2021 ◽  
pp. 1-17
Author(s):  
Maodong Li ◽  
Guanghui Xu ◽  
Yuanwang Fu ◽  
Tingwei Zhang ◽  
Li Du
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Convergence Speed ◽  
Whale Optimization Algorithm ◽  
Original Algorithm ◽  
Inertia Weight ◽  
Whale Optimization ◽  
Multimodal Function ◽  
Update Strategy ◽  
Improved Algorithm

 In this paper, a whale optimization algorithm based on adaptive inertia weight and variable spiral position updating strategy is proposed. The improved algorithm is used to solve the problem that the whale optimization algorithm is more dependent on the randomness of the parameters, so that the algorithm’s convergence accuracy and convergence speed are insufficient. The adaptive inertia weight, which varies with the fitness of individual whales, is used to balance the algorithm’s global search ability and local exploitation ability. The variable spiral position update strategy based on the collaborative convergence mechanism is used to dynamically adjust the search range and search accuracy of the algorithm. The effective combination of the two can make the improved whale optimization algorithm converge to the optimal solution faster. It had been used 18 international standard test functions, including unimodal function, multimodal function, and fixed-dimensional function to test the improved whale optimization algorithm in this paper. The test results show that the improved algorithm has faster convergence speed and higher algorithm accuracy than the original algorithm and several classic algorithms. The algorithm can quickly converge to near the optimal value in the early stage, and then effectively jump out of the local optimal through adaptive adjustment, and has a certain ability to solve large-scale optimization problems.

scholarly journals Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

2019 ◽  
Vol 9 (18) ◽  
pp. 3755 ◽  
Author(s):  
Wei Chen ◽  
Haoyuan Hong ◽  
Mahdi Panahi ◽  
Himan Shahabi ◽  
Yi Wang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Landslide Susceptibility ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Economic Losses ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Prediction Ability ◽  
Inference System ◽  
Whale Optimization

The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

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 Loss Minimization in Optimal Reactive Power Planning (ORPP) Based on Whale Optimization Algorithm

2018 ◽  
Vol 7 (3.15) ◽  
pp. 192
Author(s):  
Muhammad Hakimin Nasru ◽  
Ismail Musirin ◽  
Mohamad Khairuzzaman Mohamad Zamani ◽  
Siti Rafidah Abdul Rahim ◽  
Muhamad Hatta Hussain ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimal Allocation ◽  
Reactive Power ◽  
Transmission Loss ◽  
Whale Optimization Algorithm ◽  
Loss Minimization ◽  
Power Scheduling ◽  
Whale Optimization ◽  
Reactive Power Planning ◽  
Better Than

The key of RPP is the optimal allocation of reactive power considering location and size. This paper presents the loss minimization in optimal reactive power planning (ORPP) based on Whale Optimization Algorithm (WOA). The objective is to minimize transmission loss by considering several load conditions at bus 3, bus 15 and bus 21. Reactive Power Scheduling (RPS) and Transformer Tap Changer Setting (TTCS) were set as the control variables. Validation was conducted on the IEEE 30 Bus RTS. Results from the study indicate that the proposed WOA can minimize transmission loss better than Evolutionary Programming (EP). 

scholarly journals Loss Minimization by Reconfiguration in Radial Distribution Network using Whale Optimization Technique

2020 ◽  
Vol 9 (2) ◽  
pp. 1746-1751
Keyword(s):  
Radial Distribution ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Heuristic Algorithms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Whale Optimization Algorithm ◽  
Function Evaluation ◽  
Voltage Profile ◽  
Whale Optimization

This paper provides a new approach for solving the problem of network reconfiguration in the presence of Whale Optimization Algorithm (WOA). It is aimed at reducing actual power loss and enlightening the voltage profile in the supply system. The voltage and branch current capacity constraints have been included in the objective function evaluation. The method has been evaluated at three separate heuristic algorithms on 33-bus radial distribution systems to demonstrate the performance and effectiveness of the proposed method. In this paper the comparison of performance of two latest optimization techniques such as Whale Optimization Algorithm (WOA) with classic optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The new optimization technique produces better result compare to other two optimization logarithm..

Optimal total harmonic distortion minimization in multilevel inverter using improved whale optimization algorithm

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Aala Kalananda Vamsi Krishna Reddy ◽  
Komanapalli Venkata Lakshmi Narayana
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Harmonic Distortion ◽  
Total Harmonic Distortion ◽  
Whale Optimization Algorithm ◽  
Matlab Simulation ◽  
Multilevel Inverters ◽  
Whale Optimization ◽  
Distortion Minimization ◽  
Optimum Solution

AbstractThis paper presents the solution to mitigate the total harmonic distortion (THD) in multilevel inverters (MLIs) using novel improved whale optimization algorithm (IWOA). The IWOA falls under the category of swarm-based nature inspired optimization algorithms. It uses a novel diffusion process using a random walk technique and utilizes an additional ranking system to estimate the optimum solution to minimize THD. Moreover, THD minimization is further accomplished through nine various meta-heuristic algorithms for investigation and comparative analysis. The selected algorithms along with the proposed IWOA are rigorously tested on single phase 5 and 7 level cascaded H-Bridge MLIs for various performance parameters such as consistency, computational efficiency and speed of convergence. It is found that the proposed algorithm outperforms the nine algorithms and is efficient for THD minimization for modulation index (MI) in the range of 0–1. The results are analyzed and reported after thorough verification using MATLAB simulation.

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.

Adaptive whale optimization for intelligent multi-constraints power quality improvement under deregulated environment

2019 ◽  
Vol 17 (3) ◽  
pp. 490-514
Author(s):  
Niharika Thakur ◽  
Y.K. Awasthi ◽  
Manisha Hooda ◽  
Anwar Shahzad Siddiqui
Keyword(s):  
Power Quality ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Quality Improvements ◽  
Content Type ◽  
Facts Devices ◽  
Whale Optimization ◽  
Quality Issues ◽  
Better Than ◽  
Bus System

Purpose Power quality issues highly affect the secure and economic operations of the power system. Although numerous methodologies are reported in the literature, flexible alternating current transmission system (FACTS) devices play a primary role. However, the FACTS devices require optimal location and sizing to perform the power quality enhancement effectively and in a cost efficient manner. This paper aims to attain the maximum power quality improvements in IEEE 30 and IEEE 57 test bus systems. Design/methodology/approach This paper contributes the adaptive whale optimization algorithm (AWOA) algorithm to solve the power quality issues under deregulated sector, which enhances available transfer capability, maintains voltage stability, minimizes loss and mitigates congestions. Findings Through the performance analysis, the convergence of the final fitness of AWOA algorithm is 5 per cent better than artificial bee colony (ABC), 3.79 per cent better than genetic algorithm (GA), 2,081 per cent better than particle swarm optimization (PSO) and fire fly (FF) and 2.56 per cent better than whale optimization algorithm (WOA) algorithms at 400 per cent load condition for IEEE 30 test bus system, and the fitness convergence of AWOA algorithm for IEEE 57 test bus system is 4.44, 4.86, 5.49, 7.52 and 9.66 per cent better than FF, ABC, WOA, PSO and GA, respectively. Originality/value This paper presents a technique for minimizing the power quality problems using AWOA algorithm. This is the first work to use WOA-based optimization for the power quality improvements.

scholarly journals Kapur’s Entropy for Color Image Segmentation Based on a Hybrid Whale Optimization Algorithm

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 318 ◽  
Author(s):  
Chunbo Lang ◽  
Heming Jia
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Similarity Index ◽  
Whale Optimization Algorithm ◽  
Color Image Segmentation ◽  
De Algorithm ◽  
Whale Optimization ◽  
Kapur’S Entropy

In this paper, a new hybrid whale optimization algorithm (WOA) called WOA-DE is proposed to better balance the exploitation and exploration phases of optimization. Differential evolution (DE) is adopted as a local search strategy with the purpose of enhancing exploitation capability. The WOA-DE algorithm is then utilized to solve the problem of multilevel color image segmentation that can be considered as a challenging optimization task. Kapur’s entropy is used to obtain an efficient image segmentation method. In order to evaluate the performance of proposed algorithm, different images are selected for experiments, including natural images, satellite images and magnetic resonance (MR) images. The experimental results are compared with state-of-the-art meta-heuristic algorithms as well as conventional approaches. Several performance measures have been used such as average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), Wilcoxon’s rank sum test, and Friedman test. The experimental results indicate that the WOA-DE algorithm is superior to the other meta-heuristic algorithms. In addition, to show the effectiveness of the proposed technique, the Otsu method is used for comparison.

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