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Horizontal crossover and co-operative hunting-based whale optimization algorithm for feature selection

KNOWLEDGE-BASED SYSTEMS 2023年 282卷

作者： Chakraborty, Sanjoy Saha, Apu Kumar Ezugwu, Absalom E. Chakraborty, Ratul Saha, Ashim Natl Inst Technol Agartala Dept Comp Sci & Engn Agartala Tripura India Bir Bikram Mem Coll Dept Informat Technol Agartala Tripura India Natl Inst Technol Agartala Dept Math Agartala Tripura India North West Univ Unit Data Sci & Comp 11 Hoffman St ZA-2520 Potchefstroom South Africa Maharaja Bir Bikram Coll Dept Stat Agartala Tripura India

The whale optimization algorithm (WOA) is a widely-used approach for problem-solving, but it has some inherent limitations such as poor exploration capabilities, susceptibility to local optima, and reduced solution accuracy. To address these drawbacks, this study introduces a novel approach known as Horizontal Crossover and Co-operative-hunting-based WOA (HCCWOA). This enhanced algorithm incorporates a weight, co-operative learning techniques, and a horizontal crossover strategy into the WOA framework. The introduction of horizontal crossover bolsters the exploration capabilities of WOA, while the integration of co-operative learning techniques and an inertia weight enhances its exploitation abilities. Recognizing the significance of feature selection in this context, the proposed algorithm is applied in a wrapper mode with the K-Nearest Neighbor (KNN) classifier to select relevant features. The effectiveness of the HCCWOA is rigorously evaluated on twelve classical datasets sourced from the UCI repository. A comprehensive comparison is conducted with eight well-established meta-heuristic algorithms and five recent variations of WOA. Performance metrics, including maximum accuracy, minimum fitness, and minimum feature count, are considered in this comparative analysis. The simulation results affirm that the HCCWOA outperforms other algorithms in at least six out of the twelve datasets. This enhanced performance is further substantiated through statistical analyses, including Friedman's rank test, paired-sample Wilcoxon signed-rank test, two-way ANOVA test, T-test, and boxplot analysis. The combination of empirical results and statistical validation supports the superior effectiveness of the proposed HCCWOA approach, highlighting its ability to effectively explore feature spaces and select the most relevant characteristics for classification tasks.

关键词： whale optimization algorithm K-Nearest Neighbor Feature selection Binary algorithm Dimensionality reduction

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Estimation of Transmission Line Parameters Using Voltage-Current Measurements and whale optimization algorithm

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ENERGIES 2021年第11期14卷 3239页

作者： Hassanein, Wael S. Ahmed, Marwa M. Mosaad, Mohamed I. Abu-Siada, A. King Abdulaziz Univ Dept Ind Engn Fac Engn Jeddah 80204 Saudi Arabia King Abdulaziz Univ Dept Elect Engn Fac Engn Jeddah 80204 Saudi Arabia Yanbu Ind Coll YIC Yanbu Ind City 46452 Saudi Arabia Curtin Univ Elect & Comp Engn Discipline Bentley WA 6152 Australia

Real-time estimation of transmission line (TL) parameters is essential for proper management of transmission and distribution networks. These parameters can be used to detect incipient faults within the line and hence avoid any potential consequences. While some attempts can be found in the literature to estimate TL parameters, the presented techniques are either complex or impractical. Moreover, none of the presented techniques published in the literature so far can be implemented in real time. This paper presents a cost-effective technique to estimate TL parameters in real time. The proposed technique employs easily accessible voltage and current data measured at both ends of the line. For simplicity, only one quarter of the measured data is sampled and utilized in a developed objective function that is solved using the whale optimization algorithm (WOA) to estimate the TL parameters. The proposed objective function comprises the sum of square errors of the measured data and the corresponding estimated values. The robustness of the proposed technique is tested on a simple two-bus and the IEEE 14-bus systems. The impact of uncertainties in the measured data including magnitude, phase, and communication delay on the performance of the proposed estimation technique is also investigated. Results reveal the effectiveness of the proposed method that can be implemented in real time to detect any incipient variations in the TL parameters due to abnormal or fault events.

关键词： transmission lines voltage-current measurements parameters estimation whale optimization algorithm fault location

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Enhancing IoT (Internet of Things) feature selection: A two-stage approach via an improved whale optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Zhang, Kunpeng Liu, Yanheng Wang, Xue Mei, Fang Sun, Geng Zhang, Jindong Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Jilin Peoples R China Jilin Univ Coll Software Changchun 130012 Jilin Peoples R China

Feature selection is a critical task for optimizing system performance and reducing computational overhead in the context of Internet of Things (IoT) applications. This paper presents a two-stage feature selection approach specifically designed for IoT scenarios. In the first stage, a variety of feature dimensionality reduction techniques are employed to significantly reduce the dimensionality of the original feature set by more than 50%. This process results in the creation of a highly effective feature subset, which serves as a solid foundation for subsequent feature selection in the second stage. In the second stage, feature selection is performed on the feature subset by an evolutionary algorithm to obtain high accuracy. Notably, we propose an improved whale optimization algorithm (WOA-HA), which incorporates several improvement factors such as a chaotic H & eacute;non map mechanism (HMM), adaptive coefficient vector (ACV), and a binary operator. To assess the effectiveness of our approach, we compare the performance of WOA-HA with other evolutionary algorithms in terms of feature selection outcomes. Through extensive experiments, our proposed approach achieves an average accuracy of up to 95.5% on Aalto IoT dataset and 98.8% on RT-IoT 2022 dataset, respectively. Meanwhile, the average number of selected features reduced by about 82.5% on Aalto IoT dataset and about 62.3% in the RT-IoT 2022 dataset, respectively. Our proposed approach consistently outperforms other methods and achieves the best performance on most datasets with higher accuracy and fewer features.

关键词： Feature selection Internet of things Feature dimensionality reduction Chaotic whale optimization algorithm

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Spark-based parallel processing whale optimization algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第4期34卷

作者： Alshayeji, Mohammad Behbehani, Bader Ahmad, Imtiaz Kuwait Univ Dept Comp Engn Safat Kuwait

Swarm intelligence meta-heuristic optimization algorithms for optimizing engineering applications have become increasingly popular. The whale optimization algorithm (WOA) is a recent and effective swarm intelligence optimization algorithm that mimics humpback whales' behaviors when optimizing a problem. Applying the algorithm to achieve optimal solutions has shown good results compared to most meta-heuristic optimization algorithms. However, complex applications might require the processing of large-scale computations, which results in down-scaling computational throughput of WOA. Apache Spark, a well-known parallel data processing framework, is the most recent distributed computing framework and has been proven to be the most efficient. In this article, we propose a WOA implementation on top of Apache Spark, represented as SBWOA, to enhance its computational performance while providing higher scalability of the algorithm for handling more complex problems. Compared with the recently reported MapReduce WOA (MR-WOA), and serial implementation of WOA, our approach achieves significant enhancements with respect to computational performance for the highest population size with the maximum number of iterations. SBWOA successfully handles higher-complexity problems which require complex computations.

关键词： Apache Spark big data processing meta-heuristic optimization algorithms swarm intelligence whale optimization algorithm

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Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled whale optimization algorithm

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APPLIED ENERGY 2022年 315卷 119033-119033页

作者： Jadoun, Vinay Kumar Prashanth, G. Rahul Joshi, Siddharth Suhas Narayanan, K. Malik, Hasmat Garcia Marquez, Fausto Pedro Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal Karnataka India SASTRA Deemed Univ Dept Elect & Elect Engn Thanjavur India BEARS NUS Campus Singapore Singapore Univ Castilla La Mancha Ingenium Res Grp Ciudad Real 13071 Spain

The significance and purpose of this multi-objective Combined Heat and Power Economic Emission Dispatch (MO-CHPEED) problem aims to determine the optimal generator output of the co-generation systems, in which two conflicting objectives of the fuel cost and mass of emissions are to be simultaneously minimized. The nonlinear and nonconvex nature of the objective functions needs a good optimization technique to handle it. This paper proposes a Dynamically Controlled whale optimization algorithm (DCWOA) to solve the multi-objective non-convex MO-CHPEED problem in fuzzy environment. The proposed DCWOA is an improved variant of the traditional WOA method by adding dynamically controlled constriction function. Both the conflicting objectives of fuel cost and mass of emissions are handled using Fuzzy Framework. To highlight the performance of the proposed technique, it is tested on the latest CEC test functions and three different MO-CHPEED case studies. The results obtained by proposed DCWOA after 100 independent trails on latest CEC test functions and compared with latest different published methods show the effectiveness and robustness of the proposed method for getting better average and STD values. Moreover, proposed DCWOA is also tested on different dimensioned MO-CHPEED test functions after 100 independent trails and compared with latest techniques. Again the most compromise results given by proposed DCWOA highlights the supremacy of the proposed method in terms of the getting better fitness and best compromise solution obtained and the convergence traits of the MO-CHPEED problem.

关键词： CHP units Multi-objective economic and emission dispatch Trapezoidal fuzzy framework whale optimization algorithm Dynamically controlled constriction function

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A nonlinear randomly reuse-based mutated whale optimization algorithm and its application for solving engineering problems

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Wu, Lei Xu, Dengpan Guo, Qiang Chen, Erqi Xiao, Wensheng China Univ Petr East China Coll Mech & Elect Engn Qingdao 266580 Peoples R China China Univ Petr East China Natl Engn Res Ctr Marine Geophys Prospecting & Exp Qingdao 266580 Peoples R China

The whale optimization algorithm (WOA) is a meta-heuristic optimization algorithm inspired by the social behavior of whale hunting. However, the WOA has the defects of easily falling into local optimum and slow convergence speed. Therefore, to overcome the shortcomings of the original WOA, a novel variant of WOA called nonlinear randomly reuse-based mutated whale optimization algorithm (NRRMWOA) is proposed in this study. In detail, the proposed NRRMWOA includes three novel strategies as compared with original WOA. Firstly, a nonlinear adaptive parameter strategy is introduced to achieve nonlinear adjustment of search pattern along with the iteration time. The second improvement is the random reuse strategy, which can fully utilize the current optimal whale to improve the solution accuracy. The thirdly proposed late disturbance mutation strategy has the function of increasing population diversity in the later stage of iteration. The three proposed strategies complement each other and significantly improve the convergence efficiency and global exploration capability of WOA. To verify the superiority of the proposed NRRMWOA, a series of typical benchmark functions are utilized to conduct comprehensive numerical experiments. Compared with three sets of comparison algorithms (five variants of WOA, five advanced algorithms, and five well-known algorithms), the ARVs (average ranking values) obtained by NRRMWOA are 1.4667, 1.9167, and 2.1333, respectively. The rankings are all the best in the corresponding group. The detailed experimental and statistics results indicate that the presented NRRMWOA has higher convergence speed and better solution quality compared with the three sets of existing algorithms. In addition, 15 real-world optimization problems are employed to further test the performance of NRRMWOA for solving engineering optimization problems. For the results of 15 real-world optimization problems, the proposed NRRMWOA achieves the best results including 8 best "

关键词： whale optimization algorithm Swarm intelligence Meta-heuristic algorithm Engineering design Global optimization

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Optimal techno-economic analysis of a renewable based hybrid microgrid incorporating gravity energy storage system in Indian perspective using whale optimization algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第9期31卷

作者： Dhar, Pinaki Chakraborty, Niladri Jadavpur Univ Power Engn Dept Saltlake Campus Kolkata India

A grid connected renewable energy based hybrid microgrid, incorporating solar PV, biomass generators, and Gravity Energy storage system (GESS) has been proposed in this article. This is done to provide uninterrupted energy to a hilly village of North-East India. A bi-directional energy transaction with the central grid is considered for handling uncertainties and to gain monetary benefit through energy sale. The GESS is only charged through solar PV and biomass generators, to reduce the carbon footprint. The outward energy transaction with the central grid helps in reducing the energy cost for consumers inside the microgrid. Transaction price of energy from the central grid is considered from historical data available in the website of Indian Energy Exchange Limited (IEX). For different combinations of loading pattern and GESS initial storage, the Levelized cost of energy (LCOE) is found to vary in between INR 2.71/kWh to INR 3.41/kWh, which is lower than the current Nagaland state government approved tariff of INR 3.55/kWh. Any subsidy from governmental or non-governmental organizations has not been considered in the present study while calculating the installation cost of Solar PV and Biomass generators to portray the actual scenario. whale optimization algorithm (WOA) has been used to find the optimal solution of the proposed system.

关键词： biomass gravity energy storage levelized cost of energy microgrid solar PV whale optimization algorithm

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A whale optimization algorithm with combined mutation and removing similarity for global optimization and multilevel thresholding image segmentation

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APPLIED SOFT COMPUTING 2023年 137卷

作者： Wang, Jiquan Bei, Jinling Song, Haohao Zhang, Hongyu Zhang, Panli Northeast Agr Univ Coll Engn Harbin 150030 Peoples R China

Because of the problem of the whale optimization algorithm (WOA), such as weak local search ability, easy fall into local optimality, unable to better balance the exploration and exploitation abilities of the algorithm, a whale optimization algorithm with combined mutation and removing similarity (CRWOA) is proposed. Firstly, CRWOA proposed an adaptive adjustment method of the parameters (A) over right arrow and (C) over right arrow in the encircling prey stage and an improved bubble net attack update position formula, so that the updated whale is located around the prey, which improves the local search ability of the algorithm. Secondly, a roulette selection operator is proposed to randomly select individuals participating in position updates from the population, which improves the possibility of producing excellent individuals in the random search stage. Thirdly, a combined mutation operator based on individual fitness value is proposed to better balance the exploration and exploitation abilities of the algorithm. Finally, to better maintain the diversity of the population, an improved removing similarity operation is added. In addition, in the first set of experiments, the CEC 2017 benchmark test functions are selected to compare the performance of CRWOA with other group intelligent optimization algorithms. In the second group of experiments, Kapur and Otsu are used as objective functions to perform multilevel threshold segmentation on 10 grayscale images. The experimental results show that CRWOA is better than the five comparison algorithms in terms of convergence and segmentation quality. (c) 2023 Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm Combined mutation operator Remove similarity operation Multilevel image thresholding Kapur entropy otsu method

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Predicting body fat using a novel fuzzy-weighted approach optimized by the whale optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期217卷

作者： Fan, Zongwen Gou, Jin Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China

Obesity, termed as excessive body fat, is a major public health problem. Being able to accurately predict body fat percentage provides an opportunity for making appropriate prevention and diagnosis. Although various assessment techniques have been applied for body fat estimation, high-cost tests are required using special equipment, and the performance of standard machine learning models are typically impacted by noise data. In this paper, we propose a novel fuzzy-weighted approach for improving body fat prediction using easily accessed body measurements in a cost-effective manner. Given the noise data problem, we design a novel fuzzy weighted operation by extending the minimum and maximum values for calculating the fuzzy weights to cover more possibilities. The fuzzy weights are incorporated into the error constraints of a relative error support vector machine model to improve its robustness to noise data. In addition, the whale optimization algorithm (WOA) is applied in our proposed model for parameter optimization. Our proposed fuzzy-weighted approach delivers excellent results for body fat prediction by outperforming other well-known machine learning models. Further, the statistical test based on the 20 runs of experiments not only confirms that our proposed model could significantly outperform the models under comparison, but also show the fuzzy weighted operation and WOA are effective to improve the proposed approach. This offers a viable alternative for medical decision makers to make appropriate prevention, early diagnosis, and early treatment of body fat related diseases when high-cost measurement techniques are not available.

关键词： Fuzzy-weighted approach Body fat prediction whale optimization algorithm Early prevention and diagnosis Obesity

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Locating electric vehicle charging stations with service capacity using the improved whale optimization algorithm

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ADVANCED ENGINEERING INFORMATICS 2019年 41卷 100901-000页

作者： Zhang, Hao Tang, Lei Yang, Chen Lan, Shulin Beijing Technol & Business Univ Business Sch Beijing Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn HKU ZIRI Lab Phys Internet Hong Kong Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China

This study proposes an Improved whale optimization algorithm (IWOA) that, on the basis of whale optimization algorithm (WOA) designed by Mirjalili and Lewis (2016), introduces Gaussian mutation operator, differential evolution operator, and crowding degree factor to the algorithm framework. Test results with nine classic examples show that IWOA significantly improves WOA's precision and computing speed. We also model the locating problem of Electric Vehicle (EV) charging stations with service risk constraints and apply IWOA to solve it. This paper introduces service risk factors, which include the risk of service capacity and user anxiety, establishing the EV charging station site selection model considering service risk. Computational results based on a large-scale problem instance suggest that both the model and the algorithm are effective to apply in practical locating planning projects and help reduce social costs.

关键词： Electric vehicle charging station location whale optimization algorithm Gaussian variation Differential evolution Crowding factor Service risk

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