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Multimodal optimization using whale optimization algorithm enhanced with local search and niching technique

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第1期17卷 1-27页

作者： Li, Hui Zou, Peng Huang, Zhiguo Zeng, Chenbo Liu, Xiao Beijing Univ Chem Technol Dept Comp Sci & Technol Beijing 100029 Peoples R China

For some real-world problems, it is desirable to find multiple global optima as many as possible. The multimodal optimization approach which finds multiple optima in a single run shows significant difference with the single modal optimization *** whale optimization algorithm (WOA) is a newly emerging reputable optimization algorithm. Its global search ability has been verified in many benchmark functions and real-world applications. In this paper, we propose a multimodal version of whale optimization algorithm (MMWOA). MMWOA enhances the multimodal search ability of WOA by using the niching technique and improves the local search efficiency of WOA by combining the Gaussian sampling technique. The algorithm has been tested on multimodal optimization benchmark functions recommended by CEC'2013 and on a multimodal optimization problem with non-linear constraints. Experimental results indicate that MMWOA has competitive performance compared with other state-of-the-art multimodal optimization algorithms.

关键词： whale optimization algorithm local search clustering niching algorithms

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A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis

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NEURAL COMPUTING & APPLICATIONS 2021年第20期33卷 13667-13691页

作者： Stephan, Punitha Stephan, Thompson Kannan, Ramani Abraham, Ajith Karunya Inst Technol & Sci Dept Comp Sci & Engn Coimbatore Tamil Nadu India MS Ramaiah Univ Appl Sci Fac Engn & Technol Dept Comp Sci & Engn Bangalore Karnataka India Univ Teknol PETRONAS Dept Elect & Elect Engn Seri Iskandar Perak Malaysia Sci Network Innovat & Res Excellence Machine Intelligence Res Labs Washington DC USA

Breast cancer is the most common among women that leads to death if not diagnosed at early stages. Early diagnosis plays a vital role in decreasing the mortality rate globally. Manual methods for diagnosing breast cancers suffer from human errors and inaccuracy, and consume time. A computer-aided diagnosis (CAD) can overcome the disadvantages of manual methods and helps radiologists for accurate decision-making. A CAD system based on artificial neural network (ANN) optimized using a swarm-based approach can improve the accuracy of breast cancer diagnosis due to its strong prediction capabilities. Artificial bee colony (ABC) and whale optimization are metaheuristic search algorithms used to solve combinatorial optimization problems. This paper proposes a hybrid artificial bee colony with whale optimization algorithm (HAW) by integrating the exploitative employee bee phase of ABC with the bubble net attacking method of whale optimization to propose an employee bee attacking phase. In the employee bee attacking phase, employee bees use exploitation of humpback whales for finding better food source positions. The weak exploration of standard ABC is improved using the proposed mutative initialization phase that forms the explorative phase of the HAW algorithm. HAW algorithm is used in simultaneous feature selection (FS) and parameter optimization of an ANN model. HAW is implemented using backpropagation learning that includes resilient backpropagation (HAW-RP), Levenberg-Marquart (HAW-LM) and momentum-based gradient descent (HAW-GD). These hybrid variants are evaluated using various breast cancer datasets in terms of accuracy, complexity and computational time. HAW-RP variant achieved higher accuracy of 99.2%, 98.5%, 96.3%, 98.8%, 98.7% and 99.1% with low-complexity ANN model when compared to HAW-LM and HAW-GD for WBCD, WDBC, WPBC, DDSM, MIAS and INbreast, respectively.

关键词： ANNs Artificial bee colony Multilayer perceptron Levenberg– Marquardt Resilient backpropagation Momentum-based gradient descent backpropagation whale optimization algorithm

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A hybrid whale optimization algorithm for global optimization

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第1期14卷 431-467页

作者： Chakraborty, Sanjoy Saha, Apu Kumar Sharma, Sushmita Chakraborty, Ratul Debnath, Sudhan Iswar Chandra Vidyasagar Coll Dept Comp Sci & Engn Belonia Tripura India Natl Inst Technol Dept Comp Sci & Engn Agartala Tripura India Natl Inst Technol Dept Math Agartala Tripura India Maharaja Bir Bikram Coll Dept Stat Agartala Tripura India Maharaja Bir Bikram Coll Dept Chem Agartala Tripura India

Notwithstanding the superior performance of the whale optimization algorithm (WOA) on a wide range of optimization issues, the exploitation in WOA gets more preference during the search process, thereby compromising the solution accuracy and diversity and also increases the chance of premature convergence. In this study, a novel modified WOA (m-SDWOA) is presented where the conventional WOA is combined with the modified mutualism phase of symbiotic organisms search (SOS), DE/rand/1/bin mutation strategy of differential evolution (DE), and commensalism phase of SOS. A new selection parameter gamma is introduced to select between exploration and exploitation phases of the algorithm. This overall arrangement balances the ability of the algorithm to explore or exploit. The algorithm's efficiency is verified through 42 benchmark functions and IEEE CEC 19 test suite and comparing the results with various state-of-the-art algorithms comprising basic methods, WOA variants, and DE variants. Statistical analyses like Friedman's test, box plot comparison, and Nemenyi multiple comparison tests are employed to check the proposed algorithm's consistency and statistical superiority. Finally, four real-life engineering design problems have been solved to confirm the problem-solving capability of the proposed m-SDWOA. All these analyses demonstrate the superiority of the proposed algorithm over the compared algorithms.

关键词： whale optimization algorithm Hybrid algorithm Benchmark functions IEEE CEC 2019 functions Nemenyi multiple comparison test Engineering design problem

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Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第3期11卷 573-602页

作者： Tawhid, Mohamed A. Ibrahim, Abdelmonem M. Thompson Rivers Univ Fac Sci Dept Math Stat Kamloops BC V2C 0C8 Canada Al Azhar Univ Fac Sci Dept Math Assiut Branch Assiut Egypt

The principle of any approach for solving feature selection problem is to find a subset of the original features. Since finding a minimal subset of the features is an NP-hard problem, it is necessary to develop and propose practical and efficient heuristic algorithms. The whale optimization algorithm is a recently developed nature-inspired meta-heuristic optimization algorithm that imitates the hunting behavior of humpback whales to solve continuous optimization problems. In this paper, we propose a novel binary whale optimization algorithm (BWOA) to solve feature selection problem. BWOA is especially desirable and appealing for feature selection problem whenever there is no heuristic information that can lead the search to the optimal minimal subset. Nonetheless, whales can find the best features as they hunt the prey. Rough set theory (RST) is one of the effective algorithms for feature selection. We use RST with BWOA as the first experiment, and in the second experiment, we use a wrapper approach with BWOA on three different classifiers for feature selection. Also, we verify the performance and the effectiveness of the proposed algorithm by performing our experiments using 32 datasets from the UCI machine learning repository and comparing the proposed algorithm with some powerful existing algorithms in the literature. Furthermore, we employ two nonparametric statistical tests, Wilcoxon Signed-Rank test, and Friedman test, at 5% significance level. Our results show that the proposed algorithm can provide an efficient tool to find a minimal subset of the features.

关键词： Feature selection Classification whale optimization algorithm Rough set theory Wrapper approach Logistic regression

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

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期42卷 1501-1517页

作者： Li, Maodong Xu, Guanghui Fu, Yuanwang Zhang, Tingwei Du, Li Hubei Univ Technol Dept Automat Wuhan Peoples R China Hubei Univ Technol Sch Elect & Elect Engn Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan Peoples R China

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.

关键词： whale optimization algorithm adaptive inertia weight convergence speed variable spiral position update cooperative convergence mechanism

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Levy Flight Trajectory-Based whale optimization algorithm for Global optimization

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IEEE ACCESS 2017年 5卷 6168-6186页

作者： Ling, Ying Zhou, Yongquan Luo, Qifang Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Key Labs Guangxi High Sch Complex Syst & Computat Nanning 530006 Peoples R China

The whale optimization algorithm (WOA) has been shown to be powerful in searching for an optimal solution. This paper proposes an improvement to the whale optimization algorithm that is based on a Levy flight trajectory and called the Levy flight trajectory-based whale optimization algorithm (LWOA). The LWOA makes the WOA faster and more robust and avoids premature convergence. The Levy flight trajectory is helpful for increasing the diversity of the population against premature convergence and enhancing the capability of jumping out of local optimal optima. This method helps obtaining a better tradeoff between the exploration and exploitation of the WOA. The proposed algorithm is characterized by quick convergence and high precision, and it can effectively get rid of a local optimum. The LWOA is further compared with other well-known nature-inspired algorithms on 23 benchmarks and solving infinite impulse response model identification. The statistical results on the benchmark functions show that the LWOA can significantly outperform others on a majority of the benchmark functions, especially in solving an optimization problem that has high dimensionality. Additionally, the superior identification capability of the proposed algorithm is evident from the results obtained through the simulation study compared with other algorithms. All the results prove the superiority of the LWOA.

关键词： Levy flight trajectory whale optimization algorithm global optimization IIR system

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Multi-objective whale optimization algorithm for content-based image retrieval

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第19期77卷 26135-26172页

作者： Abd El Aziz, Mohamed Ewees, Ahmed A. Hassanien, Aboul Ella Zagazig Univ Fac Sci Dept Math Zagazig Egypt Damietta Univ Dept Comp Dumyat Egypt Univ Bisha Bisha Saudi Arabia Cairo Univ Fac Comp & Informat Informat Technol Cairo Egypt

In the recent years, there are massive digital images collections in many fields of our life, which led the technology to find methods to search and retrieve these images efficiently. The content-based is one of the popular methods used to retrieve images, which depends on the color, texture and shape descriptors to extract features from images. However, the performance of the content-based image retrieval methods depends on the size of features that are extracted from images and the classification accuracy. Therefore, this problem is considered as a multi-objective and there are several methods that used to manipulate it such as NSGA-II and NSMOPSO. However, these methods have drawbacks such as their time and space complexity are large since they used traditional non-dominated sorting methods. In this paper, a new non-dominated sorting based on multi-objective whale optimization algorithm is proposed for content-based image retrieval (NSMOWOA). The proposed method avoids the drawbacks in other non-dominated sorting multi-objective methods that have been used for content-based image retrieval through reducing the space and time complexity. The results of the NSMOWOA showed a good performance in content-based image retrieval problem in terms of recall and precision.

关键词： Content based image retrieval Multi-objective optimization whale optimization algorithm Feature selection Non-dominated sorting

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An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm

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ENERGY REPORTS 2020年 6卷 530-542页

作者： Cao, Yan Li, Yiqing Zhang, Geng Jermsittiparsert, Kittisak Nasseri, Maryam Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China Chulalongkorn Univ Social Res Inst Bangkok 10330 Thailand Islamic Azad Univ Dept Engn Ardabil Branch Ardebil Iran

Recently, the application of the proton exchange membrane fuel cells (PEMFCs) is extensively increasing as a popular renewable energy source. PEMFCs need low temperature for the operation along with high power density and easy implementation ability. These characteristics turned them into the most interesting type of fuel cells. However, PEMFCs need a technique for keeping them in a desired operating point. This is an essential item, especially during the current variation. In this study, a new configuration is proposed for designing an optimal PEMFC system. Two-phase interleaved boost DC/DC converter is also utilized for increasing the output voltage terminal. LQR optimal strategy is used for regulating the PEMFC. For improving the efficiency of the proposed LQR controller, a newly developed version of the whale optimization algorithm, called improved chaotic whale optimization algorithm is proposed. Simulation results of the proposed system are compared with different methods and the results showed that the proposed system has higher efficiency from the viewpoint of the current ripple and overshoot. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Proton exchange membrane fuel cell Parameter estimation LQR whale optimization algorithm Chaotic

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A Nonlinear Adaptive Weight-Based Mutated whale optimization algorithm and Its Application for Solving Engineering Problems

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IEEE ACCESS 2024年 12卷 40225-40254页

作者： Wang, Zhi Li, Yayun Wu, Lei Guo, Qiang SINOPEC Petr Engn Corp Dongying 257000 Peoples R China China Univ Petr Sch Petr Engn Qingdao 266580 Peoples R China

whale optimization algorithm (WOA) is a swarm-based optimization algorithm with exceptional performance and significant originality. In this study, a novel variant of WOA called nonlinear adaptive weight-based mutated WOA (NAWMWOA) is proposed to overcome the shortcomings of original WOA such as easily falling into local optimum and slow convergence speed. In detail, the proposed NAWMWOA includes three novel strategies as comparing with original WOA. Firstly, a nonlinear convergence factor is embedded into the original WOA to balance exploration and exploitation ability. The second improvement is an adaptive weight strategy, which can enhance the exploratory searching trends and improve the solution accuracy. Moreover, the thirdly proposed hybrid mutation strategy has the function of increasing the accuracy and jumping out of the local optimum. The combination of the three strategies significantly improve convergence efficiency and search accuracy of original WOA. To verify the remarkable performance of the proposed NAWMWOA, a series of illustrious WOA variants and state-of-the-art intelligent algorithms is compared with the NAWMWOA on 37 benchmark functions and three typical engineering problems. The details of experimental and statistics results illustrate that the presented NAWMWOA has higher convergence efficiency and better solution accuracy. As a conclusion, the proposed NAWMWOA is a competitive and outstanding algorithm that can effectively solve optimization problems in practical engineering.

关键词： whale optimization algorithm swarm intelligence adaptive weight engineering design

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Interconnected multi-machine power system stabilizer design using whale optimization algorithm

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Protection and Control of Modern Power Systems 2019年第1期4卷 13-23页

作者： Butti Dasu Mangipudi Sivakumar Rayapudi Srinivasarao Gudlavalleru Engineering College GudlavalleruAndhra pradeshIndia JNTUK KakinadaAndhra pradeshIndia

The role of Power System Stabilizer (PSS) in the power system is to provide necessary damping torque to the system in order to suppress the oscillations caused by a variety of disturbances that occur frequently and maintain the stability of the system. In this paper, a PSS design technique is proposed using whale optimization algorithm (WOA) by considering eigenvalue objective function. Two bench mark multi machine test systems: three- generator nine- bus system, two- area four- generator inter connected system working on various operating conditions are considered as case studies and tested with the proposed technique. Extensive simulation results are obtained and effectiveness of proposed WOA-PSS are compared with well - known PSO and DE based stabilizers under several disturbances.

关键词： Power system stabilizer Stability whale optimization algorithm Dynamic performance Generators Eigenvalues

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