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A modified whale optimization algorithm for large-scale global optimization problems

EXPERT SYSTEMS WITH APPLICATIONS 2018年 114卷 563-577页

作者： Sun, Yongjun Wang, Xilu Chen, Yahuan Liu, Zujun Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China

As a new and competitive population-based optimization algorithm, the whale optimization algorithm (WOA) outperforms some other biological-inspired algorithms from the perspective of simplicity and efficiency. However, WOA will get stuck into local optima and degrade accuracy for large-scale global optimization (LSGO) problems. To address the issue, a modified whale optimization algorithm (MWOA) is proposed for solving LSGO problems. In order to balance the exploration and exploitation abilities, a nonlinear dynamic strategy based on a cosine function for updating the control parameter is given. A Levy-flight strategy is adopted to make the algorithm jump out of local optima. Moreover, a quadratic interpolation method is applied to the leader of the population, which enhances the local exploitation ability and improves the solution accuracy. MWOA is tested over 25 well-known benchmark functions with dimensions ranging from 100 to 1000. The experimental results demonstrate the superior performance of MWOA on LSGO, in terms of solution accuracy, convergence speed, and stability compared with other state-of-the-art optimization algorithms. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Large-scale global optimization whale optimization algorithm Levy flight Self-adaptive strategy Quadratic interpolation

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Hybrid whale optimization algorithm with simulated annealing for feature selection

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NEUROCOMPUTING 2017年 260卷 302-312页

作者： Mafarja, Majdi M. Mirjalili, Seyedali Birzeit Univ Dept Comp Sci Birzeit Palestine Grifith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia

Hybrid metaheuristics are of the most interesting recent trends in optimization and memetic algorithms. In this paper, two hybridization models are used to design different feature selection techniques based on whale optimization algorithm (WOA). In the first model, Simulated Annealing (SA) algorithm is embedded in WOA algorithm, while it is used to improve the best solution found after each iteration of WOA algorithm in the second model. The goal of using SA here is to enhance the exploitation by searching the most promising regions located by WOA algorithm. The performance of the proposed approaches is evaluated on 18 standard benchmark datasets from UCI repository and compared with three well-known wrapper feature selection methods in the literature. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which insures the ability of WOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Feature selection Hybrid optimization whale optimization algorithm Simulated annealing Classification WOA optimization

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Optimizing connection weights in neural networks using the whale optimization algorithm

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SOFT COMPUTING 2018年第1期22卷 1-15页

作者： Aljarah, Ibrahim Faris, Hossam Mirjalili, Seyedali Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia

The learning process of artificial neural networks is considered as one of the most difficult challenges in machine learning and has attracted many researchers recently. The main difficulty of training a neural network is the nonlinear nature and the unknown best set of main controlling parameters (weights and biases). The main disadvantages of the conventional training algorithms are local optima stagnation and slow convergence speed. This makes stochastic optimization algorithm reliable alternative to alleviate these drawbacks. This work proposes a new training algorithm based on the recently proposed whale optimization algorithm (WOA). It has been proved that this algorithm is able to solve a wide range of optimization problems and outperform the current algorithms. This motivated our attempts to benchmark its performance in training feedforward neural networks. For the first time in the literature, a set of 20 datasets with different levels of difficulty are chosen to test the proposed WOA-based trainer. The results are verified by comparisons with back-propagation algorithm and six evolutionary techniques. The qualitative and quantitative results prove that the proposed trainer is able to outperform the current algorithms on the majority of datasets in terms of both local optima avoidance and convergence speed.

关键词： optimization whale optimization algorithm WOA Multilayer perceptron MLP Training neural network Evolutionary algorithm

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Modified whale optimization algorithm for solving unrelated parallel machine scheduling problems

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SOFT COMPUTING 2021年第14期25卷 9545-9557页

作者： Al-qaness, Mohammed A. A. Ewees, Ahmed A. Abd Elaziz, Mohamed Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Univ Bisha Dept E Syst Bisha 61922 Saudi Arabia Damietta Univ Dept Comp Dumyat 34511 Egypt Zagazig Univ Dept Math Fac Sci Zagazig 44519 Egypt Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk Russia

Unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup times is considered a hot topic among the researchers, as it presents more complexity to be able to find an optimal solution. Many efforts have been made to solve UPMSP problems and established their performances. Therefore, in this study, a new method is introduced to address UPMSP problems with sequence-dependent and machine-dependent setup time. Our proposed method utilizes two meta-heuristic techniques, the whale optimization algorithm (WOA) and the firefly algorithm (FA), by combining their features to perform this task. The hybrid model is called WOAFA. For more detail, the operators of the FA are employed to improve the exploitation ability of the WOA by serving as a local search. Moreover, the quality of the proposed WOAFA method is tested by comparing with well-known meta-heuristic algorithms over six machines and six jobs, namely (2, 4, 6, 8, 10, and 12 machines) and (20, 40, 60, 80, 100, and 120 jobs).

关键词： whale optimization algorithm Firefly algorithm Meta-heuristic Unrelated parallel machine scheduling problem Local search

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Design and Development of a B-Type Inverter for Harmonic Mitigation in a Grid Integrated System Using whale optimization algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第2期51卷 151-170页

作者： Raj, Devesh Venkatraman, Thiyagarajan Balasubramanian, Muthuselvan Vanaja, Dishore Shunmugham Sri Sivasubramaniya Nadar Coll Engn Elect & Elect Engn Kalavakkam Tamil Nadu India Mar Baselios Coll Engn & Technol Elect & Elect Engn Thiruvananthapuram Kerala India

This paper introduces a B-type inverter and an improved Y source DC-DC converter that use a selective harmonic elimination (SHE) method based on whale optimization to inject power into the grid with reduced harmonic content. The suggested topology contains four unidirectional switches, two bidirectional switches and three DC sources to produce the required output voltage level. The proposed topology can be functioned with equal and unequal DC sources. The developed structure can generate the negative voltages without the use of an H-bridge. It has more redundant states, and it is partially fault tolerant in nature. The proposed structure provides a high value of level to switch ratio. A single input multiple output high gain modular Y-source DC-DC converter is implemented for effective utilization of the PV modules in the suggested system. The triggering angles for the suggested system is generated using the SHE based whale optimization algorithm (WOA). Using the SHE-WOA method a total harmonic distortion (THD) of 4.31% is obtained. The developed system is integrated with the grid and for grid synchronization PI based WOA method is used. Simulation study has identified the presence of only 2.47% of harmonics in the grid current and 2.03% in experimental analysis. Both these harmonic distortions are less than the IEEE-519 standards.

关键词： B-type modular multilevel inverter modular-Y source converter whale optimization algorithm selective harmonic elimination total harmonic distortion (THD)

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Opinion leader detection using whale optimization algorithm in online social network

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 142卷 113016-000页

作者： Jain, Lokesh Katarya, Rahul Sachdeva, Shelly Delhi Technol Univ Dept Comp Sci & Engn New Delhi India Natl Inst Technol Dept Comp Sci & Engn New Delhi India

In the current digital era, optimization is one of the most significant problems in the social network. Most of the issues related to optimization are NP-complete and not possible to solve them in polynomial time. Detection of opinion leader based on their optimized centrality measure is a critical issue. The opinion leaders have a non-trivial influence on the other user's decision-making process and can solve various problems related to the diffusion of new products and innovations in the real world. In this paper, we proposed a new Social Network-based whale optimization algorithm (SNWOA) to find the top-N opinion leaders by measuring the reputation of the user using various standard optimization function in the network. The proposed algorithm is advantageous to determine the opinion leaders because it based on the bubble-net hunting behavior of humpback whales. The algorithm found the best possible solution as the number of users raises progressively in the network;therefore, the general complexity of the algorithm remains unchanged. Besides, we also proposed a new approach to categorize the communities based on the similarity index comprising neighbor similarity and clustering coefficient as the significant components. Initially, we computed the objective function of each user by using their centralities and deployed the proposed algorithm with different optimization functions to identify the local and universal opinion leaders. We implemented the proposed algorithm on the real and synthesized datasets and compared the result based on the accuracy, precision, recall, and F1-score. The result indicates that the proposed algorithms give a better result as compared to the other standard Social Network Analysis (SNA) measures. We also concluded that the community partitioning algorithm is even better than the other community detection algorithms based on different parameters and computational time. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： whale optimization algorithm Node centrality Community detection Nature-inspired meta-heuristic algorithm Online social network Opinion leader

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An efficient and robust bat algorithm with fusion of opposition -based learning and whale optimization algorithm

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INTELLIGENT DATA ANALYSIS 2020年第3期24卷 581-606页

作者： Luo, Jinkun He, Fazhi Yong, Jiashi Wuhan Univ Sch Comp Sci 299 Ba Yi Rd Wuhan 430072 Hubei Peoples R China

Bat algorithm (BA) has the advantage of fast convergence, but there is still room for improvement in accuracy and stability of solution. An efficient and robust fusion bat algorithm (ERFBA) is proposed to overcome these defects. In the population reconstruction, an effective diversity population (EDP) is reconstructed by designing a multi-strategy opposition-based learning with disturbance. In the exploration, an adaptive constraint step whale optimization algorithm is presented to obtain the promising regions with fewer blind spots by exploring EDP. In the exploitation, we design a new BA local search strategy by novel combination between dynamic regulation and Cauchy mutation to get accurate and stable solution. Numerous experiments show that ERFBA has remarkable advantages in accuracy and stability for many high dimension, unimodal and multimodal problems. Moreover, the proposed algorithm is further tested and applied in areas of intelligent data analysis and intelligent design. The results show that the overall performance of the proposed ERFBA is better than other existing algorithms.

关键词： whale optimization algorithm elite opposition-based learning bat algorithm Cauchy mutation real-world problem multi-layer perceptron

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Swarm intelligence based deep learning model via improved whale optimization algorithm and Bi-directional long short-term memory for fault diagnosis of chemical processes

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ISA TRANSACTIONS 2024年 147卷 227-238页

作者： Ji, Chunlei Zhang, Chu Suo, Leiming Liu, Qianlong Peng, Tian Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian 223003 Peoples R China

The chemical production process typically possesses complexity and high risks. Effective fault diagnosis is a key technology for ensuring the reliability and safety of chemical production processes. In this study, a comprehensive fault diagnosis method based on time-varying filtering empirical mode decomposition (TVF-EMD), kernel principal component analysis (KPCA), and an improved whale optimization algorithm (WOA) to optimize bi-directional long short-term memory (BiLSTM) is proposed. This research utilizes TVF-EMD and KPCA to analyze and preprocess the raw data, eliminating noise and and reducing the dimensions of the fault data. Subsequently, BiLSTM is employed for fault data classification. To address the hyperparameters within BiLSTM, the enhanced WOA is used for optimization. Finally, the efficacy and superiority of this approach are validated through two fault diagnosis examples.

关键词： Fault diagnosis Kernel principal component analysis whale optimization algorithm Bi-directional long short-term memory Tennessee eastman process

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RMOWOA: A Revamped Multi-Objective whale optimization algorithm for Maximizing the Lifetime of a Network in Wireless Sensor Networks

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2024年第5-6期52卷 325-366页

作者： Dwivedi, Bhanu Patro, Bachu Dushmanta Kumar Rajkiya Engn Coll REC Dept Comp Sci & Engn Kannauj 209732 India Dr APJ Abdul Kalam Tech Univ AKTU Lucknow 226031 India Manav Rachna Int Inst Res & Studies MRIIRS Sch Engn & Technol Dept Comp Sci & Engn Faridabad 121004 Haryana India

Wireless sensor networks (WSNs) consist of sensor nodes that detect, process, and transmit various types of information to a base station unit. The development of energy-efficient routing protocols is a crucial challenge in WSNs. This study proposes a novel algorithm called RMOWOA, i.e., Revamped Multi-Objective whale optimization algorithm, which utilizes concentric circles with different radii to partition the network. The circles are divided into eight equal sectors, and sections are formed at the intersections of sectors and layers. Each section contains a small number of nodes, and an agent is selected based on specific criteria. The nodes within each section transmit their detected information to the corresponding agent or cluster head. This process is repeated until the base station receives the data. The selection of agents is based on a WOA-based approach, known for enhancing the network's lifetime. The selected agent aggregates the data, performs redundant residue number-based error detection and rectification, and forwards the information to the lower segment's agent within that sector. The proposed RMOWOA algorithm is evaluated through simulation analysis and compared with established benchmark cluster head selection schemes such as SFA- Cluster Head Selection, FCGWO-Cluster Head Selection, and ABC-Cluster Head Selection. The experimental results of the RMOWOA algorithm demonstrate reduced energy consumption and extended network lifespan by effectively balancing the ratio of alive and dead nodes in WSNs.

关键词： Cluster head selection whale optimization algorithm Wireless sensor network Network lifetime

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Parallel Enhanced whale optimization algorithm for Independent Tasks Scheduling on Cloud Computing

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IEEE ACCESS 2024年 12卷 23529-23548页

作者： Khan, Zulfiqar Ali Aziz, Izzatdin Abdul Osman, Nurul Aida Bt Nabi, Said Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar 32610 Malaysia Virtual Univ Pakistan Dept Comp Sci & Informat Technol Lahore 44000 Pakistan

Cloud computing has been imperative for computing systems worldwide since its inception. The researchers strive to leverage the efficient utilization of cloud resources to execute workload quickly in addition to providing better quality of service. Among several challenges on the cloud, task scheduling is one of the fundamental NP-hard problems. Meta-heuristic algorithms are extensively employed to solve task scheduling as a discrete optimization problem and therefore several meta-heuristic algorithms have been developed. However, they have their own strengths and weaknesses. Local optima, poor convergence, high execution time, and scalability are the predominant issues among meta-heuristic algorithms. In this paper, a parallel enhanced whale optimization algorithm is proposed to schedule independent tasks in the cloud with heterogeneous resources. The proposed algorithm improves solution diversity and avoids local optima using a modified encircling maneuver and an adaptive bubble net attacking mechanism. The parallelization technique keeps the execution time low despite its internal complexity. The proposed algorithm minimizes the makespan while improving resource utilization and throughput. It demonstrates the effectiveness of the proposed PEWOA against the best performing enhanced whale optimization algorithm (WOAmM) and Multi-core Random Matrix Particle Swarm optimization (MRMPSO). The algorithm consistently produces better results with varying number of tasks on GoCJ dataset, indicating better scalability. The experiments are conducted in CloudSim utilizing a variety of GoCJ and HCSP instances. Various statistical tests are also conducted to evaluate the significance of the results.

关键词： Task scheduling meta-heuristic whale optimization algorithm cloud computing

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