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Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications

作者： Zhang-Han Zhuang Ming-Chao Chiang Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (纸本)9781450383042

Since there are way too many possible combinations of hyperparameters for training a desired deep neural network (DNN) model, finding out a set of suitable values for them is typically a difficult topic for the researchers when they use DNN for solving forecasting problems. In addition to manual tuning and trial-and-error for hyperparameters, how to automatically determine the values of hyperparameters has become a critical problem in recent years. In this study, we present a metaheuristic algorithm based on the whale optimization algorithm (WOA) to select suitable hyperparameters for the DNN because WOA demonstrates brilliant convergence speed in many optimization problems and the local optima avoidance mechanism is devised to prevent the searches from trapping into suboptimal solution easily. To validate the feasibility of the proposed algorithm, we compared it with several state-of-the-art hyperparameter selection algorithms for DNN in solving the network-wide traffic speed prediction problem. The experimental results show that WOA not only behaves much more stable but also outperforms all the other hyperparameter selection algorithms compared in this study in terms of the mean square error, mean average error, and mean average percentage error.

关键词： whale optimization algorithm

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Improved whale optimization algorithm applied to design PID plus second-order derivative controller for automatic voltage regulator system

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2020年第6期43卷 541-552页

作者： Mokeddem, Diab Mirjalili, Seyedali Univ Ferhat Abbas Setif 1 Dept Elect Engn Fac Technol Setif Algeria Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia

The paper proposes an Improved whale optimization algorithm (IWOA). Its performance is validated by solving 23 benchmark functions. Comparing the results of IWOA with well-known meta-heuristic algorithms shows its efficiency. Three non-parametric statistical tests, namely, Friedman, Friedman aligned and Quade tests are used to confirm the proposed algorithm's superiority. IWOA is employed to design the parameters of a controller, namely, PID plus second-order derivative (PIDD2) for an automatic voltage regulator system (AVR). In fact, the proposed technique benefits from an evolutionary operator crossover to promote the diversity of solutions while maintaining a reasonable local search behavior. The results are compared with the results of similar algorithms including Particle Swarm optimization, Genetic algorithm, Teaching Learning-Based optimization, Differential Evolution, Cuckoo Search algorithm and Artificial Bee Colony, demonstrating the advantages and the efficiency of the IWOA-PIDD(2)controller. Robustness analysis of the optimal design obtained is conducted by varying the time constants of the AVR system component. The results proved that the proposed technique reliably outperforms most of the current techniques.

关键词： whale optimization algorithm metaheuristic technique arithmetic crossover PIDD(2)controller automatic voltage regulator

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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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Joint Compressed Sensing and Enhanced whale optimization algorithm for Pilot Allocation in Underwater Acoustic OFDM Systems

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IEEE ACCESS 2019年 7卷 95779-95796页

作者： Jiang, Rongkun Wang, Xuetian Cao, Shan Zhao, Jiafei Li, Xiaoran Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Shanghai Univ Dept Commun & Informat Engn Shanghai 200444 Peoples R China

In underwater acoustic-orthogonal frequency division multiplexing (UWA-OFDM) systems, the performance of channel estimation is significantly affected by pilot allocation in the framework of compressed sensing (CS). However, for optimizing the pilot allocation, an exhaustive search method over all possible allocations is computationally prohibitive and random search method may not ensure convergence accuracy. In this paper, the meta-heuristic algorithm of the whale optimization algorithm (WOA) is employed to address this issue. For reinforcing the capability of balancing exploration and exploitation, an enhanced variant of WOA termed EWOA is presented with four optimization strategies. After that, a joint algorithm combining CS with EWOA (CS-EWOA) is proposed for pilot allocation in UWA-OFDM systems. Through extensive simulations, the improvement of EWOA is demonstrated on the majority of benchmark functions over other well-known meta-heuristic algorithms. With regard to bit error rate (BER) and mean square error (MSE) for channel estimation, the proposed CS-EWOA algorithm outperforms the equispaced, random, genetic algorithm (GA), particle swarm optimization (PSO), and WOA-based pilot allocation methods. Moreover, it is robust with varying system subcarriers and channel models. Furthermore, the CS-EWOA exhibits superior convergence performance without increasing the computational complexity compared with the GA-, PSO-, and WOA-based methods in the iteration process of pilot allocation optimization. It can be concluded from the simulation results that the proposed CS-EWOA algorithm is competitive to optimize pilot allocation for channel estimation in UWA-OFDM systems.

关键词： Compressed sensing OFDM whale optimization algorithm underwater acoustic communication channel estimation pilot allocation

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Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 127卷 58-67页

作者： Li, Ling-Ling Sun, Jin Tseng, Ming-Lang Li, Zhi-Gang Hebei Univ Technol Sch Elect Engn State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Sch Elect Engn Key Lab Electromagnet Field & Elect Apparatus Rel Tianjin 300130 Peoples R China Asia Univ Inst Innovat & Circular Econ Suwon South Korea China Med Univ Dept Med Res Taichung Taiwan

This study focuses on the aging evaluation of Insulated gate bipolar transistor (IGBT) modules to ensure their stability during operation. An aging degree evaluation model is proposed based on whale optimization algorithm optimized extreme learning machine (WOA-ELM) algorithm. This study is mainly concentrated on two aspects. One is to use WOA to optimize the input weights and hidden layer biases of ELM to improve its prediction performance. This study tested the performance of WOA-ELM on several benchmark datasets. The results show that the prediction performance of WOA-ELM is better than ELM, genetic algorithm optimized ELM, cuckoo search optimized ELM, and dandelion algorithm optimized ELM. The other is to measure the electrical and thermal characteristic data of IGBT module under different aging conditions by accelerated aging test. Based on the analysis of the experimental data under different aging degrees, a method for evaluating the aging degree of IGBT modules based on WOA-ELM is proposed. Simulation results based on experimental data show that WOA-ELM still has better accuracy and generalization performance than others. In summary, the WOA-ELM algorithm is applicable to the aging evaluation method of IGBT modules proposed in this study which has good practical value. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Insulated gate bipolar transistor module Aging degree evaluation Extreme learning machine whale optimization algorithm

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3DPCNN based on whale optimization algorithm for color image segmentation

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第1期37卷 1499-1511页

作者： Xing, Zhikai Jia, Heming Song, Wenlong Northeast Forestry Univ Harbin Heilongjiang Peoples R China

Considering that the 3D pulse-coupled neural network (3D-PCNN) model has the deficiency of high parameter complexity and low accuracy in color image segmentation, swarm intelligence optimization algorithm is adopted to optimize the image segmentation process. In this paper, whale optimization algorithm (WOA) is adopted to optimize the 3D-PCNN model parameters E and beta. The improved product cross entropy (IPCE) is chosen as the fitness function of optimization algorithm. WOA algorithm is used to find the minimum fitness function, and the corresponding optimal parameters are obtained. Through the study of image segmentation in the image segmentation library of University of Berkeley and the actual plant canopy image, the maximum entropy value and the Tsallis entropy value are compared and analyzed. Experimental results illustrate that the proposed algorithm can obtain more accurate image segmentation effect and higher segmentation rate.

关键词： 3D-PCNN color image segmentation whale optimization algorithm improved product cross entropy

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GWOA: a hybrid genetic whale optimization algorithm for combating attacks in cognitive radio network

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第11期10卷 4345-4360页

作者： Elghamrawy, Sally M. Hassanien, Abou Ella MISR Higher Inst Engn & Technol Mansoura Egypt SRGE Cairo Egypt Cairo Univ Fac Comp & Informat Cairo Egypt

With the rapidly growing number of wireless users and systems more frequency bands are needed, which led to the spectrum scarcity problems. Cognitive radio (CR) is an emerging technology that enables the dynamic spectrum access by optimizing the usage of the available spectrum through giving the unlicensed Secondary Users (SU) the opportunity to exploit the spectrum without causing any interference with the Primary Users' (PU) usage. Security threats is one of the substantial problems issues that must be addressed in CR networks. Primary User Emulation (PUE) attacks are one of the main threats in deploying CRN, where the Malicious Users (MU) mimic the PU signal to confuse the other SUs and corrupt the spectrum sensing process. In this paper, a hybrid Genetic whale optimization algorithm (GWOA) is proposed to optimize the spectrum utilization and to combat the PUE attacks. GWOA integrates the crossover and mutation operations with WOA algorithm to reach the balance between exploitation and exploration phases, to find the optimal solution based on a fitness function. The simulations results show promising performance of GWOA, when compared with recent detection algorithms.

关键词： Securing spectrum sensing Primary user emulation PUE Cognitive radio network (CRN) Genetic algorithm whale optimization algorithm

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An Adaptive whale optimization algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation

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IEEE ACCESS 2019年 7卷 110138-110158页

作者： Li, Yintong Han, Tong Zhao, Hui Gao, Hanjie Air Force Engn Univ Coll Aeronaut Engn Xian 710038 Shaanxi Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130000 Jilin Peoples R China

To overcome the defect of whale optimization algorithm (WOA) being easily fallen into local optimum caused by the ill-distribution of solutions, this paper explores an adaptive WOA variant using Gaussian distribution strategies (GDSs), named GDS-WOA. In GDS-WOA, by means of one GDS, named the Gaussian estimation of distribution method, the superior population information is used to evolve the distribution scope and modify the evolution direction. Moreover, an adaptive framework is adopted to integrate the Gaussian estimation of distribution method and WOA together, in which each individual can update its position using Gaussian estimation of distribution method or WOA according to an adaptive probability parameter. When the algorithm falls into stagnation, another GDS, named Gaussian random walk, is activated to enrich the population diversity and help the algorithm get rid of the local optimum. Additionally, the greedy strategy is carried out to select the offspring from the parents and the generated candidates to fully retain the promising solutions. The GDS-WOA is benchmarked on CEC 2014 test suite, and the performance of GDS-WOA is evaluated by comparing with WOA and its promising variant IWOA, as well as other five state-of-the-art evolutionary algorithms, i.e., COA, VCS, CoBiDE, HFPSO and GWO. The statistical results demonstrate that GDS-WOA outperforms other competitors in terms of convergence efficiency and accuracy. Finally, GDS-WOA is applied to solve the optimal task allocation problem of heterogeneous unmanned combat aerial vehicles (UCAVs). To address this constrained real-world optimizing problem efficiently, the mathematical model of heterogeneous UCAVs task allocation is described with the operational effectiveness value as the objective. The validity and practicauility of the model as well as the performance of GDS-WOA for solving constrained optimization problem are demonstrated by the experimental results.

关键词： whale optimization algorithm CEC 2014 numerical optimization UCAV task allocation

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Optimal swarm decomposition with whale optimization algorithm for weak feature extraction from multicomponent modulation signal

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2019年 122卷 673-691页

作者： Miao, Yonghao Zhao, Ming Makis, Viliam Lin, Jing Beihang Univ Sch Reliabil & Syst Engn Xueyuan Rd 37 Beijing 100083 Peoples R China Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China

Since multicomponent modulation and complicated interference simultaneously exist in the vibration signals caused by the bearing compound fault, the fault feature becomes rather weak and is hard to be extracted. Therefore, the diagnosis of bearing compound fault is always considered as the bottle neck issue of machinery condition monitoring. The decomposition methods which can separate the individual component into different modes with appropriate criterion provide an alternative for solving this issue. However, various weaknesses, including mode mixing, depending on predefined mode number and poor noise suppression, seriously restrict their application range. To overcome the disadvantages, a novel decomposition method, swarm decomposition (SWD), is initially introduced in the diagnosis of the machinery fault. Inspired by the swarm intelligence, SWD can intelligently decompose the signal by using the swarm filtering and iterative algorithm. A series of analysis, including an impulse responses test and a numerical validation, verifies SWD has a remarkable performance in the decomposition of multicomponent modulation signal. Yet, the superiority also relies on the choice of thresholds. Therefore, a novel nature-inspired meta-heuristic optimization algorithm, whale optimization algorithm (WOA), is applied to solve the problem. Benefiting from the virtues of SWD and WOA, the proposed optimal swarm decomposition (OSWD) is more suitable for the weak feature extraction from multicomponent modulation signal. Finally, to further highlight its superiority in the diagnosis of the bearing compound fault, a number of simulation and real datasets from the axle box bearings of locomotive are applied. Results show that OSWD can be considered as an alternative to address the bottle neck issue from the bearing compound fault. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Swarm decomposition whale optimization algorithm Variational mode decomposition Bearing fault diagnosis Compound fault diagnosis Feature extraction

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An improved Levy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8319-S8334页

作者： Abdel-Basset, Mohamed Abdle-Fatah, Laila Sangaiah, Arun Kumar Zagazig Univ Fac Comp & Informat Dept Operat Res Sharqiyah Egypt Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

The consolidation of virtual machine (VM) is the strategy of efficient and intelligent use of cloud datacenters resources. One of the important subproblems of VM consolidation is VM placement problem. The main objective of VM placement problem is to minimize the number of running physical machines or hosts in cloud datacenters. This paper focuses on solving VM placement problem with respect to the available bandwidth which is formulated as variable sized bin packing problem. Moreover, a new bandwidth allocation policy is developed and hybridized with an improved variant of whale optimization algorithm (WOA) called improved Levy based whale optimization algorithm. Cloudsim toolkit is used in order to test the validity of the proposed algorithm on 25 different data sets that generated randomly and compared with many optimization algorithms including: WOA, first fit, best fit, particle swarm optimization, genetic algorithm, and intelligent tuned harmony search. The obtained results are analyzed by Friedman test which indicates the prosperity of the proposed algorithm for minimizing the number of running physical machine.

关键词： Cloud computing Virtual machine placement Variable sized bin packing problem Bandwidth allocation policy Levy flight whale optimization algorithm Metaheuristic

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