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Dr. Shu-Chuan Chu
Shandong University of Science & Technology

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0 Evolutionary Computation
0 Image Processing
0 Swarm Intelligence
0 Wireless Sensor Networks
0 Artificial Inelligence

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Wireless Sensor Networks
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Evolutionary Computation

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Original article
Published: 03 June 2021 in Complex & Intelligent Systems
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This work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the Phasmatodea (stick insect) population, called the Phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.

ACS Style

Pei-Cheng Song; Shu-Chuan Chu; Jeng-Shyang Pan; Hongmei Yang. Simplified Phasmatodea population evolution algorithm for optimization. Complex & Intelligent Systems 2021, 1 -19.

AMA Style

Pei-Cheng Song, Shu-Chuan Chu, Jeng-Shyang Pan, Hongmei Yang. Simplified Phasmatodea population evolution algorithm for optimization. Complex & Intelligent Systems. 2021; ():1-19.

Chicago/Turabian Style

Pei-Cheng Song; Shu-Chuan Chu; Jeng-Shyang Pan; Hongmei Yang. 2021. "Simplified Phasmatodea population evolution algorithm for optimization." Complex & Intelligent Systems , no. : 1-19.

Research article
Published: 30 April 2021 in Security and Communication Networks
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The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

ACS Style

Jeng-Shyang Pan; Fang Fan; Shu-Chuan Chu; Hui-Qi Zhao; Gao-Yuan Liu. A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks. Security and Communication Networks 2021, 2021, 1 -15.

AMA Style

Jeng-Shyang Pan, Fang Fan, Shu-Chuan Chu, Hui-Qi Zhao, Gao-Yuan Liu. A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks. Security and Communication Networks. 2021; 2021 ():1-15.

Chicago/Turabian Style

Jeng-Shyang Pan; Fang Fan; Shu-Chuan Chu; Hui-Qi Zhao; Gao-Yuan Liu. 2021. "A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks." Security and Communication Networks 2021, no. : 1-15.

Journal article
Published: 14 April 2021 in Wireless Communications and Mobile Computing
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The wireless sensor network is a network composed of sensor nodes self-organizing through the application of wireless communication technology. The application of wireless sensor networks (WSNs) requires high security, but the transmission of sensitive data may be exposed to the adversary. Therefore, to guarantee the security of information transmission, researchers propose numerous security authentication protocols. Recently, Wu et al. proposed a new three-factor authentication protocol for WSNs. However, we find that their protocol cannot resist key compromise impersonation attacks and known session-specific temporary information attacks. Meanwhile, it also violates perfect forward secrecy and anonymity. To overcome the proposed attacks, this paper proposes an enhanced protocol in which the security is verified by the formal analysis and informal analysis, Burross-Abadii-Needham (BAN) logic, and ProVerif tools. The comparison of security and performance proves that our protocol has higher security and lower computational overhead.

ACS Style

Tsu-Yang Wu; Lei Yang; Zhiyuan Lee; Shu-Chuan Chu; Saru Kumari; Sachin Kumar. A Provably Secure Three-Factor Authentication Protocol for Wireless Sensor Networks. Wireless Communications and Mobile Computing 2021, 2021, 1 -15.

AMA Style

Tsu-Yang Wu, Lei Yang, Zhiyuan Lee, Shu-Chuan Chu, Saru Kumari, Sachin Kumar. A Provably Secure Three-Factor Authentication Protocol for Wireless Sensor Networks. Wireless Communications and Mobile Computing. 2021; 2021 ():1-15.

Chicago/Turabian Style

Tsu-Yang Wu; Lei Yang; Zhiyuan Lee; Shu-Chuan Chu; Saru Kumari; Sachin Kumar. 2021. "A Provably Secure Three-Factor Authentication Protocol for Wireless Sensor Networks." Wireless Communications and Mobile Computing 2021, no. : 1-15.

Article
Published: 12 April 2021 in Applied Intelligence
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The Pigeon-Inspired Optimization (PIO) algorithm is an intelligent algorithm inspired by the behavior of pigeons returned to the nest. The binary pigeon-inspired optimization (BPIO) algorithm is a binary version of the PIO algorithm, it can be used to optimize binary application problems. The transfer function plays a very important part in the BPIO algorithm. To improve the solution quality of the BPIO algorithm, this paper proposes four new transfer function, an improved speed update scheme, and a second-stage position update method. The original BPIO algorithm is easier to fall into the local optimal, so a new speed update equation is proposed. In the simulation experiment, the improved BPIO is compared with binary particle swarm optimization (BPSO) and binary grey wolf optimizer (BGWO). In addition, the benchmark test function, statistical analysis, Friedman’s test and Wilcoxon rank-sum test are used to prove that the improved algorithm is quite effective, and it also verifies how to set the speed of dynamic movement. Finally, feature selection was successfully implemented in the UCI data set, and higher classification results were obtained with fewer feature numbers.

ACS Style

Jeng-Shyang Pan; Ai-Qing Tian; Shu-Chuan Chu; Jun-Bao Li. Improved binary pigeon-inspired optimization and its application for feature selection. Applied Intelligence 2021, 1 -19.

AMA Style

Jeng-Shyang Pan, Ai-Qing Tian, Shu-Chuan Chu, Jun-Bao Li. Improved binary pigeon-inspired optimization and its application for feature selection. Applied Intelligence. 2021; ():1-19.

Chicago/Turabian Style

Jeng-Shyang Pan; Ai-Qing Tian; Shu-Chuan Chu; Jun-Bao Li. 2021. "Improved binary pigeon-inspired optimization and its application for feature selection." Applied Intelligence , no. : 1-19.

Research article
Published: 29 March 2021 in Journal of Advanced Transportation
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This paper studies the problem of intelligence optimization, a fundamental problem in analyzing the optimal solution in a wide spectrum of applications such as transportation and wireless sensor network (WSN). To achieve better optimization capability, we propose a multigroup Multistrategy Compact Sine Cosine Algorithm (MCSCA) by using the compact strategy and grouping strategy, which makes the initialized randomly generated value no longer an individual in the population and avoids falling into the local optimum. New evolution formulas are proposed for the intergroup communication strategy. Performance studies on the CEC2013 benchmark demonstrate the effectiveness of our new approach regarding convergence speed and accuracy. Finally, we apply MCSCA to solve the dispatch system of public transit vehicles. Experimental results show that MCSCA can achieve better optimization.

ACS Style

Minghui Zhu; Shu-Chuan Chu; Qingyong Yang; Wei Li; Jeng-Shyang Pan. Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles. Journal of Advanced Transportation 2021, 2021, 1 -16.

AMA Style

Minghui Zhu, Shu-Chuan Chu, Qingyong Yang, Wei Li, Jeng-Shyang Pan. Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles. Journal of Advanced Transportation. 2021; 2021 ():1-16.

Chicago/Turabian Style

Minghui Zhu; Shu-Chuan Chu; Qingyong Yang; Wei Li; Jeng-Shyang Pan. 2021. "Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles." Journal of Advanced Transportation 2021, no. : 1-16.

Journal article
Published: 19 March 2021 in Energy
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Inspired by migratory graying, Pan et al. proposed the fish migration optimization (FMO) algorithm. It integrates the models of migration and swim into the optimization process. This paper firstly proposes a binary version of FMO, called BFMO. In order to improve the search ability of BFMO, ABFMO is introduced to solve the problems of stagnation and falling into local traps. The transfer function is responsible for mapping the continuous search space to the binary space. It plays a critical factor in the binary meta-heuristics. This paper brings a new transfer function and compares it with the transfer functions used by BPSO, BGSA and BGWO. Experiments prove that the new transfer function has realized good results in the solving quality. Unit commitment (UC) is a NP-hard binary optimization problem. BFMO and ABFMO are tested with the IEEE benchmark systems consisting of various generating units with 24-h demand horizon. The effectivenesses of BFMO and ABFMO are compared with seven binary evolutionary algorithms. The simulation results and non-parametric tests verify that they achieve great performance.

ACS Style

Jeng-Shyang Pan; Pei Hu; Shu-Chuan Chu. Binary fish migration optimization for solving unit commitment. Energy 2021, 226, 120329 .

AMA Style

Jeng-Shyang Pan, Pei Hu, Shu-Chuan Chu. Binary fish migration optimization for solving unit commitment. Energy. 2021; 226 ():120329.

Chicago/Turabian Style

Jeng-Shyang Pan; Pei Hu; Shu-Chuan Chu. 2021. "Binary fish migration optimization for solving unit commitment." Energy 226, no. : 120329.

Journal article
Published: 10 March 2021 in Knowledge-Based Systems
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The meta-heuristic evolutionary algorithm is widely used because of its excellent global optimization ability. However, its demand for a mass of evaluation times will lead to an increase in time complexity. Especially when the dimensions of actual problems are too high, the time cost for fitness evaluation is usually minutes, hours, or even days. To improve the above shortcomings and the ability to solve high-dimensional expensive problems, a Fuzzy Hierarchical Surrogate Assisted Probabilistic Particle Swarm Optimization is proposed in this paper. This algorithm first uses Fuzzy Surrogate-Assisted (FSA), Local surrogate-assisted (LSA), and Global surrogate-assisted (GSA) models to fit the fitness evaluation function individually. Secondly, a probabilistic particle swarm optimization is implemented to predict the trained model and update the samples. FSA mainly uses a Fuzzy Clustering algorithm that divides the archive DataBase (DB) into multiple sub-archives to model separately to accurately estimate the function landscape of the function in the partial search space. LSA is mainly designed to capture the local details of the fitness function around the current individual neighborhood and enhance the local optimal accuracy estimation. GSA will build an accurate global model in the entire search space. To verify the performance of our proposed algorithm in solving high-dimensional expensive problems, experiments on seven benchmark functions are conducted in 30D, 50D, and 100D. The final test results show that our proposed algorithm is more competitive than other most advanced algorithms.

ACS Style

Shu-Chuan Chu; Zhi-Gang Du; Yan-Jun Peng; Jeng-Shyang Pan. Fuzzy Hierarchical Surrogate Assists Probabilistic Particle Swarm Optimization for expensive high dimensional problem. Knowledge-Based Systems 2021, 220, 106939 .

AMA Style

Shu-Chuan Chu, Zhi-Gang Du, Yan-Jun Peng, Jeng-Shyang Pan. Fuzzy Hierarchical Surrogate Assists Probabilistic Particle Swarm Optimization for expensive high dimensional problem. Knowledge-Based Systems. 2021; 220 ():106939.

Chicago/Turabian Style

Shu-Chuan Chu; Zhi-Gang Du; Yan-Jun Peng; Jeng-Shyang Pan. 2021. "Fuzzy Hierarchical Surrogate Assists Probabilistic Particle Swarm Optimization for expensive high dimensional problem." Knowledge-Based Systems 220, no. : 106939.

Journal article
Published: 04 March 2021 in Applied Sciences
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QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm generalized differential evolution (DE) algorithm to matrix form. QUATRE was originally designed for a continuous search space, but many practical applications are binary optimization problems. Therefore, we designed a novel binary version of QUATRE. The proposed binary algorithm is implemented using two different approaches. In the first approach, the new individuals produced by mutation and crossover operation are binarized. In the second approach, binarization is done after mutation, then cross operation with other individuals is performed. Transfer functions are critical to binarization, so four families of transfer functions are introduced for the proposed algorithm. Then, the analysis is performed and an improved transfer function is proposed. Furthermore, in order to balance exploration and exploitation, a new liner increment scale factor is proposed. Experiments on 23 benchmark functions show that the proposed two approaches are superior to state-of-the-art algorithms. Moreover, we applied it for dimensionality reduction of hyperspectral image (HSI) in order to test the ability of the proposed algorithm to solve practical problems. The experimental results on HSI imply that the proposed methods are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

ACS Style

Shu-Chuan Chu; Zhongjie Zhuang; Junbao Li; Jeng-Shyang Pan. A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm. Applied Sciences 2021, 11, 2251 .

AMA Style

Shu-Chuan Chu, Zhongjie Zhuang, Junbao Li, Jeng-Shyang Pan. A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm. Applied Sciences. 2021; 11 (5):2251.

Chicago/Turabian Style

Shu-Chuan Chu; Zhongjie Zhuang; Junbao Li; Jeng-Shyang Pan. 2021. "A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm." Applied Sciences 11, no. 5: 2251.

Article
Published: 12 February 2021 in Cluster Computing
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Salp swarm algorithm (SSA) is a swarm intelligence algorithm inspired by the swarm behavior of salps in oceans. In this paper, a adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented. Adaptive multi-group mechanism is to evenly divide the initial population into several subgroups, and then exchange information among subgroups after each adaptive iteration. Communication strategy is also an important part of adaptive multi-group mechanism. This paper proposes three new communication strategies and focuses on promoting the performance of SSA. These measures significantly improve the cooperative ability of SSA, accelerate convergence speed, and avoid easily falling into local optimum. And the benchmark functions confirm that AMSSA is better than the original SSA in exploration and exploitation. In addition, AMSSA is combined with prediction of wind power based on back propagation (AMSSA-BP) neural network. The simulation results show that the AMSSA-BP neural network prediction model can achieve a better prediction effect of wind power.

ACS Style

Jeng-Shyang Pan; Jie Shan; Shi-Guang Zheng; Shu-Chuan Chu; Cheng-Kuo Chang. Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm. Cluster Computing 2021, 1 -16.

AMA Style

Jeng-Shyang Pan, Jie Shan, Shi-Guang Zheng, Shu-Chuan Chu, Cheng-Kuo Chang. Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm. Cluster Computing. 2021; ():1-16.

Chicago/Turabian Style

Jeng-Shyang Pan; Jie Shan; Shi-Guang Zheng; Shu-Chuan Chu; Cheng-Kuo Chang. 2021. "Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm." Cluster Computing , no. : 1-16.

Journal article
Published: 14 January 2021 in ISA Transactions
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Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by nature. Due to its superior performance, it has been widely used in real life. However, it also has some shortcomings in some optimization cases, such as low solution accuracy and slow solution speed. Therefore, in this paper, distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to improve these shortcomings. The distributed parallel technique is implanted to divide the initial fireflies into several subgroups, and exchange the information based on communication strategies among subgroups after the fixed iteration. The communication strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. For verifying its performance, this paper compared DPFA with famous optimization algorithms, and experimental results show that DPFA has stronger competitiveness under the test suite of CEC2013. Furthermore, the proposed DPFA is also applied to the PID parameter tuning of variable pitch wind turbine, and conducted experiments show that DPFA outperforms other algorithms. It can smooth the power output and reduce the impact on the power grid when the wind speed fluctuates.

ACS Style

Jie Shana; Jeng- Shyang Panabd; Cheng- Kuo Changa; Shu- Chuan Chubc; Shi- Guang Zhenga. A distributed parallel firefly algorithm with communication strategies and its application for the control of variable pitch wind turbine. ISA Transactions 2021, 1 .

AMA Style

Jie Shana, Jeng- Shyang Panabd, Cheng- Kuo Changa, Shu- Chuan Chubc, Shi- Guang Zhenga. A distributed parallel firefly algorithm with communication strategies and its application for the control of variable pitch wind turbine. ISA Transactions. 2021; ():1.

Chicago/Turabian Style

Jie Shana; Jeng- Shyang Panabd; Cheng- Kuo Changa; Shu- Chuan Chubc; Shi- Guang Zhenga. 2021. "A distributed parallel firefly algorithm with communication strategies and its application for the control of variable pitch wind turbine." ISA Transactions , no. : 1.

Journal article
Published: 05 January 2021 in IEEE Access
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Proportional Integral Derivative (PID) controller is one of the most classical controllers, which has a good performance in industrial applications. The traditional PID parameter tuning relies on experience, however, the intelligent algorithm is used to optimize the controller, which makes it more convenient. Fish Migration Optimization (FMO) is an excellent algorithm that mimics the swim and migration behaviors of fish biology. Especially, the formulas for optimization were obtained from biologists. However, the optimization effect of FMO for PID control is not prominent, since it is easy to skip the optimal solution with integer-order velocity. In order to improve the optimization performance of FMO, Fractional-Order Fish Migration Optimization (FOFMO) is proposed based on fractional calculus (FC) theory. In FOFMO, the velocity and position are updated in fractional-order forms. In addition, the fishes should migration back to a position which is more conducive to survival. Therefore, a new strategy based on the global best solution to generate new positions of offsprings is proposed. The experiments are performed on benchmark functions and PID controller. The results show that FOFMO is superior to the original FMO, and the PID controller tuned by FOFMO is more robust and has better performance than other contrast algorithms.

ACS Style

Baoyong Guo; Zhongjie Zhuang; Jeng-Shyang Pan; Shu-Chuan Chu. Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm. IEEE Access 2021, 9, 8808 -8819.

AMA Style

Baoyong Guo, Zhongjie Zhuang, Jeng-Shyang Pan, Shu-Chuan Chu. Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm. IEEE Access. 2021; 9 (99):8808-8819.

Chicago/Turabian Style

Baoyong Guo; Zhongjie Zhuang; Jeng-Shyang Pan; Shu-Chuan Chu. 2021. "Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm." IEEE Access 9, no. 99: 8808-8819.

Letter
Published: 31 December 2020 in Electronics Letters
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Nearest feature line is an effective classification algorithm. However, if a test sample cannot be linear represented by the training samples, the algorithm may not work very well. Moreover, another problem is that it will have a large computation complexity. Therefore, the authors propose a novel algorithm. To begin with, the test sample is linear represented by all the training samples, and the errors between the test sample and every training sample are calculated. The authors only keep the training samples with small errors. In this way, on the one hand, training samples are not suitable for the test sample will be ignored, on the other hand, running time can be reduced. To further reduce the computing time of the algorithm, nearest neighbour search technique is applied to the algorithm. Experiments on numerical and image database show the algorithm cannot only improve the classification accuracy, but also reduce runtime.

ACS Style

Zhongjie Zhuang; Jeng‐Shyang Pan; Shu‐Chuan Chu; Hao Luo. Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm. Electronics Letters 2020, 57, 20 -22.

AMA Style

Zhongjie Zhuang, Jeng‐Shyang Pan, Shu‐Chuan Chu, Hao Luo. Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm. Electronics Letters. 2020; 57 (1):20-22.

Chicago/Turabian Style

Zhongjie Zhuang; Jeng‐Shyang Pan; Shu‐Chuan Chu; Hao Luo. 2020. "Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm." Electronics Letters 57, no. 1: 20-22.

Journal article
Published: 15 December 2020 in IEEE Access
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Symbiotic Organism Search (SOS) algorithm is highly praised by researchers for its excellent convergence performance, global optimization ability and simplicity in solving various continuous practical problems. However, in the real world, there are many binary problems, which can only take values of 0 and 1, that still need to be solved. Since the original SOS algorithm cannot directly solve the binary problem, the original ASOS Binary SOS (BSOS) algorithm has the disadvantage of premature convergence. In order to improve the limitations of the ASBSOS algorithm, we propose an Improved BSOS (IBSOS) algorithm. As we all know, the transfer function is very important in the binarization of continuous optimization algorithms. Therefore, we used 9 transfer functions in the IBSOS algorithm to binarize the continuous SOS algorithm and analyzed the impact of each transfer function on the performance of the BSOS algorithm. Moreover, we use the same three biological symbiosis strategies as the continuous SOS algorithm in our proposed IBSOS algorithm to binarize the SOS algorithm to improve The diversity of the algorithm execution process and the ability to balance algorithm exploration and development. In order to verify the performance of IBSOS using different transfer functions, we use 13 benchmark functions to show the global optimization capability and convergence speed of the BSOS algorithm. Finally, we apply the algorithm to feature selection in the ten data sets of UCI. The experimental results with low classification error and few features further verify the excellent performance of the IBSOS algorithm.

ACS Style

Zhi-Gang Du; Jeng-Shyang Pan; Shu-Chuan Chu; Yi-Jui Chiu. Improved Binary Symbiotic Organism Search Algorithm With Transfer Functions for Feature Selection. IEEE Access 2020, 8, 225730 -225744.

AMA Style

Zhi-Gang Du, Jeng-Shyang Pan, Shu-Chuan Chu, Yi-Jui Chiu. Improved Binary Symbiotic Organism Search Algorithm With Transfer Functions for Feature Selection. IEEE Access. 2020; 8 (99):225730-225744.

Chicago/Turabian Style

Zhi-Gang Du; Jeng-Shyang Pan; Shu-Chuan Chu; Yi-Jui Chiu. 2020. "Improved Binary Symbiotic Organism Search Algorithm With Transfer Functions for Feature Selection." IEEE Access 8, no. 99: 225730-225744.

Journal article
Published: 15 December 2020 in Information Sciences
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Surrogate-assisted evolutionary algorithms (SAEAs) are potential approaches to solve computationally expensive optimization problems. The critical idea of SAEAs is to combine the powerful searching capabilities of evolutionary algorithms with the predictive capabilities of surrogate models. In this study, an efficient surrogate-assisted hybrid optimization (SAHO) algorithm is proposed via combining two famous algorithms, namely, teaching–learning-based optimization (TLBO) and differential evolution (DE). The TLBO is focused on global exploration and the DE is concentrated on local exploitation. These two algorithms are carried out alternately when no better candidate solution can be found. Meanwhile, a new prescreening criterion based on the best and top collection information is introduced to choose promising candidates for real function evaluations. Besides, two evolution control (i.e., the generation-based and individual-based) strategies and a top-ranked restart strategy are integrated in the SAHO. Moreover, a local RBF surrogate which does not need too many training samples is employed to model the landscapes of the target function. Sixteen benchmark functions and the tension/compression spring design problem are adopted to compare the proposed SAHO with other state-of-the-art approaches. Extensive comparison results demonstrate that the proposed SAHO has superior performance for solving expensive optimization problems.

ACS Style

Jeng-Shyang Pan; Nengxian Liu; Shu-Chuan Chu; Taotao Lai. An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems. Information Sciences 2020, 561, 304 -325.

AMA Style

Jeng-Shyang Pan, Nengxian Liu, Shu-Chuan Chu, Taotao Lai. An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems. Information Sciences. 2020; 561 ():304-325.

Chicago/Turabian Style

Jeng-Shyang Pan; Nengxian Liu; Shu-Chuan Chu; Taotao Lai. 2020. "An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems." Information Sciences 561, no. : 304-325.

Journal article
Published: 24 November 2020 in Engineering Applications of Artificial Intelligence
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With the rapid development of information technology, infringements have become increasingly serious. Digital watermarking is an effective method to protect information. The current watermarking technology still has room for further improvement in imperceptibility and robustness. This paper proposes an improved watermarking technology using meta-heuristic algorithm. Further, Quick Response code (QR code) is used as a carrier to transmit information. The improved Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) is used to hide the watermark into the QR code. Therefore, digital watermarking is realized on the QR code. In the common watermark embedding methods, the digital watermark is related to the embedding strength. How to find a suitable embedding factor and reduce distortion is of great significance to these watermarking algorithms. This paper mainly proposes two novel algorithms based on States of Matter Search (SMS) algorithm to find suitable embedding factors. The first algorithm uses an adaptive parameter to control the movement of particles called the adaptive step States of Matter Search (sSMS). The second algorithm incorporates co-evolutionary matrix to enhance the search capability named Co-evolution States of Matter Search (CSMS). DWT-SVD is updated through two algorithms to acquire optimal embedding strength factors on the QR code watermarking. By adjusting the embedding strength factors, the intensity of the watermark embedded in different frequency domains would be modified. The experimental results have higher PSNR and the QR code can still be decoded by a general decoder. It shows that the proposed approaches are practicable and effective.

ACS Style

Jeng-Shyang Pan; Xiao-Xue Sun; Shu-Chuan Chu; Ajith Abraham; Bin Yan. Digital watermarking with improved SMS applied for QR code. Engineering Applications of Artificial Intelligence 2020, 97, 104049 .

AMA Style

Jeng-Shyang Pan, Xiao-Xue Sun, Shu-Chuan Chu, Ajith Abraham, Bin Yan. Digital watermarking with improved SMS applied for QR code. Engineering Applications of Artificial Intelligence. 2020; 97 ():104049.

Chicago/Turabian Style

Jeng-Shyang Pan; Xiao-Xue Sun; Shu-Chuan Chu; Ajith Abraham; Bin Yan. 2020. "Digital watermarking with improved SMS applied for QR code." Engineering Applications of Artificial Intelligence 97, no. : 104049.

Research article
Published: 06 October 2020 in Wireless Communications and Mobile Computing
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Wireless sensor networks (WSN) have gradually integrated into the concept of the Internet of Things (IoT) and become one of the key technologies. This paper studies the optimization algorithm in the field of artificial intelligence (AI) and effectively solves the problem of node location in WSN. Specifically, we propose a hybrid algorithm WOA-QT based on the whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm. It skillfully combines the strengths of the two algorithms, not only retaining the WOA’s distinctive framework advantages but also having QUATRE’s excellent coevolution ability. In order to further save optimization time, an auxiliary strategy for dynamically shrinking the search space (DSS) is introduced in the algorithm. To ensure the fairness of the evaluation, this paper selects 30 different types of benchmark functions and conducts experiments from multiple angles. The experiment results demonstrate that the optimization quality and efficiency of WOA-QT are very prominent. We use the proposed algorithm to optimize the weighted centroid location (WCL) algorithm based on received signal strength indication (RSSI) and obtain satisfactory positioning accuracy. This reflects the high value of the algorithm in practical applications.

ACS Style

Jeng-Shyang Pan; Fang Fan; Shu-Chuan Chu; Zhigang Du; Huiqi Zhao. A Node Location Method in Wireless Sensor Networks Based on a Hybrid Optimization Algorithm. Wireless Communications and Mobile Computing 2020, 2020, 1 -14.

AMA Style

Jeng-Shyang Pan, Fang Fan, Shu-Chuan Chu, Zhigang Du, Huiqi Zhao. A Node Location Method in Wireless Sensor Networks Based on a Hybrid Optimization Algorithm. Wireless Communications and Mobile Computing. 2020; 2020 ():1-14.

Chicago/Turabian Style

Jeng-Shyang Pan; Fang Fan; Shu-Chuan Chu; Zhigang Du; Huiqi Zhao. 2020. "A Node Location Method in Wireless Sensor Networks Based on a Hybrid Optimization Algorithm." Wireless Communications and Mobile Computing 2020, no. : 1-14.

Journal article
Published: 19 September 2020 in Knowledge-Based Systems
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Many real-world engineering optimization problems usually need a lot of time for function evaluations or have massive decision variables. It is still a big challenge to address these problems effectively. Recently, surrogate-assisted meta-heuristic algorithms have drawn increasing attention, and have shown their potential to deal with such expensive complex optimization problems. In this study, a surrogate-assisted quasi-affine transformation evolutionary (SA-QUATRE) algorithm is proposed to further enhance the optimization efficiency and effectiveness. In SA-QUATRE, the global and the local surrogate models are effectively combined for fitness estimation. The global surrogate model is built based on all data in the database for global exploration. While, the local surrogate model is constructed with a predefined number of top best samples for local exploitation. Meanwhile, both the generation- and individual-based evolution controls as well as a top best restart strategy are incorporated in the global and the local searches. To enhance the exploration and the exploitation capabilities, the global search uses the mean of the population to be evaluated with the expensive real fitness function, while the local search chooses the individual with the best fitness according to the surrogate for real evaluation. The proposed SA-QUATRE is compared with five state-of-the-art optimization approaches over seven commonly used benchmark functions with dimensions varying from 10 to 100. Moreover, the proposed SA-QUATRE is also applied to solve the tension/compression spring design problem. The experimental results show that SA-QUATRE is promising for optimizing computationally expensive problems.

ACS Style

Nengxian Liu; Jeng-Shyang Pan; Chaoli Sun; Shu-Chuan Chu. An efficient surrogate-assisted quasi-affine transformation evolutionary algorithm for expensive optimization problems. Knowledge-Based Systems 2020, 209, 106418 .

AMA Style

Nengxian Liu, Jeng-Shyang Pan, Chaoli Sun, Shu-Chuan Chu. An efficient surrogate-assisted quasi-affine transformation evolutionary algorithm for expensive optimization problems. Knowledge-Based Systems. 2020; 209 ():106418.

Chicago/Turabian Style

Nengxian Liu; Jeng-Shyang Pan; Chaoli Sun; Shu-Chuan Chu. 2020. "An efficient surrogate-assisted quasi-affine transformation evolutionary algorithm for expensive optimization problems." Knowledge-Based Systems 209, no. : 106418.

Research article
Published: 10 June 2020 in International Journal of Distributed Sensor Networks
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In modern times, swarm intelligence has played an increasingly important role in finding an optimal solution within a search range. This study comes up with a novel solution algorithm named QUasi-Affine TRansformation-Pigeon-Inspired Optimization Algorithm, which uses an evolutionary matrix in QUasi-Affine TRansformation Evolutionary Algorithm for the Pigeon-Inspired Optimization Algorithm that was designed using the homing behavior of pigeon. We abstract the pigeons into particles of no quality and improve the learning strategy of the particles. Having different update strategies, the particles get more scientific movement and space exploration on account of adopting the matrix of the QUasi-Affine TRansformation Evolutionary algorithm. It increases the versatility of the Pigeon-Inspired Optimization algorithm and makes the Pigeon-Inspired Optimization less simple. This new algorithm effectively improves the shortcoming that is liable to fall into local optimum. Under a number of benchmark functions, our algorithm exhibits good optimization performance. In wireless sensor networks, there are still some problems that need to be optimized, for example, the error of node positioning can be further reduced. Hence, we attempt to apply the proposed optimization algorithm in terms of positioning, that is, integrating the QUasi-Affine TRansformation-Pigeon-Inspired Optimization algorithm into the Distance Vector–Hop algorithm. Simultaneously, the algorithm verifies its optimization ability by node location. According to the experimental results, they demonstrate that it is more outstanding than the Pigeon-Inspired Optimization algorithm, the QUasi-Affine TRansformation Evolutionary algorithm, and particle swarm optimization algorithm. Furthermore, this algorithm shows up minor errors and embodies a much more accurate location.

ACS Style

Xiao-Xue Sun; Jeng-Shyang Pan; Shu-Chuan Chu; Pei Hu; Ai-Qing Tian. A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks. International Journal of Distributed Sensor Networks 2020, 16, 1 .

AMA Style

Xiao-Xue Sun, Jeng-Shyang Pan, Shu-Chuan Chu, Pei Hu, Ai-Qing Tian. A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks. International Journal of Distributed Sensor Networks. 2020; 16 (6):1.

Chicago/Turabian Style

Xiao-Xue Sun; Jeng-Shyang Pan; Shu-Chuan Chu; Pei Hu; Ai-Qing Tian. 2020. "A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks." International Journal of Distributed Sensor Networks 16, no. 6: 1.

Journal article
Published: 01 June 2020 in Applied Soft Computing
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The three-dimensional (3D) path planning of unmanned robots focuses on avoiding collisions with obstacles and finding an optimized path to the target location in a complex three-dimensional environment. An improved cuckoo search algorithm based on compact and parallel techniques for three-dimensional path planning problems is proposed. This paper implements the compact cuckoo search algorithm, and then, a new parallel communication strategy is proposed. The compact scheme can effectively save the memory of the unmanned robot. The parallel scheme can increase the accuracy and achieve faster convergence. The proposed algorithm is tested on several selected functions and three-dimensional path planning. Results compared with other methods show that the proposed algorithm can provide more competitive results and achieve more efficient execution.

ACS Style

Pei-Cheng Song; Jeng-Shyang Pan; Shu-Chuan Chu. A parallel compact cuckoo search algorithm for three-dimensional path planning. Applied Soft Computing 2020, 94, 106443 .

AMA Style

Pei-Cheng Song, Jeng-Shyang Pan, Shu-Chuan Chu. A parallel compact cuckoo search algorithm for three-dimensional path planning. Applied Soft Computing. 2020; 94 ():106443.

Chicago/Turabian Style

Pei-Cheng Song; Jeng-Shyang Pan; Shu-Chuan Chu. 2020. "A parallel compact cuckoo search algorithm for three-dimensional path planning." Applied Soft Computing 94, no. : 106443.

Journal article
Published: 23 April 2020 in Sensors
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In this paper, a new intelligent computing algorithm named Enhanced Black Hole (EBH) is proposed to which the mutation operation and weight factor are applied. In EBH, several elites are taken as role models instead of only one in the original Black Hole (BH) algorithm. The performance of the EBH algorithm is verified by the CEC 2013 test suit, and shows better results than the original BH. In addition, the EBH and other celebrated algorithms can be used to solve node coverage problems of Wireless Sensor Network (WSN) in 3-D terrain with satisfactory performance.

ACS Style

Jeng-Shyang Pan; Qing-Wei Chai; Shu-Chuan Chu; Ning Wu. 3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm. Sensors 2020, 20, 2411 .

AMA Style

Jeng-Shyang Pan, Qing-Wei Chai, Shu-Chuan Chu, Ning Wu. 3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm. Sensors. 2020; 20 (8):2411.

Chicago/Turabian Style

Jeng-Shyang Pan; Qing-Wei Chai; Shu-Chuan Chu; Ning Wu. 2020. "3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm." Sensors 20, no. 8: 2411.