Showing papers by "Shu-Chuan Chu published in 2021"
TL;DR: 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).
73 citations
TL;DR: Two novel algorithms based on States of Matter Search (SMS) algorithm to find suitable embedding factors and reduce distortion are proposed for improved watermarking technology using meta-heuristic algorithm.
58 citations
TL;DR: In this paper, the authors proposed a new transfer function and compared it with the transfer functions used by BPSO, BGSA and BGWO, which achieved good results in solving quality.
52 citations
TL;DR: 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 PPE, which has better performance than similar algorithms.
Abstract: 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.
33 citations
TL;DR: This study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy that can prevent the algorithm from falling into a local optimal solution of robot path planning.
Abstract: Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.
24 citations
TL;DR: A Fuzzy Hierarchical Surrogate Assisted, Local surrogate- assisted, and Global surrogate-assisted models are used to fit the fitness evaluation function individually to solve high-dimensional expensive problems.
Abstract: 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 ( D B ) 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.
23 citations
TL;DR: A new heuristic algorithm named Parallel Compact Cat Swarm Optimization (PCCSO) with three separate communication strategies and the concept of the compact is presented, which is not only reflected in enhancing the ability of local search, but also in saving the computer memory.
Abstract: Cat swarm optimization (CSO) has been applied to a variety of fields because of the better capacity of searching for optimum and higher robustness. However, the poor convergency and larger memory consumption are still core defects, which restricts the efficiency of optimization to a larger extent. A new heuristic algorithm named Parallel Compact Cat Swarm Optimization (PCCSO) with three separate communication strategies and the concept of the compact are presented in this article. The advantage of PCCSO is not only reflected in enhancing the ability of local search, but also in saving the computer memory. The experimental results on CEC2013 benchmark functions demonstrate that the PCCSO is always superior to PSO, CSO, and improved CSO in getting convergent. Then, the PCCSO is applied to DV-Hop to effectively improve the localization accuracy of unknown nodes while also saving WSN memory. The experimental results based on PCCSO from the different number of sensor nodes also illustrate that the PCCSO-DV-Hop shows a lower localization error compared to other optimization algorithms based on DV-Hop.
21 citations
TL;DR: In this paper, a fractional-order fish migration optimization (FOFMO) is proposed to improve the optimization performance of FMO, which is based on fractional calculus (FC) theory.
Abstract: 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.
20 citations
TL;DR: In this article, Wu et al. proposed an enhanced protocol in which the security is verified by the formal analysis and informal analysis, Burross-Abadii-Needham (BAN) logic, and ProVerif tools.
Abstract: 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.
19 citations
TL;DR: A adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented and it is shown that the AMSSA-BP neural network prediction model can achieve a better prediction effect of wind power.
Abstract: 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.
14 citations
TL;DR: In this paper, a multigroup multistrategy Compact Sine Cosine Algorithm (MCSCA) is proposed to solve the dispatch system of public transit vehicles, which makes the initialized randomly generated value no longer an individual in the population and avoids falling into the local optimum.
Abstract: 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.
TL;DR: Four new transfer function, an improved speed update scheme, and a second-stage position update method are proposed for the binary pigeon-inspired optimization algorithm to improve the solution quality of the BPIO algorithm.
Abstract: 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.
TL;DR: Wang et al. as discussed by the authors proposed a lightweight Intelligent Intrusion Detection Model for WSN, which combines k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) to improve the classification accuracy and greatly reduce the false alarm rate.
Abstract: 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.
TL;DR: A parallel and compact version of the Sine Cosine Algorithm (PCSCA) is proposed in this article, which can effectively improve search ability and increase the diversity of solutions.
Abstract: A Parallel and Compact version of the Sine Cosine Algorithm (PCSCA) is proposed in this article. Parallel method can effectively improve search ability and increase the diversity of solutions. We develop three communication strategies based on parallelism idea to serve different types of optimization function to achieve the best performance. Furthermore, compact method uses statistical distribution to represent the solutions, which can save memory space and energy of the digital device. To check the optimization effect of the proposed PCSCA algorithm, it is tested on the CEC2013 benchmark function set and compared to SCA, parallel compact Cuckoo Search (PCCS) algorithms. The empirical study demonstrates that PCSCA has improved by 50.1% and 5.6%, compared to SCA and PCCS, respectively. Finally, we apply PCSCA to optimize the position accuracy of sensor node deployed in 3D actual terrain. Experimental results show that PCSCA can achieve lower localization error via Time Difference of Arrival method.
TL;DR: A novel algorithm based on the modes communication for the parallel cat swarm optimization is proposed so as to improve the location accuracy of DV-hop.
Abstract: Two factors, accuracy and cost, have always plagued the node positing in wireless sensor networks (WSN). If positioning is required to be accurate enough, the cost of equipment required for the location must increase significantly. Conversely, the lower cost will bring some problems like the big bias of positioning. DV-hop is a widely used positioning algorithm due to its low dependence on the device and the low operating cost. Many modified DV-hop algorithms improve the estimation accuracy of the average jump distance and the distance between the unknown and known nodes by adding weights, applying least squares, and using heuristic algorithms. In this paper, a novel algorithm based on the modes communication for the parallel cat swarm optimization is proposed so as to improve the location accuracy of DV-hop.
TL;DR: Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution.
Abstract: Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. ...
TL;DR: Comparisons of MFPA using three strategies with FPA and PSO show that MFPA based on novel communication strategies has a good global optimization ability, improving the convergence speed and accuracy of the FPA.
Abstract: Multi-group Flower Pollination Algorithm (MFPA) based on novel communication strategies was proposed with an eye to the disadvantages of the Flower Pollination Algorithm (FPA), such as tardy convergence rate, inferior search accuracy, and strong local optimum. By introducing a parallel operation to divide the population into some groups, the global search capability of the algorithm was improved. Then three new communication strategies were proposed. Strategy 1 combined high-quality pollens of each group for evolution and replaced the old pollens. Strategy 2 let each group’s inferior pollens approaching to the optimal pollen. Strategy 3 was a combination of strategies 1 and 2. Then, experiments on 25 classical test functions show that MFPA based on novel communication strategies has a good global optimization ability, improving the convergence speed and accuracy of the FPA. Thus, we compare MFPA using three strategies with FPA and PSO, its result shows that MFPA is better than FPA and PSO. Finally, we also applied it to two practical problems and achieved a better convergence effect than FPA.
TL;DR: In this paper, an automatic pennation angle measuring approach based on deep learning is proposed, where the Local Radon Transform (LRT) is used to detect the superficial and deep aponeuroses on the pennation angles.
TL;DR: The proposed binary version of QUATRE is designed for dimensionality reduction of hyperspectral image (HSI) and the experimental results imply that the proposed methods are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
Abstract: 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).
TL;DR: In this paper, the authors proposed a compact adaptive particle swarm algorithm (cAPSO), which replaces the specific position of each particle by the distribution probability of the particle swarm, which greatly reduces the memory usage.
Abstract: The mobile sensor network can sense and collect the data information of the monitored object in real time in the monitoring area. However, the collected information is meaningful only if the location of the node is known. This paper mainly optimizes the Monte Carlo Localization (MCL) in mobile sensor positioning technology. In recent years, the rapid development of heuristic algorithms has provided solutions to many complex problems. This paper combines the compact strategy into the adaptive particle swarm algorithm and proposes a compact adaptive particle swarm algorithm (cAPSO). The compact strategy replaces the specific position of each particle by the distribution probability of the particle swarm, which greatly reduces the memory usage. The performance of cAPSO is tested on 28 test functions of CEC2013, and compared with some existing heuristic algorithms, it proves that cAPSO has a better performance. At the same time, cAPSO is applied to MCL technology to improve the accuracy of node localization, and compared with other heuristic algorithms in the accuracy of MCL, the results show that cAPSO has a better performance.
TL;DR: In this paper, a binary slime mold algorithm and the strategy of adding unselected factors proposed in this paper have a good performance in spectrum allocation and the resulting spectrum allocation scheme can achieve efficient use of network resources.
Abstract: Spectrum has now become a scarce resource due to the continuous development of wireless communication technology. Cognitive radio technology is considered to be a new method to solve the shortage of spectrum resources. The spectrum allocation model of cognitive radio can effectively avoid the waste of spectrum resources. A novel binary version of slime mould algorithm is proposed for the spectrum allocation model to solve the spectrum allocation scheme. In addition, adding unselected factors strategy can make the approach find a better solution. Compared with other algorithms, the novel binary slime mould algorithm and the strategy of adding unselected factors proposed in this paper have a good performance in spectrum allocation. The resulting spectrum allocation scheme can achieve efficient use of network resources.
TL;DR: In this article, a two-phase quasi-affine transformation evolution with feedback (tfQUATRE) algorithm is proposed to improve the exploration and exploitation abilities by adjusting the search tendency at different phases.
TL;DR: In this article, a distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to improve the shortcomings of the traditional FA algorithm, such as low solution accuracy and slow solution speed.
Abstract: 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.
TL;DR: In the simulation of wireless sensor network (WSN) dynamic deployment optimization, it is found that using this method can get the ideal sensor node distribution, which makes PSCA’s performance in solving other practical problems worth looking forward to.
Abstract: All along, people have a high enthusiasm for the research of optimization algorithm. A large number of new algorithms and methods have emerged. The sine cosine algorithm (SCA) is an excellent algorithm that has appeared in recent years. It is a stochastic optimization algorithm based on population. Compared with the existing algorithms, SCA is a suitable solution to different optimization problems, especially the optimization of unimodal functions. It is qualified to optimize real-world problems with unknown and limited search space. But sometimes it does not perform satisfactorily when dealing with some specific problems, such as optimization of multimodal functions or composite functions. This paper presents a parallel version of the sine cosine algorithm (PSCA) with three communication strategies. Different strategies can be selected according to the type of optimization function to achieve better results. We have repeatedly tested different types of functions, and the results show that the proposed PSCA can solve the optimization problem more specifically. In the simulation of wireless sensor network (WSN) dynamic deployment optimization, it is found that using this method can get the ideal sensor node distribution, which makes PSCA’s performance in solving other practical problems worth looking forward to.
TL;DR: This paper proposes an equilibrium optimiser of interswarm interactive learning strategy to plan the shortest flight path of drones.
Abstract: This paper proposes an equilibrium optimiser of interswarm interactive learning strategy to plan the shortest flight path of drones. This paper divide the population into a learning swarm and a lea...
TL;DR: In this article, a compact Sine Cosine Algorithm (cSCA) is proposed to solve the vehicle routing problem with time window in transportation and the quality of the solution is further improved by introducing the relocate operator.
Abstract: In this paper, the compact Sine Cosine Algorithm (cSCA) is proposed. The cSCA algorithm is not based on population, but simulates the behavior of the actual population through a probability model called virtual population. Compared with the original algorithm, the cSCA algorithm takes up less memory space. However, frequent sampling may lead to poor solution quality. In view of this situation, this paper introduces the intergenerational generation sampling mechanism to improve the cSCA algorithm. Through the CEC2013 function set test, compared with the original SCA algorithm and other compact algorithms, the algorithm proposed in this paper can show strong solving ability. Finally, this paper describes how to apply the proposed algorithm and the SCA algorithm to solve the vehicle routing problem with time window in transportation. The quality of the solution is further improved by introducing the relocate operator. Through Solomon standard test data, the calculation performance of the algorithms is verified.
TL;DR: In this article, the authors proposed a novel algorithm for path searching based on an improved black hole (BH) method to enhance the real-time navigation efficiency, where parallel evolution, and information exchange strategy inspired by the quasi-affine transformation evolution (QUATRE) algorithm, allow agents with effective information to search the solution space quickly and effectively that can prompt the convergence speed and expand the diversity of solutions.
Abstract: Traffic navigation is an important part of intelligent transportation systems (ITSs). In this paper, we propose a novel algorithm for path searching based on an improved black hole (BH) method to enhance the real-time navigation efficiency. The original BH algorithm is optimized firstly. Parallel evolution, and information exchange strategy inspired by the quasi-affine transformation evolution (QUATRE) algorithm, allow agents with effective information to search the solution space quickly and effectively that can prompt the convergence speed and expand the diversity of solutions. At the same time, strengthen the exploitation around the global best solution also can enable agents to find the target quickly. The performance of our algorithm can be confirmed by CEC 2017 benchmark functions. For practical purposes, the optimal path should not only consider the shortest distance, but also the minimum fuel consumption. Besides, the effectiveness and timeliness are of great significance for real-time path navigation. In the simulation system, our proposed algorithm can reduce the error rate of navigation to find a realistic path. The results indicate that the proposed algorithm for navigation is effective and stable.