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Firefly algorithm with division of roles for complex optimal scheduling Research Articles

Jia Zhao, Wenping Chen, Renbin Xiao, Jun Ye,zhaojia925@163.com,chen_9731@163.com,rbxiao@hust.edu.cn,yejun68@sina.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 10,   Pages 1311-1333 doi: 10.1631/FITEE.2000691

Abstract: A single strategy used in the cannot effectively solve the complex problem. Thus, we propose the FA with (DRFA). Herein, fireflies are divided into leaders, developers, and followers, while a learning strategy is assigned to each role: the leader chooses the greedy ; the developer chooses two leaders randomly and uses the strategy for local development; the follower randomly selects two excellent particles for global exploration. To improve the efficiency of the fixed step size used in FA, a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages. Role division can balance the development and exploration ability of the algorithm. The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems. The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of of cascade reservoirs.

Keywords： 萤火虫算法；角色分工；柯西突变；精英邻域搜索；优化调度    

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Optimized deployment of a radar network based on an improved firefly algorithm Regular Papers

Xue-jun ZHANG, Wei JIA, Xiang-min GUAN, Guo-qiang XU, Jun CHEN, Yan-bo ZHU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3,   Pages 425-437 doi: 10.1631/FITEE.1800749

Abstract:

The threats and challenges of unmanned aerial vehicle (UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm (FA) and four recently proposed FA variants.

Keywords： Improved firefly algorithm     Radar surveillance network     Deployment optimization     Unmanned aerial vehicle (UAV) invasion defense    

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The Improvement of Genetic Algorithm and Its Application in the Optimal Operation of Reservoirs

Zhong Denghua,Xiong Kaizhi,Cheng Liqin

Strategic Study of CAE 2003, Volume 5, Issue 9,   Pages 22-26

Abstract:

Genetic algorithms search for the optimal solution by continually improving the individual of the population. Because of the difficulty in convergence and solving of individual fitness, standard genetic algorithm (SGA) is not used widely. Based on the improvement of SGA, especially the improvement of the selection operator in SGA, a new genetic algorithm(AGA) is proposed to solve the problems about the optimal operation of reservoirs. A new coding method is presented which is based on the subscript sequence of reservoir capacity array other than the water level sequence. An engineering example illustrates that AGA is much more efficient than SGA, and also the new coding method predigests the course of genetic algorithm in the optimal operation of reservoirs.

Keywords： genetic algorithm     improvement     reservoir     optimal operation    

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A Pareto Strength SCE-UA Algorithm for ReservoirOptimization Operation

Lin Jianyi,Cheng Chuntian,Gu Yanping,Wu Xinyu

Strategic Study of CAE 2007, Volume 9, Issue 10,   Pages 80-82

Abstract:

In this paper,  the Pareto strength SCE-UA algorithm (PSSCE) is presented to handle the reservoir optimization operation problem.  The approach treats the constrained optimization as a two-objective optimization: one objective is the original objective function; the other is the degree of constraint violation.  SCE-UA algorithm is applied to the two-objective optimization by using the individual's comparing procedure and the population ranking procedure which are respectively based on the Pareto dominance relationship and the Pareto strength definition.  The new approach is more general,  effective and robust.

Keywords： reservoir optimal operation     constrained optimization     Pareto dominate     Pareto strength     SCE-UA algorithm    

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A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction Research Article

Shaoqiang YE, Kaiqing ZHOU, Azlan Mohd ZAIN, Fangling WANG, Yusliza YUSOFF

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1574-1590 doi: 10.1631/FITEE.2200334

Abstract: Harmony search (HS) is a form of stochastic meta-heuristic inspired by the improvisation process of musicians. In this study, a modified HS with a hybrid cuckoo search (CS) operator, HS-CS, is proposed to enhance global search ability while avoiding falling into local optima. First, the randomness of the HS pitch disturbance adjusting method is analyzed to generate an adaptive inertia weight according to the quality of solutions in the harmony memory and to reconstruct the fine-tuning bandwidth optimization. This is to improve the efficiency and accuracy of HS algorithm optimization. Second, the CS operator is introduced to expand the scope of the solution space and improve the density of the population, which can quickly jump out of the local optimum in the randomly generated harmony and update stage. Finally, a dynamic parameter adjustment mechanism is set to improve the efficiency of optimization. Three theorems are proved to reveal HS-CS as a meta-heuristic algorithm. In addition, 12 benchmark functions are selected for the optimization solution to verify the performance of HS-CS. The analysis shows that HS-CS is significantly better than other algorithms in optimizing high-dimensional problems with strong robustness, high convergence speed, and high convergence accuracy. For further verification, HS-CS is used to optimize the back propagation neural network (BPNN) to extract weighted fuzzy production rules. Simulation results show that the BPNN optimized by HS-CS can obtain higher classification accuracy of weighted fuzzy production rules. Therefore, the proposed HS-CS is proved to be effective.

Keywords： Harmony search algorithm     Cuckoo search algorithm     Global convergence     Function optimization     Weighted fuzzy production rule extraction    

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MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization Research Article

Kai MENG, Chen CHEN, Bin XIN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1828-1847 doi: 10.1631/FITEE.2200237

Abstract: The (SSA) is a recent meta-heuristic optimization approach with the advantages of simplicity and flexibility. However, SSA still faces challenges of premature convergence and imbalance between exploration and exploitation, especially when tackling multimodal . Aiming to deal with the above problems, we propose an enhanced variant of SSA called the multi-strategy enhanced (MSSSA) in this paper. First, a chaotic map is introduced to obtain a high-quality initial population for SSA, and the opposition-based learning strategy is employed to increase the population diversity. Then, an is designed to accommodate an adequate balance between exploration and exploitation. Finally, a is embedded in the individual update stage to avoid falling into local optima. To validate the effectiveness of the proposed MSSSA, a large number of experiments are implemented, including 40 complex functions from the IEEE CEC2014 and IEEE CEC2019 test suites and 10 classical functions with different dimensions. Experimental results show that the MSSSA achieves competitive performance compared with several state-of-the-art optimization algorithms. The proposed MSSSA is also successfully applied to solve two engineering . The results demonstrate the superiority of the MSSSA in addressing practical problems.

Keywords： Swarm intelligence     Sparrow search algorithm     Adaptive parameter control strategy     Hybrid disturbance mechanism     Optimization problems    

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Recent progress on the study of distributed economic dispatch in smart grid: an overview Review Articles

Guanghui Wen, Xinghuo Yu, Zhiwei Liu,wenguanghui@gmail.com,x.yu@rmit.edu.au,zwliu@hust.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 1,   Pages 1-140 doi: 10.1631/FITEE.2000205

Abstract: Designing an efficient (DED) strategy for the (SG) in the presence of multiple generators plays a paramount role in obtaining various benefits of a new generation power system, such as easy implementation, low maintenance cost, high energy efficiency, and strong robustness against uncertainties. It has drawn a lot of interest from a wide variety of scientific disciplines, including power engineering, control theory, and applied mathematics. We present a state-of-the-art review of some theoretical advances toward DED in the SG, with a focus on the literature published since 2015. We systematically review the recent results on this topic and subsequently categorize them into distributed discrete- and continuous-time economic dispatches of the SG in the presence of multiple generators. After reviewing the literature, we briefly present some future research directions in DED for the SG, including the distributed security economic dispatch of the SG, distributed fast economic dispatch in the SG with practical constraints, efficient initialization-free DED in the SG, DED in the SG in the presence of smart energy storage batteries and flexible loads, and DED in the SG with artificial intelligence technologies.

Keywords： Distributed economic dispatch     Distributed optimization     Smart grid     Continuous-time optimization algorithm     Discrete-time optimization algorithm    

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Application prospect of PSO in hydrology

Dong Qianjin,Cao Guangjing,Wang Xianjia,Dai Huichao,Zhao Yunfa

Strategic Study of CAE 2010, Volume 12, Issue 1,   Pages 81-85

Abstract:

The basic algorithm and its flow are introduced at first, then its application to scheduling operation of reservoir, economic operation of hydropower and parameter calibration in hydrology field is discussed, the suggestion for future study is pointed out that should strengthen the study of adaptive mechanism and convergence performance in PSO, compare and combine with other technology, broaden the region of application to hydrology which may supply a new method for solving much optimal problem in hydrology field.

Keywords： hydrology science     particle swarm optimization     scheduling operation     economical operation    

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An improved fruit fly optimization algorithm for solving traveling salesman problem Article

Lan HUANG, Gui-chao WANG, Tian BAI, Zhe WANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1525-1533 doi: 10.1631/FITEE.1601364

Abstract: The traveling salesman problem (TSP), a typical non-deterministic polynomial (NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimization algorithm (FOA) is used to solve TSP, since it has the advantages of being easy to understand and having a simple implementation. However, it has problems, including a slow convergence rate for the algorithm, easily falling into the local optimum, and an insufficient optimi-zation precision. To address TSP effectively, three improvements are proposed in this paper to improve FOA. First, the vision search process is reinforced in the foraging behavior of fruit flies to improve the convergence rate of FOA. Second, an elimination mechanism is added to FOA to increase the diversity. Third, a reverse operator and a multiplication operator are proposed. They are performed on the solution sequence in the fruit fly’s smell search and vision search processes, respectively. In the experiment, 10 benchmarks selected from TSPLIB are tested. The results show that the improved FOA outperforms other alternatives in terms of the convergence rate and precision.

Keywords： Traveling salesman problem     Fruit fly optimization algorithm     Elimination mechanism     Vision search     Operator    

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Multimethod Collaborative Optimization Algorithm Based on Embedding Collaboration

Luo Wencai,Luo Shishan,Wang Zhenguo

Strategic Study of CAE 2004, Volume 6, Issue 4,   Pages 51-55

Abstract:

Multi-method collaborative optimization algorithm based on embedding collaboration is advanced. It uses embedding structure to collaborate different kinds of optimization methods, and makes use of the collaboration effect among them to improve the optimization performance. A multimethod collaborative optimization algorithm base on embedding collaboration is designed, which is composed of genetic algorithm, pattern search method and Powell's method. Results show that multi-method collaborative optimization algorithm based on embedding collaboration obtains better global optimization performance than single optimization method.

Keywords： multimethod collaborative optimization algorithm     embedding collaboration     genetic algorithm     pattern search method     Powell's method    

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A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search Research Article

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1557-1573 doi: 10.1631/FITEE.2200515

Abstract: s (CNNs) have been developed quickly in many real-world fields. However, CNN’s performance depends heavily on its hyperparameters, while finding suitable hyperparameters for CNNs working in application fields is challenging for three reasons: (1) the problem of encoding for different types of hyperparameters in CNNs, (2) expensive computational costs in evaluating candidate hyperparameter configuration, and (3) the problem of ensuring convergence rates and model performance during hyperparameter search. To overcome these problems and challenges, a hybrid-model optimization algorithm is proposed in this paper to search suitable hyperparameter configurations automatically based on the and (GPPSO) algorithm. First, a new encoding method is designed to efficiently deal with the CNN hyperparameter problem. Second, a hybrid-surrogate-assisted model is proposed to reduce the high cost of evaluating candidate hyperparameter configurations. Third, a novel activation function is suggested to improve the model performance and ensure the convergence rate. Intensive experiments are performed on imageclassification benchmark datasets to demonstrate the superior performance of GPPSO over state-of-the-art methods. Moreover, a case study on metal fracture diagnosis is carried out to evaluate the GPPSO algorithm performance in practical applications. Experimental results demonstrate the effectiveness and efficiency of GPPSO, achieving accuracy of 95.26% and 76.36% only through 0.04 and 1.70 GPU days on the CIFAR-10 and CIFAR-100 datasets, respectively.

Keywords： Convolutional neural network     Gaussian process     Hybrid model     Hyperparameter optimization     Mixed-variable     Particle swarm optimization    

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Tabu search based resource allocation in radiological examination process execution None

Chun-hua HE

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 3,   Pages 446-458 doi: 10.1631/FITEE.1601802

Abstract: Efficient resource scheduling and allocation in radiological examination process (REP) execution is a key requirement to improve patient throughput and radiological resource utilization and to manage unexpected events that occur when resource scheduling and allocation decisions change due to clinical needs. In this paper, a Tabu search based approach is presented to solve the resource scheduling and allocation problems in REP execution. The primary objective of the approach is to minimize a weighted sum of average examination flow time, average idle time of the resources, and delays. Unexpected events, i.e., emergent or absent examinations, are also considered. For certain parameter combinations, the optimal solution of radiological resource scheduling and allocation is found, while considering the limitations such as routing and resource constraints. Simulations in the application case are performed. Results show that the proposed approach makes efficient use of radiological resource capacity and improves the patient throughput in REP execution.

Keywords： Radiological examination process (REP)     Resource scheduling and allocation     Tabu search    

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Research on economic operation of microgrid with high temperature energy storage system

Luo Yi and Zhang Lijuan

Strategic Study of CAE 2015, Volume 17, Issue 1,   Pages 74-80

Abstract:

With the advantages of high efficiency， environmental protection and energy conservation， the high energy storage system has extensive application prospect， economic operation is becoming a wide concerned issue on microgrid with high temperature energy storage system. By analyzing the microgrid with high temperature energy storage system， based on the characteristics of each micro-source， the model of high temperature energy storage system and economic operation model of the grid-connected microgrid are constructed with time-sharing electricity. The improved immune particle swarm algorithm is used to solve the proposed model， and then field application verifies the effectiveness of model. Results show that the proposed method and model can reach the globally optimal solution of dynamic microgrid， and it is of cost saving and significant economic benefit for high temperature energy storage system to participate in thermal load supplying.

Keywords： high temperature energy storage system； microgrid； economic operation； optimized dispatching； improved immune particle swarm algorithm    

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Improved dynamic grey wolf optimizer Research Articles

Xiaoqing Zhang, Yuye Zhang, Zhengfeng Ming,249140543@qq.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 887-890 doi: 10.1631/FITEE.2000191

Abstract: In the standard (GWO), the search wolf must wait to update its current position until the comparison between the other search wolves and the three leader wolves is completed. During this waiting period, the standard GWO is seen as the static GWO. To get rid of this waiting period, two dynamic GWO algorithms are proposed: the first dynamic (DGWO1) and the second dynamic (DGWO2). In the dynamic GWO algorithms, the current search wolf does not need to wait for the comparisons between all other search wolves and the leading wolves, and its position can be updated after completing the comparison between itself or the previous search wolf and the leading wolves. The position of the search wolf is promptly updated in the dynamic GWO algorithms, which increases the iterative convergence rate. Based on the structure of the dynamic GWOs, the performance of the other improved GWOs is examined, verifying that for the same improved algorithm, the one based on dynamic GWO has better performance than that based on static GWO in most instances.

Keywords： 群智能；灰狼优化算法；动态灰狼优化算法；优化实验    

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Quantum coding genetic algorithm based on frog leaping

Xu Bo,Peng Zhiping,Yu Jianping and Ke Wende

Strategic Study of CAE 2014, Volume 16, Issue 3,   Pages 108-112

Abstract:

The determinations of the rotation phase of quantum gates and mutation probability are the two main issues that restrict the efficiency of quantum genetic algorithm. This paper presents a quantum real coding genetic algorithm（QRGA）. QRGA used an adaptive means to adjust the direction and the size of the rotation angle of quantum rotation gate. In order to ensure the direction of evolution and population diversity，the mutation probability is guided based on the step of frog leaping algorithm which quantified by fuzzy logic. Comparative experimental results show that the algorithm can avoid falling into part optimal solution and astringe to the global optimum solution quickly，which has achieved good results in the running time and performance of the solution.

Keywords： quantum encoding     quantum genetic algorithm     frog leaping algorithm     swarm intelligence    

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Title Author Date Type Operation

Firefly algorithm with division of roles for complex optimal scheduling

Jia Zhao, Wenping Chen, Renbin Xiao, Jun Ye,zhaojia925@163.com,chen_9731@163.com,rbxiao@hust.edu.cn,yejun68@sina.com

Journal Article

Optimized deployment of a radar network based on an improved firefly algorithm

Xue-jun ZHANG, Wei JIA, Xiang-min GUAN, Guo-qiang XU, Jun CHEN, Yan-bo ZHU

Journal Article

The Improvement of Genetic Algorithm and Its Application in the Optimal Operation of Reservoirs

Zhong Denghua,Xiong Kaizhi,Cheng Liqin

Journal Article

A Pareto Strength SCE-UA Algorithm for ReservoirOptimization Operation

Lin Jianyi,Cheng Chuntian,Gu Yanping,Wu Xinyu

Journal Article

A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction

Shaoqiang YE, Kaiqing ZHOU, Azlan Mohd ZAIN, Fangling WANG, Yusliza YUSOFF

Journal Article

MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization

Kai MENG, Chen CHEN, Bin XIN

Journal Article

Recent progress on the study of distributed economic dispatch in smart grid: an overview

Guanghui Wen, Xinghuo Yu, Zhiwei Liu,wenguanghui@gmail.com,x.yu@rmit.edu.au,zwliu@hust.edu.cn

Journal Article

Application prospect of PSO in hydrology

Dong Qianjin,Cao Guangjing,Wang Xianjia,Dai Huichao,Zhao Yunfa

Journal Article

An improved fruit fly optimization algorithm for solving traveling salesman problem

Lan HUANG, Gui-chao WANG, Tian BAI, Zhe WANG

Journal Article

Multimethod Collaborative Optimization Algorithm Based on Embedding Collaboration

Luo Wencai,Luo Shishan,Wang Zhenguo

Journal Article

A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Journal Article

Tabu search based resource allocation in radiological examination process execution

Chun-hua HE

Journal Article

Research on economic operation of microgrid with high temperature energy storage system

Luo Yi and Zhang Lijuan

Journal Article

Improved dynamic grey wolf optimizer

Xiaoqing Zhang, Yuye Zhang, Zhengfeng Ming,249140543@qq.com

Journal Article

Quantum coding genetic algorithm based on frog leaping

Xu Bo,Peng Zhiping,Yu Jianping and Ke Wende

Journal Article