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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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Correction of array failure using grey wolf optimizer hybridized with an interior point algorithm None

Shafqat Ullah KHAN, M. K. A. RAHIM, Liaqat ALI

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 9,   Pages 1191-1202 doi: 10.1631/FITEE.1601694

Abstract:

We design a grey wolf optimizer hybridized with an interior point algorithm to correct a faulty antenna array. If a single sensor fails, the radiation power pattern of the entire array is disturbed in terms of sidelobe level (SLL) and null depth level (NDL), and nulls are damaged and shifted from their original locations. All these issues can be solved by designing a new fitness function to reduce the error between the preferred and expected radiation power patterns and the null limitations. The hybrid algorithm has been designed to control the array’s faulty radiation power pattern. Antenna arrays composed of 21 sensors are used in an example simulation scenario. The MATLAB simulation results confirm the good performance of the proposed method, compared with the existing methods in terms of SLL and NDL.

Keywords： Failure correction     Grey wolf optimizer     Interior point algorithm     Sidelobes     Deeper null depth level    

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A novel grey wolf optimizer and its applications in 5G frequency selection surface design Research Article

Zhihao HE, Gang JIN, Yingjun WANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9,   Pages 1338-1353 doi: 10.1631/FITEE.2100580

Abstract: In , more connections are built between metaheuristics and electromagnetic equipment design. In this paper, we propose a self-adaptive (SAGWO) combined with a novel optimization model of a 5G (FSS) based on FSS unit nodes. SAGWO includes three improvement strategies, improving the initial distribution, increasing the randomness, and enhancing the local search, to accelerate the convergence and effectively avoid local optima. In benchmark tests, the proposed optimizer performs better than the five other optimization algorithms: original (GWO), genetic algorithm (GA), particle swarm optimizer (PSO), improved (IGWO), and selective opposition based grey wolf optimization (SOGWO). Due to its global searchability, SAGWO is suitable for solving the optimization problem of a 5G FSS that has a large design space. The combination of SAGWO and the new FSS optimization model can automatically obtain the shape of the FSS unit with electromagnetic interference shielding capability at the center operating frequency. To verify the performance of the proposed method, a double-layer ring FSS is designed with the purpose of providing electromagnetic interference shielding features at 28 GHz. The results show that the optimized FSS has better electromagnetic interference shielding at the center frequency and has higher angular stability. Finally, a sample of the optimized FSS is fabricated and tested.

Keywords： Grey wolf optimizer     Fifth-generation wireless communication system (5G)     Frequency selection surface     Shape optimization    

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Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization Research Article

Jian DONG, Xia YUAN, Meng WANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9,   Pages 1390-1406 doi: 10.1631/FITEE.2100420

Abstract: We propose a competitive binary (CBMOGWO) to reduce the heavy computational burden of conventional multi-objective problems. This method introduces a population competition mechanism to reduce the burden of electromagnetic (EM) simulation and achieve appropriate fitness values. Furthermore, we introduce a function of cosine oscillation to improve the linear convergence factor of the original binary (BMOGWO) to achieve a good balance between exploration and exploitation. Then, the optimization performance of CBMOGWO is verified on 12 standard multi-objective test problems (MOTPs) and four multi-objective knapsack problems (MOKPs) by comparison with the original BMOGWO and the traditional binary multi-objective particle swarm optimization (BMOPSO). Finally, the effectiveness of our method in reducing the computational cost is validated by an example of a compact high-isolation dual-band multiple-input multiple-output (MIMO) antenna with high-dimensional mixed design variables and multiple objectives. The experimental results show that CBMOGWO reduces nearly half of the computational cost compared with traditional methods, which indicates that our method is highly efficient for complex problems. It provides new ideas for exploring new and unexpected antenna structures based on multi-objective evolutionary algorithms (MOEAs) in a flexible and efficient manner.

Keywords： Antenna topology optimization     Multi-objective grey wolf optimizer     High-dimensional mixed variables     Fast design    

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Survey on Particle Swarm Optimization Algorithm

Yang Wei,Li Chiqiang

Strategic Study of CAE 2004, Volume 6, Issue 5,   Pages 87-94

Abstract:

Particle swarm optimization (PSO) is a new optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. PSO can be implemented with ease and few parameters need to be tuned. It has been successfully applied in many areas. In this paper, the basic principles of PSO are introduced at length, and various improvements and applications of PSO are also presented. Finally, some future research directions about PSO are proposed.

Keywords： swarm intelligence     evolutionary algorithm     particle swarm optimization    

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Ant Colony Algorithm：Survey and Prospect

Duan Haibin,Wang Daobo, Yu Xiufen

Strategic Study of CAE 2007, Volume 9, Issue 2,   Pages 98-102

Abstract:

 Ant colony algorithm is a novel category of bionic meta唱heuristic system, and parallel computation and positive feedback mechanism are adopted in this algorithm. Ant colony algorithm, which has strong robustness and is easy to combine with other methods in optimization, has wide application in various combined optimization fields. Based on the introduction of the mathematical model of basic ant colony algorithm, typical improved models and applications of the ant colony algorithm in the21st century are listed. Finally, based on the systematic address of model improvement, theoretical analysis, parallel realization, application field,  hardware realization and intelligent combination, the key issues and prospects of the ant colony algorithm are proposed in detail.

Keywords： ant colony algorithm     pheromone     positive feedback     optimization    

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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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An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application Article

Xiao-qing ZHANG, Zheng-feng MING

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1705-1719 doi: 10.1631/FITEE.1601555

Abstract: Due to its simplicity and ease of use, the standard grey wolf optimizer (GWO) is attracting much attention. However, due to its imperfect search structure and possible risk of being trapped in local optima, its application has been limited. To perfect the performance of the algorithm, an optimized GWO is proposed based on a mutation operator and eliminating-reconstructing mechanism (MR-GWO). By analyzing GWO, it is found that it conducts search with only three leading wolves at the core, and balances the exploration and exploitation abilities by adjusting only the parameter a, which means the wolves lose some diversity to some extent. Therefore, a mutation operator is introduced to facilitate better searching wolves, and an eliminating- reconstructing mechanism is used for the poor search wolves, which not only effectively expands the stochastic search, but also accelerates its convergence, and these two operations complement each other well. To verify its validity, MR-GWO is applied to the global optimization experiment of 13 standard continuous functions and a radial basis function (RBF) network approximation experiment. Through a comparison with other algorithms, it is proven that MR-GWO has a strong advantage.

Keywords： Swarm intelligence     Grey wolf optimizer     Optimization     Radial basis function network    

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Dolphin swarm algorithm Article

Tian-qi WU,Min YAO,Jian-hua YANG

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 8,   Pages 717-729 doi: 10.1631/FITEE.1500287

Abstract: By adopting the distributed problem-solving strategy, swarm intelligence algorithms have been successfully applied to many optimization problems that are difficult to deal with using traditional methods. At present, there are many well-implemented algorithms, such as particle swarm optimization, genetic algorithm, artificial bee colony algorithm, and ant colony optimization. These algorithms have already shown favorable performances. However, with the objects becoming increasingly complex, it is becoming gradually more difficult for these algorithms to meet human’s demand in terms of accuracy and time. Designing a new algorithm to seek better solutions for optimization problems is becoming increasingly essential. Dolphins have many noteworthy biological characteristics and living habits such as echolocation, information exchanges, cooperation, and division of labor. Combining these biological characteristics and living habits with swarm intelligence and bringing them into optimization problems, we propose a brand new algorithm named the ‘dolphin swarm algorithm’ in this paper. We also provide the definitions of the algorithm and specific descriptions of the four pivotal phases in the algorithm, which are the search phase, call phase, reception phase, and predation phase. Ten benchmark functions with different properties are tested using the dolphin swarm algorithm, particle swarm optimization, genetic algorithm, and artificial bee colony algorithm. The convergence rates and benchmark function results of these four algorithms are compared to testify the effect of the dolphin swarm algorithm. The results show that in most cases, the dolphin swarm algorithm performs better. The dolphin swarm algorithm possesses some great features, such as first-slow-then-fast convergence, periodic convergence, local-optimum-free, and no specific demand on benchmark functions. Moreover, the dolphin swarm algorithm is particularly appropriate to optimization problems, with more calls of fitness functions and fewer individuals.

Keywords： Swarm intelligence     Bio-inspired algorithm     Dolphin     Optimization    

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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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A review of the multiobjective tradeoff research of construction projects based on intelligent optimization algorithm

Zhang Lianying,Xu Chang,Wu Qiong

Strategic Study of CAE 2012, Volume 14, Issue 11,   Pages 107-112

Abstract:

The optimal equilibrium between the multiple objectives of construction projects is a significant aspect of project management research, which has seen rapid development in recent years, gaining a bunch of fruitful achievements. In this paper, a review is provided for the multiobjective tradeoff research of construction projects based on literature review. Models under deterministic conditions and nondeterministic conditions are investigated and summarized. Some suggestions on the possible direction for future research are included considering the algorithms adopted in the problem solution. This paper aims at providing a review of the achievements in this area so far and keeping track of the ongoing research topics so as to give certain indications for research that follows.

Keywords： construction project     project management     multi-objective optimization     intelligent optimization algorithm    

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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 distributed stochastic optimization algorithm with gradient-tracking and distributed heavy-ball acceleration Research Articles

Bihao Sun, Jinhui Hu, Dawen Xia, Huaqing Li,huaqingli@swu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 11,   Pages 1463-1476 doi: 10.1631/FITEE.2000615

Abstract: has been well developed in recent years due to its wide applications in machine learning and signal processing. In this paper, we focus on investigating to minimize a global objective. The objective is a sum of smooth and strongly convex local cost functions which are distributed over an undirected network of nodes. In contrast to existing works, we apply a distributed heavy-ball term to improve the convergence performance of the proposed algorithm. To accelerate the convergence of existing distributed stochastic first-order gradient methods, a momentum term is combined with a gradient-tracking technique. It is shown that the proposed algorithm has better acceleration ability than GT-SAGA without increasing the complexity. Extensive experiments on real-world datasets verify the effectiveness and correctness of the proposed algorithm.

Keywords： 分布式优化；高性能算法；多智能体系统；机器学习问题；随机梯度    

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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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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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Title Author Date Type Operation

Improved dynamic grey wolf optimizer

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

Journal Article

Correction of array failure using grey wolf optimizer hybridized with an interior point algorithm

Shafqat Ullah KHAN, M. K. A. RAHIM, Liaqat ALI

Journal Article

A novel grey wolf optimizer and its applications in 5G frequency selection surface design

Zhihao HE, Gang JIN, Yingjun WANG

Journal Article

Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization

Jian DONG, Xia YUAN, Meng WANG

Journal Article

Survey on Particle Swarm Optimization Algorithm

Yang Wei,Li Chiqiang

Journal Article

Ant Colony Algorithm：Survey and Prospect

Duan Haibin,Wang Daobo, Yu Xiufen

Journal Article

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

Kai MENG, Chen CHEN, Bin XIN

Journal Article

An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application

Xiao-qing ZHANG, Zheng-feng MING

Journal Article

Dolphin swarm algorithm

Tian-qi WU,Min YAO,Jian-hua YANG

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

A review of the multiobjective tradeoff research of construction projects based on intelligent optimization algorithm

Zhang Lianying,Xu Chang,Wu Qiong

Journal Article

A Pareto Strength SCE-UA Algorithm for ReservoirOptimization Operation

Lin Jianyi,Cheng Chuntian,Gu Yanping,Wu Xinyu

Journal Article

A distributed stochastic optimization algorithm with gradient-tracking and distributed heavy-ball acceleration

Bihao Sun, Jinhui Hu, Dawen Xia, Huaqing Li,huaqingli@swu.edu.cn

Journal Article

Application prospect of PSO in hydrology

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

Journal Article

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

Zhong Denghua,Xiong Kaizhi,Cheng Liqin

Journal Article