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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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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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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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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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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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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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Solving Knapsack Problem by Hybrid Particle Swarm Optimization Algorithm

Gao Shang,Yang Jingyu

Strategic Study of CAE 2006, Volume 8, Issue 11, Pages 94-98

Abstract:

The classical particle swarm optimization is a powerful method to find the minimum of a numerical function, on a continuous definition domain. The particle swarm optimization algorithm combining with the idea of the genetic algorithm is recommended to solve knapsack problem. All the 6 hybrid particle swarm optimization algorithms are proved effective. Especially the hybrid particle swarm optimization algorithm derived from across strategy A and mutation strategy C is a simple yet effective algorithm and it has been applied successfully to investment problem. It can easily be modified for any combinatorial problem for which there has been no good specialized algorithm.

Keywords： particle swarm algorithm knapsack problem genetic algorithm mutation

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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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Application of Genetic Algorithm in the Optimization of Parameters in Engineering Blasting

Xu Hongtao,Lu Wenbo

Strategic Study of CAE 2005, Volume 7, Issue 1, Pages 76-80

Abstract:

The optimization of blasting parameters in engineering blasting is a complicated nonlinear programming problem. Based on the mathematical model of blasting optimization in open pit mine, the optimization problem is solved with genetic algorithm in this paper, and the feasibility and high effectiveness of optimizing blasting parameters with genetic algorithm are proved by the results. It has provided a new effective approach for solving this problem.

Keywords： engineering blasting mining optimization mathematical model nonlinear programming genetic algorithm

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Efficiency Optimization of Variable Frequency Variable Speed Water-supply Pumping Stations Based on Genetic Algorithm

Liao Li,Lin Jiaheng,Zhang Chenghui

Strategic Study of CAE 2002, Volume 4, Issue 9, Pages 54-58

Abstract:

Water-supply enterprise consumes much of electric energy in the city, and the quantity it consumes mainly depends on the pumps of pumping stations. So it is important to optimize operation of water-supply pumping stations, for safety water supply and saving energy. This paper presents an optimal scheduling model of water-supply pumping stations based on approximation of exponent curve. Corresponding to the model, solutions based on genetic algorithm are introduced. The simulation resuls illustrate its validity.

Keywords： water-supply pumping station approximation of numerical value optimization genetic algorithm

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A surrogate-based optimization algorithm for network design problems Article

Meng LI, Xi LIN, Xi-qun CHEN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11, Pages 1693-1704 doi: 10.1631/FITEE.1601403

Abstract: Network design problems (NDPs) have long been regarded as one of the most challenging problems in the field of transportation planning due to the intrinsic non-convexity of their bi-level programming form. Furthermore, a mixture of continuous/discrete decision variables makes the mixed network design problem (MNDP) more complicated and difficult to solve. We adopt a surrogate-based optimization (SBO) framework to solve three featured categories of NDPs (continuous, discrete, and mixed-integer). We prove that the method is asymptotically completely convergent when solving continuous NDPs, guaranteeing a global optimum with probability one through an indefinitely long run. To demonstrate the practical performance of the proposed framework, numerical examples are provided to compare SBO with some existing solving algorithms and other heuristics in the literature for NDP. The results show that SBO is one of the best algorithms in terms of both accuracy and efficiency, and it is efficient for solving large-scale problems with more than 20 decision variables. The SBO approach presented in this paper is a general algorithm of solving other optimization problems in the transportation field.

Keywords： Network design problem Surrogate-based optimization Transportation planning Heuristics

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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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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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Reliability Topology Optimization of Collaborative Design for Complex Products Under Uncertainties Based on the TLBO Algorithm

Zhaoxi Hong,Xiangyu Jiang,Yixiong Feng,Qinyu Tian,Jianrong Tan

Engineering 2023, Volume 22, Issue 3, Pages 71-81 doi: 10.1016/j.eng.2021.06.027

Abstract:

The topology optimization design of complex products can significantly improve material and power savings, and reduce inertial forces and mechanical vibrations effectively. In this study, a large-tonnage hydraulic press was chosen as a typically complex product to present the optimization method. We propose a new reliability topology optimization method based on the reliability-and-optimization decoupled model and teaching-learning-based optimization (TLBO) algorithm. The supports formed by the plate structure are considered as topology optimization objects, characterized by light weight and stability. The reliability optimization under certain uncertainties and structural topology optimization are processed collaboratively. First, the uncertain parameters in the optimization problem are modified into deterministic parameters using the finite difference method. Then, the complex nesting of the uncertainty reliability analysis and topology optimization are decoupled. Finally, the decoupled model is solved using the TLBO algorithm, which is characterized by few parameters and a fast solution. The TLBO algorithm is improved with an adaptive teaching factor for faster convergence rates in the initial stage and performing finer searches in the later stages. A numerical example of the hydraulic press base plate structure is presented to underline the effectiveness of the proposed method.

Keywords： Plates structure Reliability Collaborative topology optimization Teaching–learning-based optimization algorithm Uncertainty Collaborative design for product life cycle