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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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A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization Research Articles

Ming-gang Dong, Bao Liu, Chao Jing,jingchao@glut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 8,   Pages 1119-1266 doi: 10.1631/FITEE.1900321

Abstract: The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design, overlapping community detection, power dispatch, and unmanned aerial vehicle formation. To address such issues, current approaches focus mainly on problems with regular Pareto front rather than solving the . Considering this situation, we propose a many-objective evolutionary algorithm based on decomposition with (MaOEA/D-DRA) for irregular optimization. The proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem’s Pareto front. An evolutionary population and an are used in the search process, and information extracted from the is used to guide the evolutionary population to different search regions. The evolutionary population evolves with the to decompose a problem into several subproblems, and all the subproblems are optimized in a collaborative manner. The is updated with the method of . The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms using a variety of test problems with . Experimental results show that the proposed algorithm outperforms these five algorithms with respect to convergence speed and diversity of population members. By comparison with the weighted-sum approach and penalty-based boundary intersection approach, there is an improvement in performance after integration of the into the proposed algorithm.

Keywords： Many-objective optimization problems     Irregular Pareto front     External archive     Dynamic resource allocation     Shift-based density estimation     Tchebycheff approach    

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United Algorithm for Dynamic Subcarrier, Bit and Power Allocation in OFDM System

Gao Huanqin,Feng Guangzeng,Zhuqi

Strategic Study of CAE 2006, Volume 8, Issue 3,   Pages 62-65

Abstract:

A realtime united algorithm for dynamic subbcarrier, bit and power allocation according to the change of channel (UA) is presented in this paper, which can be used into the down-link of multi-user orthogonal frequency division multiplexing (OFDM) system. With the algorithm the total transmission power is the minimum while the data rate of each user and the required BER performance can be achieved. Comparing to the subcarrier allocation algorithm (WSA) , the simulation results show that the algorithm presented in this paper has better performance while both have equal calculating complexity.

Keywords： OFDM     Wong's subcarrier allocation (WSA)     UA    

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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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Application Research on Vehicle Routing Problem With Time Windows Based on Dynamic Ant Algorithm

Liu Yunzhong,Xuan Huiyu

Strategic Study of CAE 2005, Volume 7, Issue 12,   Pages 35-40

Abstract:

Ant algorithm is a newly emerged stochastic searching optimization algorithm in recent years. It has been paid much attention to since the successful application in the famous traveling salesman problem. This paper further extends the idea of this new biological optimization strategy to vehicle routing problem with time windows in logistic management and designs a new kind of dynamic ant algorithm. The ability of optimization of this new ant algorithm is tested through numerical computation which gives encouraging results.

Keywords： ant algorithm     vehicle routing problem with time windows     logistic management     dynamic    

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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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Efficient and optimized approximate GDI full adders based on dynamic threshold CNTFETs for specific least significant bits Research Article

Ayoub SADEGHI, Razieh GHASEMI, Hossein GHASEMIAN, Nabiollah SHIRI,H.ghasemian@sutech.ac.ir

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 4,   Pages 599-616 doi: 10.1631/FITEE.2200077

Abstract: Carbon nanotube field-effect transistors (CNTFETs) are reliable alternatives for conventional transistors, especially for use in (AC) based error-resilient digital circuits. In this paper, CNTFET technology and the technique are merged, and three new AC-based full adders (FAs) are presented with 6, 6, and 8 transistors, separately. The is used to attain the optimal performance of the proposed cells by considering the number of tubes and chirality vectors as its variables. The results confirm the circuits’ improvement by about 50% in terms of power-delay-product (PDP) at the cost of area occupation. The Monte Carlo method (MCM) and 32-nm CNTFET technology are used to evaluate the lithographic variations and the stability of the proposed circuits during the fabrication process, in which the higher stability of the proposed circuits compared to those in the literature is observed. The dynamic threshold (DT) technique in the transistors of the proposed circuits amends the possible voltage drop at the outputs. Circuitry performance and error metrics of the proposed circuits nominate them for the least significant bit (LSB) parts of more complex arithmetic circuits such as multipliers.

Keywords： Carbon nanotube field-effect transistor (CNTFET)     Optimization algorithm     Nondominated sorting based genetic algorithm II (NSGA-II)     Gate diffusion input (GDI)     Approximate computing    

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Study on Algorithm A* Based on Dynamic Representation of Binary Tree for Environment and Robotic Path Planning

Tang Ping,Yang Yimin

Strategic Study of CAE 2002, Volume 4, Issue 9,   Pages 50-53

Abstract:

This paper deals with a way of representing dynamic environment based on binary-tree. A new algorithm A* , which plans the path of soccer robots in complicated environments is presented. With the soccer game's environments represented by dynamic binary-tree, optimal results were obtained in a simulation of the soccer game.

Keywords： dynamic binary-tree     algorithm A*     path planning    

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Research on Battery-aware Dynamic Voltage Scaling Policy

Xu Shen,Hu Chen

Strategic Study of CAE 2008, Volume 10, Issue 2,   Pages 79-85

Abstract:

Battery lifetime is one of the critical design parameters for mobile computing d evices. Maximizing the battery lifetime is a particularly difficult problem due to the nonlinearity of the battery discharge behavior and its dependence on the discharge profile. In this paper, the problem of task scheduling with dynamic voltage scaling such that the maximum consume battery capacity is addressed. T o deal with the shortcoming of the existing battery-aware DVS policy, the idle time distribution adjustment procedure is proposed, which optimizes id le time dis tribution and then reduce the battery capacity consumption, and the cha nge of task scheduling under the procedure is analyzed. The experiment results s how that the proposed procedure can save more battery capacity consumption evidently than th e existing battery-aware DVS policy.

Keywords： battery optimization     dynamic voltage scaling     adjustment of idle time distribution    

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Fuzzy Optimum Selection Dynamic Programming Methodology for Multi-objective Optimization of Multi-stage Systems

Xiong Deqi,Yin Peihai

Strategic Study of CAE 2000, Volume 2, Issue 9,   Pages 65-69

Abstract:

Based on the concepts of the fuzzy weighted distance and membership degree, the fuzzy optimum selection dynamic programming technique that can be used for the optimization of multi-objective and multi-stage systems are developed by means of the combination of fuzzy optimum selection theory with dynamic programming technique. This is a new methodology for solving the multi-objective optimization problems of multi-stage systems. Finally, an application to the optimization of a multiple reactor system is given as an example.

Keywords： multi-stage     multi-objective optimization     fuzzy optimum selection     dynamic programming     membership degree    

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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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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

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

Jian DONG, Xia YUAN, Meng WANG

Journal Article

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

Zhihao HE, Gang JIN, Yingjun WANG

Journal Article

A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization

Ming-gang Dong, Bao Liu, Chao Jing,jingchao@glut.edu.cn

Journal Article

United Algorithm for Dynamic Subcarrier, Bit and Power Allocation in OFDM System

Gao Huanqin,Feng Guangzeng,Zhuqi

Journal Article

Survey on Particle Swarm Optimization Algorithm

Yang Wei,Li Chiqiang

Journal Article

Application Research on Vehicle Routing Problem With Time Windows Based on Dynamic Ant Algorithm

Liu Yunzhong,Xuan Huiyu

Journal Article

A Pareto Strength SCE-UA Algorithm for ReservoirOptimization Operation

Lin Jianyi,Cheng Chuntian,Gu Yanping,Wu Xinyu

Journal Article

Efficient and optimized approximate GDI full adders based on dynamic threshold CNTFETs for specific least significant bits

Ayoub SADEGHI, Razieh GHASEMI, Hossein GHASEMIAN, Nabiollah SHIRI,H.ghasemian@sutech.ac.ir

Journal Article

Study on Algorithm A* Based on Dynamic Representation of Binary Tree for Environment and Robotic Path Planning

Tang Ping,Yang Yimin

Journal Article

Research on Battery-aware Dynamic Voltage Scaling Policy

Xu Shen,Hu Chen

Journal Article

Fuzzy Optimum Selection Dynamic Programming Methodology for Multi-objective Optimization of Multi-stage Systems

Xiong Deqi,Yin Peihai

Journal Article

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

Zhong Denghua,Xiong Kaizhi,Cheng Liqin

Journal Article

Solving Knapsack Problem by Hybrid Particle Swarm Optimization Algorithm

Gao Shang,Yang Jingyu

Journal Article