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检索条件"主题词=swarm intelligence algorithm"

共 183 条记录，以下是121-130 订阅

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Analysis for optimizer based on spiking-neural oscillator networks with a simple network topology

Analysis for optimizer based on spiking-neural oscillator ne...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Sasaki, Tomoyuki Nakano, Hidehiro Shonan Inst Technol Dept Informat Fujisawa Kanagawa Japan Tokyo City Univ Dept Comp Sci Setagaya Ku Tokyo Japan

ISBN: (纸本)9798350330991;9798350331004

Optimizer based on Spiking Neural-oscillator Networks (OSNNs) are one of the deterministic PSO methods, which are based on dynamics of spiking neural-oscillator networks. In OSNNs, each particle consists of plural spiking oscillators which are coupled with other spiking oscillators by the Ring 1-way network. The Ring 1-way network affects search performances of OSNNs. However, an effectiveness of the Ring 1-way network has not been clarified. In this study, we theoretically analyze the Ring 1-way OSNNs in more detail. In addition, in order to demonstrate analytical results, we conduct numerical simulations and discuss the simulation results.

关键词： swarm intelligence algorithm particle swarm optimization deterministic system spiking oscillator networks coupling interaction

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A Novel Bat algorithm based on Collaborative and Dynamic Learning of Opposite Population 22

A Novel Bat Algorithm based on Collaborative and Dynamic Lea...

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22nd IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Yong, Jiashi He, Fazhi Li, Haoran Zhou, Weiqing Wuhan Univ State Key Lab Software Engn Wuhan Hubei Peoples R China Wuhan Univ Sch Comp Sci & Technolgy Wuhan Hubei Peoples R China Sch Comp Sci & Technolgy Wuhan Hubei Peoples R China

ISBN: (纸本)9781538614822

As a new kind of swarm intelligence algorithms, bat algorithm is inspired by the bat's echolocation model to search an optimization solution. In this paper, we propose a novel bat algorithm based on collaborative and dynamic learning of opposite population. The proposed algorithm adapts a collaborative strategy to generate the opposite population. Therefore more possible opposite individuals can be dynamically learned and added to the population. We also present elite choices for the current and the opposite population. In this way, the search diversity and search intensity can be achieved. The experimental results of 8 typical test functions show that the proposed algorithm has the characteristics of fast convergence and avoiding falling into local optimal solution.

关键词： Bat algorithm Opposition-based Learning Function optimization swarm intelligence algorithm

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Path Planning of Mobile Robot Based on Improved Wolf swarm algorithms 8

Path Planning of Mobile Robot Based on Improved Wolf Swarm A...

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IEEE 8th Joint International Information Technology and Artificial intelligence Conference (ITAIC)

作者： Chen, Xu Zhang, Yi Li, Kui Huang, Baiyue Chongqing Univ Posts & Telecommun Chongqing Peoples R China Key Lab Intelligent Mfg & Robot Chongqing Peoples R China

ISBN: (纸本)9781538681787

Aiming at solving the shortcomings of traditional wolf swarm algorithm, such as low search efficiency due to fixed search direction and radius, and easy to fall into local optimum when updating rules, an improved wolf swarm algorithm is proposed in this paper. Firstly, the exploding rules of fireworks algorithm are introduced to reduce the exploring steps of wolves near prey and increase the searching direction in order to improve the ability of local exploration, while the wolves far from prey the searching radius are increased and the searching direction are decreased in order to improve the global search ability. Secondly, the moving step size of running behavior is improved automatically to adjust the step size according to the location of each wolf which can improve the efficiency of running behavior. Finally, the updating rules of wolf swarm algorithm is improved for the individual wolf swarm is selected according to probability to enhance the global optimization ability of the algorithm. The performance test in test function and path planning simulation experiment results show that the improved wolf swarm algorithm has faster convergence speed and higher convergence accuracy.

关键词： swarm intelligence algorithm wolf swarm algorithm path planning adaptive step size

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An Improved Sparrow Search algorithm 18

An Improved Sparrow Search Algorithm

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18th IEEE Int Symp on Parallel and Distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Song, Wei Liu, Song Wang, Xiaochun Wu, Weiguo Xi An Jiao Tong Univ Fac Elect & Informat Engn Xian Peoples R China

ISBN: (纸本)9781665414852

The sparrow search algorithm (SSA) is a relatively new swarm intelligence heuristic algorithm. It has fast convergence speed, strong optimization ability and more extensive application scenarios compared with traditional heuristic search methods. And thus, the SSA is attracting the attention of researchers in different fields. However, there are deficiencies of initial population quality, search ability, and population diversity in the SSA. Therefore, this paper proposes an improved sparrow search algorithm (ISSA). The ISSA uses skew tent map-based chaotic method to produce initial population for a higher quality of convergence. For the location update of the producer sparrows during the iterations, the ISSA introduces a non-linear decreasing weight, promoting both exploration and exploitation of the search space, to improve the convergence and search precision. And the mutation strategy is employed to update the location of the scrounger sparrows with lower energy and the chaotic search is combined with the local exploitation for the scroungers with higher energy, which can enhance the diversity and avoid trapping in local optimum. Simulation experiments are carried out on 26 benchmark test functions. And the results show that the ISSA is superior to or at least competitive to the SSA in the convergence properties of accuracy, speed, and stability.

关键词： swarm intelligence algorithm sparrow search algorithm chaotic search non-linear decreasing weight convergence accuracy

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Electromagnetic Detection of Dielectric Cylinders by the Bat algorithm

Electromagnetic Detection of Dielectric Cylinders by the Bat...

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IEEE Electrical Design of Advanced Packaging and Systems (EDAPS)

作者： Zhang, Xin Yang, Chunxia Tong, Meisong Shanghai Normal Univ Dept Commun Engn Shanghai Peoples R China Tongji Univ Dept Elect Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781665466134

Due to the ill-posedness of the governing equation, the electromagnetic inversion problem is generally solved by iterative optimization of the objective function which is established by adding regularization terms. The traditional deterministic optimization methods completely rely on the gradient information of the objective function, and there would be a great risk of falling into local extrema in the case of unsatisfactory imaging environment and insufficient diversity of measurement data. swarm intelligence optimization algorithms introduce random factors, which can help to get rid of local optima. The bat algorithm (BA) is a recently proposed swarm intelligence algorithm that imitates the bat foraging behavior. It has two update modes include global search and local search, and the individual only moves in a direction better than the current position. In this paper, a customized electromagnetic inversion algorithm based on the standard BA is proposed. The position, contour and permittivity of two-dimensional cylindrical section are reconstructed successfully in the numerical simulation.

关键词： electromagnetic inversion bat algorithm swarm intelligence algorithm

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Priority-Aware Task Offloading and Resource Allocation in Vehicular Edge Computing Networks 18

Priority-Aware Task Offloading and Resource Allocation in Ve...

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18th IEEE International Conference on Mobility, Sensing and Networking (MSN)

作者： Wang, Ye Liu, Yanheng Sun, Zemin Liu, Lingling Li, Jiahui Sun, Geng Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9781665464574

In recent years, the dramatic increase in vehicles and the limited resources of VEC servers make it challenging for vehicles to execute intensive and sensitive tasks on the local own CPU. The mobile edge computing (MEC) is viewed as a promising paradigm by deploying the cloud resources on roadside road side units (RSU). However, compared to cloud server, MEC servers have limited resources. Moreover, the vehicular tasks with different priorities have different requirements on the edge resources. In this work, we propose a priority-aware collaborative task offloading and resource allocation approach for vehicular edge computing networks (VECN). Specifically, we propose a variant grey wolf optimizer (VGWO) algorithm for resource optimization and a dynamic task offloading strategy (DOS) algorithm for task offloading. Simulation results show that the proposed VGWO algorithm outperforms the basic swarm intelligence optimization algorithm, and the collaborative offloading method is able to effectively reduce the task processing latency and energy consumption.

关键词： mobile edge computing task offloading resource allocation swarm intelligence algorithm collaborative offloading method

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Recommendation Method of Ore Blending Based on Thermodynamic Principle and Adaptive Step Size 35th

Recommendation Method of Ore Blending Based on Thermodynamic...

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35th National Conference of Theoretical Computer Science (NCTCS)

作者： Wang, Huan Shen, Bingyang Gao, Yuxing Cao, Yuning Ban, Xiaojuan Univ Sci & Technol Beijing Sch Civil & Resource Engn Beijing Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China

ISBN: (纸本)9789811068935;9789811068928

In the modern metallurgical enterprises, it is necessary to design ore concentrates and production parameters according to the production, which is called ore blending. This paper presents a method of ore blending recommendation using thermodynamic principle and adaptive step size. The whole ore blending process is divided into three parts: blanking recommendation, parameter calculation and result correction. In the part of blanking recommendation, a case-based recommendation system is used to recommend the concentrate list. In the parameter calculation section, the SOMA is used to calculate the concentrate phase. And combined with the method of concentrate phase adjustment, an intelligent algorithm for rapid and accurate calculation of concentrate phase is proposed. Then the smelting parameters are calculated based on thermodynamic principle. In the result correction part, the high accuracy ore blending recommendation is achieved by combining adaptive step size with thermodynamic calculation software. Through the combination of three parts, a fast recommendation method for intelligent ore blending is proposed. The rationality and effectiveness of the method are verified by experiments.

关键词： Ore blending recommendation Concentrate phase calculation swarm intelligence algorithm SOMA Adaptive step size

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A Novel Comparative Study between Dual Population Genetic algorithm and Artificial Bee colony algorithm for Function Optimization 19

A Novel Comparative Study between Dual Population Genetic Al...

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19th International Conference on Computer and Information Technology (ICCIT)

作者： Alam, Faria Saadi, Hossain Shaikh Alam, Mohammad Shafiul Ahsanullah Univ Sci & Technol Dept Comp Sci & Engn Dhaka 1208 Bangladesh

ISBN: (纸本)9781509040902

This paper conducts a comparative study between an improved variants of genetic algorithm (GA) and a swarm intelligence algorithm (SIA), which are the Dual population Genetic algorithm (DPGA) and Artificial Bee Colony (ABC) algorithm. DPGA is a multi-population genetic algorithm (MPGA) that implements two population such as the main population and a complementary population. Since the added population has a totally different fitness function, it is preserved to supply sufficient diversity to the main population by crossover operation. However DPGA employs a dynamic strategy to maintain an appropriate distance between two populations for maintaining diversity. On the contrary ABC, a simple but exceptional derivative of SIA, applies division of labor in single population of artificial bees and allocates some of them to exploration while the others to exploitation. DPGA has its own techniques to sustain the significant balance between exploration vs. exploitation. Thus many such analytical comparisons between DPGA and ABC are the center of attention of this paper. Experiments are conducted on seven benchmark functions using ABC and results are compared with DPGA. The results demonstrate that DPGA performs well for some of the functions but by considering the result of mean absolute error, ABC performs far better than DPGA.

关键词： Dual Population Genetic algorithm Artificial Bee Colony algorithm Genetic algorithm swarm intelligence algorithm

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An Ant Colony System Solving the Travelling Salesman Region Problem 4

An Ant Colony System Solving the Travelling Salesman Region ...

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4th International Symposium on Computational and Business intelligence (ISCBI)

作者： Lammel, Benjamin Gryzlak, Karin Dornberger, Rolf Hanne, Thomas Univ Appl Sci & Arts Northwestern Switzerland Sch Business Inst Business Informat Syst CH-4600 Olten Switzerland

ISBN: (纸本)9781509034888

A variant of the well-known travelling salesman problem is about introducing particular dependencies among the cities. Such dependencies might describe the relations between single cities which can be used for autonomous vehicles that need to follow certain paths for some reasons. This paper deals with solving the Travelling Salesman Region Problem where particular connections of cities are already predefined. We are investigating and discussing the influences of the parameters of the Ant Colony Optimization algorithm solving travelling salesman region problems.

关键词： open opal travelling salesman region problem Ant Colony Optimization ACO swarm intelligence algorithm nature-inspired algorithm

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Adaptive Fireworks algorithm Based on Two-Master Sub-population and New Selection Strategy 24th

Adaptive Fireworks Algorithm Based on Two-Master Sub-populat...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Li, Xiguang Han, Shoufei Zhao, Liang Gong, Changqing Shenyang Aerosp Univ Sch Comp Shenyang 110136 Peoples R China

ISBN: (纸本)9783319700939;9783319700922

Adaptive Fireworks algorithm (AFWA) is an effective algorithm for solving optimization problems. However, AFWA is easy to fall into local optimal solutions prematurely and it also provides a slow convergence rate. In order to improve these problems, the purpose of this paper is to apply two-master sub-population (TMS) and new selection strategy to AFWA with the goal of further boosting performance and achieving global optimization. Our simulation compares the proposed algorithm (TMSFWA) with the FWA-Based algorithms and other swarm intelligence algorithms. The results show that the proposed algorithm achieves better overall performance on the standard test functions.

关键词： Adaptive fireworks algorithm Two-master Sub-population Selection strategy swarm intelligence algorithm Standard test functions

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