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检索条件"主题词=Swarm Intelligence algorithm"

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An Improved Sparrow Search algorithm 18

An Improved Sparrow Search Algorithm

引用

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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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...

引用

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 ...

引用

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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Optimal Control and Performance Enhancement of DC-DC Bidirectional SEPIC Converter 13

Optimal Control and Performance Enhancement of DC-DC Bidirec...

引用

IEEE 13th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)

作者： Mahmud, Al Jaber Mithun, Mehedi Hasan Khan, Md. Ashik Faisal, Fahim Nishat, Mirza Muntasir Hoque, Md Ashraful Islamic Univ Technol Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (数字)9781665492997

ISBN: (纸本)9781665492997

This paper represents an inspective work on the closed-loop stability of the Bidirectional SEPIC converter by implementing the Firefly algorithm (FA) and Particle swarm Optimization (PSO) for scheming an optimized PID controller to improve the Percentage of Overshoot (%OS), Rise Time (Tr), Settling Time (Ts), and Peak Amplitude (PA). At first, the transfer function of the converter's open-loop system was determined after which the converter's closed-loop arrangement was introduced with the controller's help. The algorithms were evaluated in the system through different fitness functions: IAE, ITAE, ISE, and ITSE. MATLAB is used to carry out all the simulations. After analyzing the performances for the case of the Bidirectional SEPIC converter, PSO-PID (ITSE) could be chosen as a more optimized controller than FA-based PID controllers as to performance parameters. In this case, values of overshoot (0.2674%) and PA (1.0027) were lower than FA-PID for error function. For rise time, the value of FA-PID (ITAE) was better (0.0670 sec), and for settling time, the value of FA-PID (ITSE) was better (0.1217 sec). Hence, swarm intelligence algorithms-based optimized PID controller provides more optimized results and performs far better than conventional PID controller for Bidirectional SEPIC converter.

关键词： swarm intelligence algorithm Bidirectional SEPIC Optimal Control PID Controller

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Binary-Organoid Particle swarm Optimisation for Inferring Genetic Networks

Binary-Organoid Particle Swarm Optimisation for Inferring Ge...

引用

2010 IEEE World Congress on Computational intelligence

作者： Chanthaphavong, Santi S. Chetty, Madhu Monash Univ Gippsland Sch Informat Technol Fac Informat Technol Churchill 3842 Australia

ISBN: (纸本)9781424481262

A holistic understanding of genetic interactions is crucial in the analysis of complex biological systems. However, due to the dimensionality problem (less samples and large number of genes) of microarray data, obtaining an optimal gene regulatory network is not only difficult but also computationally expensive. In this paper, a Bayesian model for the genetic interactions using the Minimum Description Length as a scoring metric is proposed. For fast optimisation of the network structure, we propose a novel swarm intelligence algorithm called Binary-Organoid Particle swarm (BORG-swarm). In BORG-swarm we introduce the concepts of probability threshold vector and particle drift to update particle positions. Experimental studies are carried out using real-life yeast cell cycle dataset. Results indicate that existing binary swarms fail to converge and suffer from long runtimes. In constrast, BORG-swarm's fast convergence towards the global optimum becomes apparent from results of extensive simulations.

关键词： BORG-swarm algorithm Bayes methods Bayesian model artificial intelligence binary-organoid particle swarm optimisation biology computing complex biological system data handling dimensionality problem genetic algorithms genetic interaction genetic network genetics microarray data minimum description length network structure optimal gene regulatory network particle drift particle swarm optimisation probability probability threshold vector real-life yeast cell cycle dataset scoring metric swarm intelligence algorithm

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Modified Printed Bow-Tie Antenna for RF Energy Harvesting Applications

Modified Printed Bow-Tie Antenna for RF Energy Harvesting Ap...

引用

IEEE Workshop on Microwave Theory and Techniques in Wireless Communications (MTTW)

作者： Boursianis, Achilles D. Papadopoulou, Maria S. Nikolaidis, Spyridon Goudos, Sotirios K. Aristotle Univ Thessaloniki Phys Dept Thesssaloniki Greece

ISBN: (纸本)9781728193984

As the power requirements of electronic systems in wireless sensor networks are progressively reducing, radio frequency (RF) energy harvesting (EH) technique is becoming increasingly interesting during the last years. In this paper, we design and optimize a modified printed bow-tie antenna, operating in the frequency band of 5G NR mobile communication networks. For the optimization process, we have selected the Monarch Butterfly algorithm that combines swarm intelligence and evolutionary characteristics. Computed results exhibit a quite acceptable performance of the optimized antenna as an RF energy harvester in the desired frequency band.

关键词： radio frequency energy harvesting bow-tie antenna monarch butterfly optimization evolutionary algorithm swarm intelligence algorithm

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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...

引用

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

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

引用

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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Pattern Search Firefly algorithm for Solving Systems of Nonlinear Equations 7

Pattern Search Firefly Algorithm for Solving Systems of Nonl...

引用

7th International Symposium on Computational intelligence and Design (ISCID)

作者： Wang, Xiaogang Zhou, Ning Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9781479970056

It is very important problem to solve systems of nonlinear equations in scientific research and engineering computation. Most traditional algorithms have the shortcomings such as high sensitivity to initial guess of the solution and poor convergence. In this paper, we present a method to solve nonlinear equation systems by using the Firefly algorithm. It is very effective to improve computation precision that Firefly algorithm integrates with pattern search strategy. We select several nonlinear equation systems which are solved with Pattern Search Firefly algorithm, while compared with other intelligence optimization algorithms such as Particle swarm Optimization, Genetic algorithm, etc. Numerical results show that Pattern Search Firefly algorithm has availability, global convergence reliability, and high computation precision, especially has advantages in solving higher dimension systems of nonlinear equations.

关键词： Firefly algorithm Pattern search system of nonlinear equations swarm intelligence algorithm bionics algorithm Hooke-Jeeves

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DITGOssi: a two-stage invasive tumor growth optimization algorithm for the detection of SNP-SNP interactions

DITGOssi: a two-stage invasive tumor growth optimization alg...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Tan, Kaiwen Dong, Shoubing Zhou, Jing Hu, Jinlong South China Univ Technol Commun & Comp Network Lab Guangdong Sch Comp Sci & Engn Wushan Rd 381 Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538654880

Detecting SNP-SNP interactions for complex diseases is a computationally complex task in genome-wide association studies (GWAS). The number of single-nucleotide polymorphism (SNP) is so large that many powerful methods can't be adopted to detect potential SNP-SNP interactions, therefore, trade-off between detection time and detection power is the key point of SNP-SNP interactions detection. In this paper, based on swarm optimization algorithm Invasive Tumor Growth Optimization (ITGO), a two-stage algorithm called DITGOssi is proposed to detect SNP-SNP interactions in case-control study, which consists of a basic DITGOssi algorithm and an improved two-stage strategy. The basic DITGOssi algorithm is a discrete ITGO algorithm, and the improved two-stage strategy has been applied to enhance the global search capability of basic DITGOssi algorithm. The experimental results in the simulation datasets indicate that our algorithm outperforms some recent algorithms in terms of detection power and computational complexity.

关键词： swarm intelligence algorithm GWAS SNP-SNP interactions

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