2008 IEEE Swarm Intelligence Symposium最新文献_第3页

Swarm intelligence and evolutionary approaches for reactive power and voltage control 群智能与无功电压控制的进化方法

2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668314

L. Grant, G. Venayagamoorthy, G. Krost, G. Bakare

{"title":"Swarm intelligence and evolutionary approaches for reactive power and voltage control","authors":"L. Grant, G. Venayagamoorthy, G. Krost, G. Bakare","doi":"10.1109/SIS.2008.4668314","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668314","url":null,"abstract":"This paper presents a comparison of swarm intelligence and evolutionary techniques based approaches for minimization of system losses and improvement of voltage profiles in a power network. Efficient distribution of reactive power in an electric network can be achieved by adjusting the excitation on generators, the on-load tap changer positions of transformers, and proper switching of discrete portions of inductors or capacitors. This is a mixed integer non-linear optimization problem where metaheuristics techniques have proven suitable for providing optimal solutions. Four algorithms explored in this paper include differential evolution (DE), particle swarm optimization (PSO), a hybrid combination of DE and PSO, and a mutated PSO (MPSO) algorithm. The effectiveness of these algorithms is evaluated based on their solution quality and convergence characteristic. Simulation studies on the Nigerian power system show that a PSO based solution is more effective than a DE approach in reducing real power losses while keeping the voltage profiles within acceptable limits. The results also show that MPSO allows for further reduction of the real power losses while maintaining a satisfactory voltage profile.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114598516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

Multi-objective design optimization of Surface Mount Permanent Magnet machine with particle swarm intelligence 基于粒子群智能的表面贴装永磁电机多目标设计优化

2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668319

Y. Duan, R. Harley, T. Habetler

引用次数: 27

Particle Swarm Optimization with Quantum Infusion for the design of digital filters 基于量子注入的粒子群算法设计数字滤波器

2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668316

Bipul Luitel, G. Venayagamoorthy

引用次数: 33

Human vs. swarm: An NK landscape game 人类vs.蜂群:一款NK景观游戏

2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668283

Xiaohui Hu, R. Eberhart

引用次数: 5

Balancing transmission power and hop count in ad hoc unicast routing with swarm intelligence 基于群智能的自组织单播路由中传输功率和跳数的平衡

2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668323

Ke Li, Chien-Chung Shen

引用次数: 0

A study of Particle Swarm Optimization on leukocyte adhesion molecules and control strategies for smart prosthetic hand 智能假手白细胞粘附分子的粒子群优化及控制策略研究

2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668324

Cheng-Hung Chen, K. Bosworth, M. Schoen, S. Bearden, D. Naidu, A. P. Gracia

引用次数: 9

Surrogate-based Multi-Objective Particle Swarm Optimization 基于代理的多目标粒子群优化

2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668300

Luis V. Santana-Quintero, C. Coello, A. G. Hernández-Díaz, J. Velázquez-Reyes

{"title":"Surrogate-based Multi-Objective Particle Swarm Optimization","authors":"Luis V. Santana-Quintero, C. Coello, A. G. Hernández-Díaz, J. Velázquez-Reyes","doi":"10.1109/SIS.2008.4668300","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668300","url":null,"abstract":"This paper presents a new algorithm that approximates real function evaluations using supervised learning with a surrogate method called support vector machine (SVM). We perform a comparative study among different leader selection schemes in a multi-objective particle swarm optimizer (MOPSO), in order to determine the most appropriate approach to be adopted for solving the sort of problems of our interest. The resulting hybrid presents a poor spread of solutions, which motivates the introduction of a second phase to our algorithm, in which an approach called rough sets is adopted in order to improve the spread of solutions along the Pareto front. Rough sets are used as a local search engine, which is able to generate solutions in the neighborhood of the nondominated solutions previously generated by the surrogate-based algorithm. The resulting approach is able to generate reasonably good approximations of the Pareto front of problems of up to 30 decision variables with only 2,000 fitness function evaluations. Our results are compared with respect to the NSGA-II, which is a multi-objective evolutionary algorithm representative of the state-of-the-art in the area.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133489257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Image-based tracking with Particle Swarms and Probabilistic Data Association 基于粒子群和概率数据关联的图像跟踪

2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668297

E. Kao, Peter VanMaasdam, John W. Sheppard

引用次数: 4

Particle swarm optimization assisted multiuser detector for M-QAM DS/CDMA systems 基于粒子群优化的M-QAM DS/CDMA多用户检测器

2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668321

Leonardo D. Oliveira, T. Abrão, P. Jeszensky, F. Casadevall

{"title":"Particle swarm optimization assisted multiuser detector for M-QAM DS/CDMA systems","authors":"Leonardo D. Oliveira, T. Abrão, P. Jeszensky, F. Casadevall","doi":"10.1109/SIS.2008.4668321","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668321","url":null,"abstract":"This paper analyzes the particle swarm optimization multiuser detector (PSO-MUD) under high-order modulation schemes, (particularly for M-QAM), in DS/CDMA systems single-input-single-output (SISO) multipath channels. In order to avoid the computation of complex-valued variables in high-order squared modulation, the optimization problem is reformulated as a real-valued problem. Considering previous results on literature for low-order modulation formats (usually binary/quadrature phase shift keying - BPSK/QPSK), a performancetimescomplexity trade-off comparison is carried out between PSO-MUD and local search multiuser detector (LS-MUD). Performance is evaluated by the symbol error rate (SER), and complexity by necessary number of cost function calculations for convergence. If the background for BPSK heuristic multiuser detection (HEURMUD) problem indicates that the 1-opt local search method is enough to achieve excellent performancetimescomplexity trade-offs, our Monte-Carlo simulation results and analysis show herein indicate the LS-MUD presents a lack of search diversity under high-order modulation formats, while the PSO-MUD is efficient to solve the MUD problem for high-order modulation schemes.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132731850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Catfish particle swarm optimization 鲶鱼粒子群优化

2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668277

Li-Yeh Chuang, S. Tsai, Cheng-Hong Yang

引用次数: 36