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2008 IEEE Swarm Intelligence Symposium最新文献

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Accurate parameter estimation of contaminant transport inverse problem using particle swarm optimization 基于粒子群优化的污染物输运反问题参数精确估计
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668334
T. V. Bharat, P. Sivapullaiah, M. M. Allam
{"title":"Accurate parameter estimation of contaminant transport inverse problem using particle swarm optimization","authors":"T. V. Bharat, P. Sivapullaiah, M. M. Allam","doi":"10.1109/SIS.2008.4668334","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668334","url":null,"abstract":"Swarm Intelligence techniques such as particle swarm optimization (PSO) are shown to be incompetent for an accurate estimation of global solutions in several engineering applications. This problem is more severe in case of inverse optimization problems where fitness calculations are computationally expensive. In this work, a novel strategy is introduced to alleviate this problem. The proposed inverse model based on modified particle swarm optimization algorithm is applied for a contaminant transport inverse model. The inverse models based on standard-PSO and proposed-PSO are validated to estimate the accuracy of the models. The proposed model is shown to be out performing the standard one in terms of accuracy in parameter estimation. The preliminary results obtained using the proposed model is presented in this work.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"39 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":"120934633","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: 9
Design of intelligent ship autopilots using particle swarm optimization 基于粒子群算法的智能船舶自动驾驶仪设计
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668327
B. Samanta, C. Nataraj
{"title":"Design of intelligent ship autopilots using particle swarm optimization","authors":"B. Samanta, C. Nataraj","doi":"10.1109/SIS.2008.4668327","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668327","url":null,"abstract":"A study is presented on the application of particle swarm optimization (PSO) to design intelligent autopilots for ship steering. Two versions of PSO-conventional and anti-predatory (APSO) - have been used. The autopilot consists of a fuzzy logic controller (FLC) emulating the characteristics of manual ship steering. The parameters for the FLC are optimized using PSO and APSO. The robustness of the autopilot based on the optimized FLC is illustrated through the simulation of a realistic nonlinear ship model. The steering performance of the optimal FLC is compared with a PID type autopilot designed with and without PSO.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"32 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":"127515340","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: 9
Velocity self-adaptation made Particle Swarm Optimization faster 速度自适应提高了粒子群优化的速度
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668280
Guangming Lin, Lishan Kang, Yongsheng Liang, Yuping Chen
{"title":"Velocity self-adaptation made Particle Swarm Optimization faster","authors":"Guangming Lin, Lishan Kang, Yongsheng Liang, Yuping Chen","doi":"10.1109/SIS.2008.4668280","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668280","url":null,"abstract":"The lognormal self-adaptation has been used extensively in evolutionary programming (EP) and evolution strategies (ES) to adjust the search step size for each objective variable. The particle swarm optimization (PSO) relies on two kinds of factors: velocity and position of particles to generate better particles. In this paper, we propose self-adaptive velocity PSO (SAVPSO) in which we firstly introduce lognormal self-adaptation strategies to efficiently control the velocity of PSO. Extensive empirical studies have been carried out to evaluate the performance of SAVPSO, standard PSO and some other improved versions of PSO. From the experimental results on 7 widely used test functions, we can show that SAVPSO outperforms standard PSO.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"108 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":"116014385","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: 2
Optimal SVM switching for a multilevel multi-phase machine using modified discrete PSO 基于改进离散粒子群算法的多电平多相电机支持向量机最优切换
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668326
C. Hutson, G. Venayagamoorthy, K. Corzine
{"title":"Optimal SVM switching for a multilevel multi-phase machine using modified discrete PSO","authors":"C. Hutson, G. Venayagamoorthy, K. Corzine","doi":"10.1109/SIS.2008.4668326","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668326","url":null,"abstract":"This paper searches for the best possible switching sequence in a multilevel multi-phase inverter that gives the lowest amount of voltage harmonics. A modified discrete particle swarm (MDPSO) algorithm is used in an attempt to find the optimal space vector modulation switching sequence that results in the lowest voltage THD. As with typical PSO cognitive and social parameters are used to guide the search, but an additional mutation term is added to broaden the amount of area searched. The search space is the feasible solutions for the predetermined vectors at a given modulation index. Comparison of the MDPSO algorithm to an integer particle swarm optimization (IPSO) is presented for all three modulation indices tested. The resulting switching sequences found show that the MDPSO algorithm is capable of finding a minimal THD solution for all modulations indices tested. The MDPSO algorithm performed better overall than the IPSO in terms of converging to the best solution with significantly lower iterations.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"24 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":"122600187","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: 30
Assembly of configurations in a networked robotic system: A case study on a reconfigurable interactive table lamp 网络机器人系统配置的装配:可重构交互式台灯的案例研究
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668318
Christopher M. Cianci, J. Nembrini, A. Prorok, A. Martinoli
{"title":"Assembly of configurations in a networked robotic system: A case study on a reconfigurable interactive table lamp","authors":"Christopher M. Cianci, J. Nembrini, A. Prorok, A. Martinoli","doi":"10.1109/SIS.2008.4668318","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668318","url":null,"abstract":"In the present study, we are interested in verifying how the progressive addition of constraints on communication and localization impact the performance of a swarm of small robots in shape formation tasks. Identified to be of importance in a swarm-user interaction context, the time required to construct a given spatial configuration is considered as a performance metric. The experimental work reported in this paper starts from global and synchronized localization information, shown to be successful both on a real hardware system and in simulation. In a second step, communication is constrained to a local scale, thus obliging a single designated robot to disseminate the global localization information to the other agents. The reliability of the radio communication channel and its impact upon the performance of the system are considered.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"32 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":"122097495","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: 6
Swarm intelligence for self-reconfiguring walking robot 自重构步行机器人的群体智能
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668286
Bojan Jakimovski, Benjamin Meyer, E. Maehle
{"title":"Swarm intelligence for self-reconfiguring walking robot","authors":"Bojan Jakimovski, Benjamin Meyer, E. Maehle","doi":"10.1109/SIS.2008.4668286","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668286","url":null,"abstract":"A robust swarm intelligence based approach for the self-reconfiguration of a fault-tolerant multi-legged walking robot is elaborated in this paper. It is used to reconfigure the posture of the legs of the robot after some failure has occurred within the robotpsilas legs and it is based on the intrinsic properties seen within swarms and boids in nature. The reconfiguration method presented does not consider any conventional inverse kinematics modeling. Instead it is solely based on swarm intelligence concepts. Throughout the paper we describe the idea behind this approach, the principle of its operation, and we demonstrate its practical usefulness in several test-cases demonstrated on our hexapod robot - OSCAR (Organic Self Configuring and Adapting Robot).","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"1 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":"122183499","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: 20
Reinforcement learning for neural networks using swarm intelligence 基于群体智能的神经网络强化学习
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668289
Matthew Conforth, Y. Meng
{"title":"Reinforcement learning for neural networks using swarm intelligence","authors":"Matthew Conforth, Y. Meng","doi":"10.1109/SIS.2008.4668289","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668289","url":null,"abstract":"In this paper, we propose a swarm intelligence based reinforcement learning (SWIRL) method to train artificial neural networks (ANN). Basically, two swarm intelligence based algorithms are combined together to train the ANN models. Ant Colony Optimization (ACO) is applied to select ANN topology, while Particle Swarm Optimization (PSO) is applied to adjust ANN connection weights. To evaluate the performance of the SWIRL model, it is applied to double pole problem and robot localization through reinforcement learning. Extensive simulation results successfully demonstrate that SWIRL offers performance that is competitive with modern neuroevolutionary techniques, as well as its viability for real-world problems.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"50 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":"130331966","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: 13
Weighting particle swarm, simulation annealing and local search optimization for S/MIMO MC-CDMA systems S/MIMO MC-CDMA系统的加权粒子群、模拟退火和局部搜索优化
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668315
T. Abrão, F. Ciriaco, Leonardo D. Oliveira, B. Angélico, P. Jeszensky, F. Casadevall
{"title":"Weighting particle swarm, simulation annealing and local search optimization for S/MIMO MC-CDMA systems","authors":"T. Abrão, F. Ciriaco, Leonardo D. Oliveira, B. Angélico, P. Jeszensky, F. Casadevall","doi":"10.1109/SIS.2008.4668315","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668315","url":null,"abstract":"This paper analyzes the complexity-performance trade-off of three heuristic approaches applied to synchronous multicarrier multiuser detection (MUD) of single/multiple transmit antennas and multiple receive antennas code division multiple access (S/MIMO MC-CDMA) systems. Weighting particle swarm optimization (WOPSO) and unitary Hamming distance search-based strategies, specifically 1-opt local search (1-LS) and simulation annealing (SA) multiuser detection algorithms, were analyzed in details using a single-objective antenna-diversity-aided optimization approach. Monte-Carlo simulations show that, after convergence, the performances reached by the three heuristic MUD (HEUR-MUD) S/MIMO MC-CDMA algorithms are identical, with computational complexities remarkably smaller than the optimum multiuser detector (OMUD). However, the computational complexities could differ substantially depending on the operation system conditions. The complexities of the HEUR-MUDs were carefully analyzed in order to demonstrate that 1-LS scheme provides the best trade-off between implementation complexity aspects and bit error rate (BER) performance when applied to multiuser detection of S/MIMO MC-CDMA systems with low order modulation.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"75 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":"132656117","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: 5
Integer-valued Particle Swarm Optimization applied to Sudoku puzzles 整数值粒子群算法在数独游戏中的应用
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668293
J. Hereford, Hunter Gerlach
{"title":"Integer-valued Particle Swarm Optimization applied to Sudoku puzzles","authors":"J. Hereford, Hunter Gerlach","doi":"10.1109/SIS.2008.4668293","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668293","url":null,"abstract":"In this paper we develop a variation of the particle swarm optimization (PSO) algorithm that is tailored to discrete optimization problems. We focus on solving Sudoku puzzles but the ideas can be extended to other problems with discrete solutions. We compare our PSO-based algorithm to the classic PSO and to a (mu+lambda) evolutionary strategy (ES) for 50 puzzles and find that the PSO algorithms do much worse than the ES. We then consider why PSO does not do well on this type of problem.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"70 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":"116341604","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: 18
A new UAV assignment model based on PSO 基于粒子群算法的无人机分配模型
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668282
Feng Pan, Xiaohui Hu, R. Eberhart, Yaobin Chen
{"title":"A new UAV assignment model based on PSO","authors":"Feng Pan, Xiaohui Hu, R. Eberhart, Yaobin Chen","doi":"10.1109/SIS.2008.4668282","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668282","url":null,"abstract":"An unmanned aerial vehicle (UAV) assignment model requires allocating vehicles to targets to perform various tasks. It is a complex assignment problem with hard constraints, and potential dimensional explosion when the scenarios become more complicated and the size of problems increases. In this paper, a new UAV assignment model is proposed which reduces the dimension of the solution space and can be easily adapted by computational intelligence algorithms. In the proposed model a local version of particle swarm optimization (PSO) is applied to accomplish the optimization work. Numerical experimental results illustrate that it can efficiently achieve the optima and demonstrate the effectiveness of combining the model and a local version of PSO to solve complex UAV assignment problems.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"20 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":"127303315","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: 19
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