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2008 IEEE Swarm Intelligence Symposium最新文献

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Swarm intelligence based optimization and control of decentralized serial sensor networks 基于群智能的分散串行传感器网络优化与控制
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668332
K. Veeramachaneni, L. Osadciw
{"title":"Swarm intelligence based optimization and control of decentralized serial sensor networks","authors":"K. Veeramachaneni, L. Osadciw","doi":"10.1109/SIS.2008.4668332","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668332","url":null,"abstract":"In this paper threshold design and hierarchy management of serial sensor networks employed for distributed detection is accomplished using a hybrid of ant colony optimization and particle swarm optimization. The particle swarm optimization determines the optimal thresholds, decision rules for the sensors. The ant colony optimization algorithm determines the hierarchy of sensor decision communication, affecting the accuracy. The problem of hierarchy management is known as ldquowho reports to whom?rdquo problem in sensor networks. The new algorithm is tested on a suite of 10 heterogeneous sensors. Probabilistic measures including probability of error and Bayesian risk are adopted to evaluate the performance of the sensor network. The new sensor management methodology is compared to (a) static hierarchy network, (b) a network with the best sensor at the top of the hierarchy and (c) incrementally best hierarchy. Results show 40% performance improvements in terms of Bayesian risk value.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"4 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":"127956615","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: 14
A distributed swarm intelligence based algorithm for a cooperative multi-robot construction task 基于分布式群智能的多机器人协同施工算法
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668296
Y. Meng, Jing Gan
{"title":"A distributed swarm intelligence based algorithm for a cooperative multi-robot construction task","authors":"Y. Meng, Jing Gan","doi":"10.1109/SIS.2008.4668296","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668296","url":null,"abstract":"A collective construction task require a multi-robot system to search for randomly distributed building blocks and push those blocks to some predefined locations. To address this problem, a bio-inspired swarm intelligence based algorithm is proposed for a distributed multi-robot system to combine explorative searching and dynamic task allocation together for collective construction. Basically, a virtual pheromone trail based method is proposed as the message passing mechanism among the robots, where robots make distributed movement decisions through local interactions. Since blocks may need multiple robots to work together, dynamic task allocation among robots is necessary. A modified Particle Swarm Optimization (PSO) method is proposed to balance the exploration and exploitation, which helps to allocate reasonable robots to different target blocks. The simulation results in multi-robot construction task demonstrate the efficiency and robustness of the proposed method.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"46 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":"132412923","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: 41
Slime Mold as a model for numerical optimization 黏液霉菌模型的数值优化
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668295
D. Monismith, B. Mayfield
{"title":"Slime Mold as a model for numerical optimization","authors":"D. Monismith, B. Mayfield","doi":"10.1109/SIS.2008.4668295","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668295","url":null,"abstract":"This work presents a novel approach to single objective optimization using the amoeba Dictyostelium discoideum (Dd), sometimes known as Slime Mold, as its basis. A short explanation of the biological background of Dd is presented. Inspirations taken from existing computational biology and educational simulation studies of Dd also are provided. Based upon previous works, an algorithm for optimization is constructed called the Slime Mold Optimization Algorithm.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"7 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":"132508066","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: 48
ACO with multiple nests’ cooperation and its application on narrow TSP 多巢协同蚁群算法及其在窄TSP上的应用
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668305
J. Wang, Wei Wang
{"title":"ACO with multiple nests’ cooperation and its application on narrow TSP","authors":"J. Wang, Wei Wang","doi":"10.1109/SIS.2008.4668305","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668305","url":null,"abstract":"On the base of researches on max-min ant system, a new algorithm named ant colony optimization with multiple nestspsila cooperation (MNC-ACO) is proposed to resolve the narrow traveling salesman problem. In MNC-ACO, we find out the edges contained in the shortest Hamiltonian circuit by the cooperation of elitist ants. Our experimental results clearly show that MNC-ACO has a faster convergence than max-min ant system and its solution quality is better.","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":"133346156","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: 3
Using opposition-based learning to improve the performance of particle swarm optimization 利用基于对立的学习改进粒子群优化的性能
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668288
Mahamed G. H. Omran, S. Al-Sharhan
{"title":"Using opposition-based learning to improve the performance of particle swarm optimization","authors":"Mahamed G. H. Omran, S. Al-Sharhan","doi":"10.1109/SIS.2008.4668288","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668288","url":null,"abstract":"Particle swarm optimization (PSO) is a stochastic, population-based optimization method, which has been applied successfully to a wide range of problems. However, PSO is computationally expensive and suffers from premature convergence. In this paper, opposition-based learning is used to improve the performance of PSO. The performance of the proposed approaches is investigated and compared with PSO when applied to eight benchmark functions. The experiments conducted show that opposition-based learning improves the performance of PSO.","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":"129368912","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
Use of a mixed radix fitness function to evolve swarm behaviors 使用混合基数适应度函数来进化群体行为
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668320
Michael A. Kovacina, M. Branicky, Daniel W. Palmer, R. Vaidyanathan
{"title":"Use of a mixed radix fitness function to evolve swarm behaviors","authors":"Michael A. Kovacina, M. Branicky, Daniel W. Palmer, R. Vaidyanathan","doi":"10.1109/SIS.2008.4668320","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668320","url":null,"abstract":"Architecting systems designed to elicit group-level behavior beyond the capability of any single agent, however, demands a labor and experimentation-intensive cycle on the part of the programmer. As part of a system to evolve swarm behaviors, we have developed a mixed radix fitness function to overcome the problems encountered with typical fitness functions when used in a multi-objective optimization problem. In this work, we show that mixed radix fitness functions can be used to encode sequential dependencies and prioritize metrics within the context of agent-based swarm behavior. To demonstrate the effectiveness of our approach, we construct a mixed radix fitness function and evolve swarm algorithms to solve a complex extension of the classic object collection problem. Further, we show the mixed radix fitness function is successful in driving evolution towards a feasible solution while avoiding local extrema.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"2 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":"125518404","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: 0
A fuzzy ant colony optimization algorithm for topology design of distributed local area networks 分布式局域网拓扑设计的模糊蚁群优化算法
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668303
S. Khan, A. Engelbrecht
{"title":"A fuzzy ant colony optimization algorithm for topology design of distributed local area networks","authors":"S. Khan, A. Engelbrecht","doi":"10.1109/SIS.2008.4668303","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668303","url":null,"abstract":"Ant colony optimization (ACO) is a powerful optimization technique that has been applied to solve a number of complex optimization problems. One such optimization problem is network topology design of distributed local area networks (DLANs). The problem requires simultaneous optimization of a number of objectives, such as monetary cost, average network delay, hop count between communicating nodes, and reliability under a set of constraints. This paper presents a multi-objective ant colony optimization algorithm to efficiently solve the DLAN topology design problem. The multi-objective aspect of the problem is handled by incorporating fuzzy logic in the ACO algorithm. The performance of fuzzy ACO is evaluated through comparison with a fuzzy simulated annealing algorithm. Empirical results suggest that the fuzzy ACO produces results of equal quality when compared with a fuzzy simulated annealing algorithm.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"82 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":"121610976","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
Power system optimization under uncertainties: A PSO approach 不确定条件下电力系统优化:一种粒子群算法
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668276
V. S. Pappala, I. Erlich
{"title":"Power system optimization under uncertainties: A PSO approach","authors":"V. S. Pappala, I. Erlich","doi":"10.1109/SIS.2008.4668276","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668276","url":null,"abstract":"Most power systems optimization problems have to be solved under uncertainty. The scenarios used for modeling the uncertainties should be able to represent their stochastic nature. If this requires huge sampling, particle swarm optimization (PSO) based scenario reduction technique can be a good option to approximate the initial scenario distribution. This paper proposes a multi-stage model for the optimal operation of a wind integrated power system. A parameter free self learning particle swarm optimization algorithm has been used to solve the deterministic and stochastic models. The robustness of the solution procedure has been verified by the effective utilization of the various generation units.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"88 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":"131713049","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: 16
Functional synthesis using discrete particle swarm optimization 基于离散粒子群优化的功能综合
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668306
Bambang A. B. Sarif, M. Abd-El-Barr
{"title":"Functional synthesis using discrete particle swarm optimization","authors":"Bambang A. B. Sarif, M. Abd-El-Barr","doi":"10.1109/SIS.2008.4668306","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668306","url":null,"abstract":"Application of multi-valued (non-binary) digital signals can provide considerable relief for a number of problems faced in binary systems, such as increased functional density and interconnection wirings. Heuristics have been used to synthesize multiple-valued logic (MVL) functions using near optimal number of product terms. In this paper, we explore the use of particle swarm optimization algorithm for synthesis of MV functions. The proposed approach was tested against 50000 randomly generated 2-variable 4-valued functions. The results show that the proposed algorithm outperforms other deterministic and ant colony based approaches in terms of the average number of product terms needed to synthesize a given MVL function.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"112 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":"116869534","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
Estimation of sensor-network topology from time-series sensor data using ant colony optimization method 利用蚁群优化方法从时间序列传感器数据估计传感器网络拓扑
2008 IEEE Swarm Intelligence Symposium Pub Date : 2008-11-07 DOI: 10.1109/SIS.2008.4668278
Kensuke Takahashi, T. Sugawara
{"title":"Estimation of sensor-network topology from time-series sensor data using ant colony optimization method","authors":"Kensuke Takahashi, T. Sugawara","doi":"10.1109/SIS.2008.4668278","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668278","url":null,"abstract":"We propose a method for estimating sensor network topology from only with time-series sensor data and without prior knowledge about the locations of sensors. The proposed method is based on ant colony optimization (ACO) but is further improved, compared with previous work[s], to construct a more accurate topology through an examination of the reliability of the acquired sensor data for the adjacency estimation. This reliability value is used to control the amount of pheromones deposited. We evaluate our method using actual sensor data and show that it can estimate adjacencies, in which the error rate is approximately 87% less than that of the previous method.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"77 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":"134157654","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
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