{"title":"Decentralized asynchronous particle swarm optimization","authors":"S. Akat, V. Gazi","doi":"10.1109/SIS.2008.4668304","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668304","url":null,"abstract":"In this article we discuss a decentralized totally asynchronous realization of the particle swarm optimization (PSO) algorithm, which is suitable for parallel implementation. The proposed method has important differences from the PSO implementations considered in the literature. In the proposed method the particles are allowed to exchange information and to update their estimates at totally independent time instants. Moreover, time delays during information exchange between particles (leading to use of outdated information) are also allowed. Furthermore, particle neighborhoods are allowed to dynamically change with time. We also provide a mathematical model of the proposed method based on results in the parallel and distributed computation literature. The performance of the proposed algorithm is tested using numerical simulations with benchmark functions.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"52 6 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":"133977920","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}
{"title":"Roach Infestation Optimization","authors":"T. Havens, C. Spain, Nathan G. Salmon, J. Keller","doi":"10.1109/SIS.2008.4668317","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668317","url":null,"abstract":"There are many function optimization algorithms based on the collective behavior of natural systems - Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are two of the most popular. This paper presents a new adaptation of the PSO algorithm, entitled Roach Infestation Optimization (RIO), that is inspired by recent discoveries in the social behavior of cockroaches. We present the development of the simple behaviors of the individual agents, which emulate some of the discovered cockroach social behaviors. We also describe a ldquohungryrdquo version of the PSO and RIO, which we aptly call Hungry PSO and Hungry RIO. Comparisons with standard PSO show that Hungry PSO, RIO, and Hungry RIO are all more effective at finding the global optima of a suite of test functions.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"28 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":"125199905","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}
Antons Rebguns, R. Anderson-Sprecher, D. Spears, W. Spears, A. Kletsov
{"title":"Using scouts to predict swarm success rate","authors":"Antons Rebguns, R. Anderson-Sprecher, D. Spears, W. Spears, A. Kletsov","doi":"10.1109/SIS.2008.4668284","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668284","url":null,"abstract":"The scenario addressed here is that of a swarm of agents (simulated robots) that needs to travel from an initial location to a goal location, while avoiding obstacles. Before deploying the entire swarm, it would be advantageous to have a certain level of confidence that a desired percentage of the swarm will be likely to succeed in getting to the goal. The approach taken in this paper is to use a small group of expendable robot ldquoscoutsrdquo to predict the success probability for the swarm. Two approaches to solving this problem are presented and compared - the standard Bernoulli trials formula, and a new Bayesian formula. Extensive experimental results are summarized that measure and compare the mean-squared error of the predictions with respect to ground truth, under a wide variety of circumstances. Experimental conclusions include the utility of a uniform prior for the Bayesian formula in knowledge-lean situations, and the accuracy and robustness of the Bayesian approach. The paper also reports an intriguing result, namely, that both formulas usually predict better in the presence of inter-agent forces than when their independence assumptions hold.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"42 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":"134043644","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}
{"title":"Particle swarm optimization with spatially meaningful neighbours","authors":"James Lane, A. Engelbrecht, J. Gain","doi":"10.1109/SIS.2008.4668281","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668281","url":null,"abstract":"Neighbourhood topologies in particle swarm optimization (PSO) are typically random in terms of the spatial positions of connected neighbours. This study explores the use of spatially meaningful neighbours for PSO. An approach is designed which uses heuristics to leverage the natural neighbours computed with Delaunay triangulation. The approach is compared to standard PSO sociometries and fitness distance ratio approaches. Although intrinsic properties of Delaunay triangulation limit the practical application of this approach to low dimensions results show that it is a successful particle swarm optimizer.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"13 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":"122329360","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}
{"title":"An analysis of Bare Bones Particle Swarm","authors":"Feng Pan, Xiaohui Hu, R. Eberhart, Yaobin Chen","doi":"10.1109/SIS.2008.4668301","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668301","url":null,"abstract":"The bare bones particle swarm (BBPS) is evolved from the canonical particle swarm optimizer (PSO). The velocity term of the canonical PSO is removed in BBPS and replaced by Gaussian sampling strategy. There is no parameter tuning and it is much easier to implement. In the paper, it is proven that the BBPS can be mathematically deduced from the canonical PSO and a more general formula of BBPS is also presented. The results presented in the paper represent initial results of an ongoing research project effort.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"10 3 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":"130190987","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}
{"title":"Application of Breeder GA to power system controller design","authors":"A. Phiri, K. Folly","doi":"10.1109/SIS.2008.4668328","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668328","url":null,"abstract":"This paper presents the tuning of power system stabilizer (PSS) parameters using a relatively new evolution algorithm called Breeder Genetic Algorithms (BGAs). BGAs are based on the concept of ldquothe survival of the fittestrdquo typical to Genetic Algorithms (GAs). The main difference between GAs and BGAs is that the evolution of BGAspsila population is based on artificial selection similar to the one used by human breeders. However, unlike GAs, the chromosomes in BGAs are always represented as sequences of real numbers rather than sequences of bits or integers. BGAs are particularly suitable to deal with continuous optimization parameters and are a very powerful and versatile optimization algorithm. The proposed BGA-PSS presented in this paper was tested over a wide range of operating conditions and its performance compared with both the Genetic Algorithm based PSS (GA-PSS) and the Conventional PSS (CPSS). Simulation results show that the performance of the BGA-PSS is better than that of the GA-PSS and the CPSS. However, both the BGA-PSS and the GA-PSS outperform the CPSS.","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":"129770880","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}
{"title":"Hardware PSO for sensor network applications","authors":"G. Tewolde, D. M. Hanna, R. Haskell","doi":"10.1109/SIS.2008.4668308","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668308","url":null,"abstract":"This paper addresses the problem of emission source localization in an environment monitored by a distributed wireless sensor network. Typical application scenarios of interest include emergency response and military surveillance. A nonlinear least squares method is employed to model the problem of estimation of the emission source location and the intensity at the source. A particle swam optimization (PSO) approach to solve this problem produces solution qualities that compete well with other best known traditional approaches. Moreover, the PSO solution achieves the best runtime performance compared to the other methods investigated. However, when it is targeted on to low capacity embedded processors PSO itself suffers from poor execution performance. To address this problem a direct, flexible and efficient hardware implementation of the PSO algorithm is developed, resulting in tremendous speedup over software solutions on embedded processors.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"35 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":"126906702","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}
{"title":"Application of Swarm Intelligence to a digital excitation control system","authors":"Kiyong Kim, P. Rao, J. Burnworth","doi":"10.1109/SIS.2008.4668313","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668313","url":null,"abstract":"This paper describes an application of swarm intelligence (SI) technique to a digital excitation control system. Some of the modern voltage regulator systems are utilizing the proportional, integral, and derivative (PID) control for stabilization. Based on given excitation system parameters, several PID tuning approaches are reported. Since in general, these parameters are not available during commissioning, specifically the machine time constants, this lack of information causes a considerable time delay and cost of fuel usage for commissioning the automatic voltage regulator (AVR). To reduce the commissioning time and cost, the excitation system parameters are automatically identified and the PID gains are calculated using well-developed algorithms. A swarm intelligence (SI) technique is proposed to identify the system parameters and compared with recursive least square (RLS) with linearization via feedback. The performance of the proposed method is evaluated with several generator sets. With self-tuned PID gains, commissioning is accomplished very quickly with excellent performance results.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"23 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":"121878285","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}
{"title":"An Evolutionary Particle Swarm Optimization algorithm for data clustering","authors":"Shafiq Alam, G. Dobbie, Patricia J. Riddle","doi":"10.1109/SIS.2008.4668294","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668294","url":null,"abstract":"Clustering is an important data mining task and has been explored extensively by a number of researchers for different application areas such as finding similarities in images, text data and bio-informatics data. Various optimization techniques have been proposed to improve the performance of clustering algorithms. In this paper we propose a novel algorithm for clustering that we call evolutionary particle swarm optimization (EPSO)-clustering algorithm which is based on PSO. The proposed algorithm is based on the evolution of swarm generations where the particles are initially uniformly distributed in the input data space and after a specified number of iterations; a new generation of the swarm evolves. The swarm tries to dynamically adjust itself after each generation to optimal positions. The paper describes the new algorithm the initial implementation and presents tests performed on real clustering benchmark data. The proposed method is compared with k-means clustering- a benchmark clustering technique and simple particle swarm clustering algorithm. The results show that the algorithm is efficient and produces compact clusters.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116229876","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}
{"title":"Stubborn ants","authors":"A. M. Abdelbar","doi":"10.1109/SIS.2008.4668307","DOIUrl":"https://doi.org/10.1109/SIS.2008.4668307","url":null,"abstract":"In ant colony optimization methods, including ant system and max-min ant system, each ant stochastically generates its candidate solution, in a given iteration, based on the same pheromone tau and heuristic eta information as every other ant. In this paper, we propose a variation in which if an ant generates a particular candidate solution St-1 in iteration t - 1, then the solution components of St-1 will have a higher probability of being selected in the candidate solution St generated by that ant in iteration t. In other words, each ant will be biased in favor of its past decisions, i.e. it will be stubborn. We evaluate this variation in the context of max-min ant system and the traveling salesman problem (TSP), using different degrees of stubbornness, and applying the ANOVA test of statistical significance to determine the level of significance of the results.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124897172","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}
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