Pub Date : 2013-04-16DOI: 10.1109/SIS.2013.6615179
Mohar Singh, Ameya Patkar, M. Pant, Ankita Jain
Over the past few decades, differential evolution (DE) has emerged as a versatile algorithm for solving a wide range of global optimization problems arising in various fields. The present study discusses a novel application of DE for thermal optimization of a single inlet T-junction. It is a common but complex problem arising in the field of thermal engineering and can be formulated as a global optimization problem subject to constraints. The current study shows the efficiency of DE in dealing with such problems.
{"title":"Global optimization of a single inlet T- junction cooling system using differential evolution","authors":"Mohar Singh, Ameya Patkar, M. Pant, Ankita Jain","doi":"10.1109/SIS.2013.6615179","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615179","url":null,"abstract":"Over the past few decades, differential evolution (DE) has emerged as a versatile algorithm for solving a wide range of global optimization problems arising in various fields. The present study discusses a novel application of DE for thermal optimization of a single inlet T-junction. It is a common but complex problem arising in the field of thermal engineering and can be formulated as a global optimization problem subject to constraints. The current study shows the efficiency of DE in dealing with such problems.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128029223","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}
Pub Date : 2013-04-16DOI: 10.1109/SIS.2013.6615183
B. Theja, A. Rajasekhar, D. Kothari, Swagatam Das
In this paper an optimally designed PID controller equipped with Power System Stabilizer (PSS) for a Single Machine Infinite Bus (SMIB) system using linearized Modified Philip-Heffron's model is presented. The PSS design based on this model utilizes signals available within the generating station and doesn't require the knowledge about external system parameters like line impedance and infinite bus voltage. A new swarm intelligent Artificial Bee Colony (ABC) algorithm has been used to tune the PSS-PID parameters to enhance the small signal stability due to small variations in generation and loads. Various simulation results and comparisons over different loading conditions on a single machine infinite bus power system using ABC tuned PID-PSS show the superiority of ABC in designing the power system stabilizer for the model considered.
{"title":"Design of PID controller based power system stabilizer using Modified Philip-Heffron's model: An artificial bee colony approach","authors":"B. Theja, A. Rajasekhar, D. Kothari, Swagatam Das","doi":"10.1109/SIS.2013.6615183","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615183","url":null,"abstract":"In this paper an optimally designed PID controller equipped with Power System Stabilizer (PSS) for a Single Machine Infinite Bus (SMIB) system using linearized Modified Philip-Heffron's model is presented. The PSS design based on this model utilizes signals available within the generating station and doesn't require the knowledge about external system parameters like line impedance and infinite bus voltage. A new swarm intelligent Artificial Bee Colony (ABC) algorithm has been used to tune the PSS-PID parameters to enhance the small signal stability due to small variations in generation and loads. Various simulation results and comparisons over different loading conditions on a single machine infinite bus power system using ABC tuned PID-PSS show the superiority of ABC in designing the power system stabilizer for the model considered.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132885777","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}
Pub Date : 2013-04-16DOI: 10.1109/SIS.2013.6615187
Yuanqing Li, Mengshi Li, Z. Ji, Qinghua Wu
This paper presents an enhanced group search optimizer (GSO), group search optimizer with intraspecific competition and lévy walk (GSOICLW), to solve the optimal power flow (OPF) problem. GSOICLW s a more biologically realistic algorithm and performs better balance between global and local searching than GSO n hat intraspecific competition IC) and lévy walk (LW) are introduced o GSO. GSOICLW is tested or the OPF problem on the IEEE 30-bus power system, with green house gases emission constraint considered. Simulation results demonstrate the accuracy and reliability of the proposed algorithm, compared with other evolutionary algorithms EAs).
针对最优潮流问题，提出了一种改进的群体搜索优化器(GSO)，即具有种群内竞争和种群内游动的群体搜索优化器(GSOICLW)。GSOICLW是一种更符合生物现实的算法，在引入种内竞争IC (intra - specific competition IC)和LW (LW)后，比GSO更好地平衡了全局和局部搜索。GSOICLW在考虑温室气体排放约束的IEEE 30总线电力系统上对OPF问题进行了测试。仿真结果验证了该算法的准确性和可靠性，并与其他进化算法进行了比较。
{"title":"Optimal power flow using group search optimizer with intraspecific competition and lévy walk","authors":"Yuanqing Li, Mengshi Li, Z. Ji, Qinghua Wu","doi":"10.1109/SIS.2013.6615187","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615187","url":null,"abstract":"This paper presents an enhanced group search optimizer (GSO), group search optimizer with intraspecific competition and lévy walk (GSOICLW), to solve the optimal power flow (OPF) problem. GSOICLW s a more biologically realistic algorithm and performs better balance between global and local searching than GSO n hat intraspecific competition IC) and lévy walk (LW) are introduced o GSO. GSOICLW is tested or the OPF problem on the IEEE 30-bus power system, with green house gases emission constraint considered. Simulation results demonstrate the accuracy and reliability of the proposed algorithm, compared with other evolutionary algorithms EAs).","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128982770","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}
Pub Date : 2013-04-16DOI: 10.1109/SIS.2013.6615189
P. Tomar, M. Pant
Since last few decades differential evolution algorithm (DE) has been successfully applied for solving many real life optimization problems. In this paper DE is applied to identifying the location of noisy sources in a multi noise plants. A trail noise technique is used to obtain the variation between trial sound pressure level (SPL) and exact SPL at monitoring points and then DE is employed in conjunction with the method of minimized variation square in seeking for the best locations and sound power level (SWLs). The results reveal that the significant locations and SWLs of noises can be precisely identified by DE.
{"title":"Noisy source recognition in multi noise plants by differential evolution","authors":"P. Tomar, M. Pant","doi":"10.1109/SIS.2013.6615189","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615189","url":null,"abstract":"Since last few decades differential evolution algorithm (DE) has been successfully applied for solving many real life optimization problems. In this paper DE is applied to identifying the location of noisy sources in a multi noise plants. A trail noise technique is used to obtain the variation between trial sound pressure level (SPL) and exact SPL at monitoring points and then DE is employed in conjunction with the method of minimized variation square in seeking for the best locations and sound power level (SWLs). The results reveal that the significant locations and SWLs of noises can be precisely identified by DE.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127530591","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}
Pub Date : 2013-04-16DOI: 10.1109/SIS.2013.6615174
Shi Cheng, Yuhui Shi, Quande Qin, T. Ting
In this paper, the nearest neighbor method on Chinese text categorization is formulated as an optimization problem. The particle swarm optimization is utilized to optimize a nearest neighbor classifier to solve the Chinese text categorization problem. The parameter k was first optimized to obtain the minimum error, then the categorization problem is formulated as a discrete, constrained, and single objective optimization problem. Each dimension of solution vector is dependent on each other in the solution space. The parameter k and the number of labeled examples for each class are optimized together to reach the minimum categorization error. In the experiment, with the utilization of particle swarm optimization, the performance of a nearest neighbor algorithm can be improved, and the algorithm can obtain the minimum categorization error rate.
{"title":"Particle swarm optimization based nearest neighbor algorithm on Chinese text categorization","authors":"Shi Cheng, Yuhui Shi, Quande Qin, T. Ting","doi":"10.1109/SIS.2013.6615174","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615174","url":null,"abstract":"In this paper, the nearest neighbor method on Chinese text categorization is formulated as an optimization problem. The particle swarm optimization is utilized to optimize a nearest neighbor classifier to solve the Chinese text categorization problem. The parameter k was first optimized to obtain the minimum error, then the categorization problem is formulated as a discrete, constrained, and single objective optimization problem. Each dimension of solution vector is dependent on each other in the solution space. The parameter k and the number of labeled examples for each class are optimized together to reach the minimum categorization error. In the experiment, with the utilization of particle swarm optimization, the performance of a nearest neighbor algorithm can be improved, and the algorithm can obtain the minimum categorization error rate.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130073518","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}
Pub Date : 2013-04-16DOI: 10.1109/SIS.2013.6615156
N. E. Toklu, R. Montemanni, L. Gambardella
In this study, we consider a capacitated vehicle routing problem where the objective function is to minimize the total travel cost.We also consider that the travel costs between the locations are subject to uncertainty, therefore they are expressed as intervals, rather than fixed numbers. The motivation of this study is to solve this problem by using a metaheuristic approach. We base our approach on a variant of ant colony optimization metaheuristic, called ant colony system, which was originally implemented for solving the deterministic version of the problem (i.e. the classical version of the problem without the uncertainty), previously reported in the literature. We modify the algorithm to incorporate a robust optimization methodology, so that the uncertainty on traveling costs can be handled.
{"title":"An ant colony system for the capacitated vehicle routing problem with uncertain travel costs","authors":"N. E. Toklu, R. Montemanni, L. Gambardella","doi":"10.1109/SIS.2013.6615156","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615156","url":null,"abstract":"In this study, we consider a capacitated vehicle routing problem where the objective function is to minimize the total travel cost.We also consider that the travel costs between the locations are subject to uncertainty, therefore they are expressed as intervals, rather than fixed numbers. The motivation of this study is to solve this problem by using a metaheuristic approach. We base our approach on a variant of ant colony optimization metaheuristic, called ant colony system, which was originally implemented for solving the deterministic version of the problem (i.e. the classical version of the problem without the uncertainty), previously reported in the literature. We modify the algorithm to incorporate a robust optimization methodology, so that the uncertainty on traveling costs can be handled.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128522831","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}
Pub Date : 2013-04-16DOI: 10.1109/SIS.2013.6615173
Wiehann Matthysen, A. Engelbrecht, K. Malan
The vector evaluated particle swarm optimization (VEPSO) algorithm is a cooperative, multi-swarm algorithm. Each sub-swarm optimizes only a single objective of a multi-objective problem (MOP), and implements a knowledge transfer strategy (KTS) to share optimal positions of the different objectives among the sub-swarms, guiding the particles to different regions of the Pareto front. This paper shows that the stagnation problem that occurs in VEPSO can be addressed by using a different KTS. A comparison is made between the ring-based and random knowledge transfer strategies. Experimental results show that the random knowledge transfer strategy suffers less from stagnation than the ring-based KTS, making it the preferred KTS to use.
{"title":"Analysis of stagnation behavior of vector evaluated particle swarm optimization","authors":"Wiehann Matthysen, A. Engelbrecht, K. Malan","doi":"10.1109/SIS.2013.6615173","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615173","url":null,"abstract":"The vector evaluated particle swarm optimization (VEPSO) algorithm is a cooperative, multi-swarm algorithm. Each sub-swarm optimizes only a single objective of a multi-objective problem (MOP), and implements a knowledge transfer strategy (KTS) to share optimal positions of the different objectives among the sub-swarms, guiding the particles to different regions of the Pareto front. This paper shows that the stagnation problem that occurs in VEPSO can be addressed by using a different KTS. A comparison is made between the ring-based and random knowledge transfer strategies. Experimental results show that the random knowledge transfer strategy suffers less from stagnation than the ring-based KTS, making it the preferred KTS to use.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114157356","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}
Pub Date : 2013-04-16DOI: 10.1109/SIS.2013.6615164
L. Tan, Jifeng Sun
Specific to the difficulty of optimization on complex multimodal problems, this paper proposes a spring oscillator model used for particle swarm optimizer algorithm (SOMPSO). In SOMPSO, the particles that trapped in the local optima in some dimensions and certain individual extreme points whose corresponding dimensions' positions are the farthest from them, will constitute the vibrators and the equilibrium points of several spring oscillator models (SOM) respectively. Velocities and positions of particles will be updated dynamically referred to the physical principle of SOM. This SOM enlarges the search space of particles to increase the diversity of the swarm. The experiment results show that, SOMPSO algorithm has good performance when compared with other four variants of the particle swarm optimizer (PSO) on the optimization of the multimodal composition functions.
{"title":"A spring oscillator model used for particle swarm optimizer","authors":"L. Tan, Jifeng Sun","doi":"10.1109/SIS.2013.6615164","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615164","url":null,"abstract":"Specific to the difficulty of optimization on complex multimodal problems, this paper proposes a spring oscillator model used for particle swarm optimizer algorithm (SOMPSO). In SOMPSO, the particles that trapped in the local optima in some dimensions and certain individual extreme points whose corresponding dimensions' positions are the farthest from them, will constitute the vibrators and the equilibrium points of several spring oscillator models (SOM) respectively. Velocities and positions of particles will be updated dynamically referred to the physical principle of SOM. This SOM enlarges the search space of particles to increase the diversity of the swarm. The experiment results show that, SOMPSO algorithm has good performance when compared with other four variants of the particle swarm optimizer (PSO) on the optimization of the multimodal composition functions.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131978741","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}
Pub Date : 2013-04-16DOI: 10.1109/SIS.2013.6615188
Nathan Fortier, John W. Sheppard, K. Pillai
Abductive inference in Bayesian networks, is the problem of finding the most likely joint assignment to all non-evidence variables in the network. Such an assignment is called the most probable explanation (MPE). A novel swarm-based algorithm is proposed that finds the k-MPE of a Bayesian network. Our approach is an overlapping swarm intelligence algorithm in which a particle swarm is assigned to each node in the network. Each swarm searches for value assignments for its node's Markov blanket. Swarms that have overlapping value assignments compete to determine which assignment will be used in the final solution. In this paper we compare our algorithm to several other local search algorithms and show that our approach outperforms the competing methods in its ability to find the k-MPE.
{"title":"Bayesian abductive inference using overlapping swarm intelligence","authors":"Nathan Fortier, John W. Sheppard, K. Pillai","doi":"10.1109/SIS.2013.6615188","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615188","url":null,"abstract":"Abductive inference in Bayesian networks, is the problem of finding the most likely joint assignment to all non-evidence variables in the network. Such an assignment is called the most probable explanation (MPE). A novel swarm-based algorithm is proposed that finds the k-MPE of a Bayesian network. Our approach is an overlapping swarm intelligence algorithm in which a particle swarm is assigned to each node in the network. Each swarm searches for value assignments for its node's Markov blanket. Swarms that have overlapping value assignments compete to determine which assignment will be used in the final solution. In this paper we compare our algorithm to several other local search algorithms and show that our approach outperforms the competing methods in its ability to find the k-MPE.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122350643","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}
Pub Date : 2013-04-16DOI: 10.1109/SIS.2013.6615160
Martin Clauss, Matthias Bernt, M. Middendorf
Ant Colony Optimization (ACO) has been successfully applied to many combinatorial optimization problems. In this work we propose a new solution construction scheme for ACO. This scheme uses the common intervals of the current iteration's best solutions to guide the ants during solution construction. Firstly, we compared the performance of ACO and the proposed algorithm Common Interval ACO (CIACO). Secondly, we conducted an in-depth study for the CIACO algorithm to investigate the influence of the common interval guidance. For both experiments a large parameter space was used. The results show, that common intervals can be used to improve the solution quality in comparison to the standard ACO algorithm.
{"title":"A common interval guided ACO algorithm for permutation problems","authors":"Martin Clauss, Matthias Bernt, M. Middendorf","doi":"10.1109/SIS.2013.6615160","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615160","url":null,"abstract":"Ant Colony Optimization (ACO) has been successfully applied to many combinatorial optimization problems. In this work we propose a new solution construction scheme for ACO. This scheme uses the common intervals of the current iteration's best solutions to guide the ants during solution construction. Firstly, we compared the performance of ACO and the proposed algorithm Common Interval ACO (CIACO). Secondly, we conducted an in-depth study for the CIACO algorithm to investigate the influence of the common interval guidance. For both experiments a large parameter space was used. The results show, that common intervals can be used to improve the solution quality in comparison to the standard ACO algorithm.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124225520","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：