2014 IEEE Symposium on Swarm Intelligence最新文献_第2页

A social-spider optimization approach for support vector machines parameters tuning 支持向量机参数调优的社会蜘蛛优化方法

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011769

D. R. Pereira, M. A. Pazoti, Luís A. M. Pereira, J. Papa

引用次数: 8

Non-dominated sorting cuckoo search for multiobjective optimization 多目标优化的非支配排序布谷鸟搜索

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011772

Xingshi He, Na Li, Xin-She Yang

引用次数: 12

Balancing search direction in cultural algorithm for enhanced global numerical optimization 平衡搜索方向的文化算法增强全局数值优化

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011814

Mostafa Z. Ali, Noor H. Awad, R. Reynolds

{"title":"Balancing search direction in cultural algorithm for enhanced global numerical optimization","authors":"Mostafa Z. Ali, Noor H. Awad, R. Reynolds","doi":"10.1109/SIS.2014.7011814","DOIUrl":"https://doi.org/10.1109/SIS.2014.7011814","url":null,"abstract":"Many meta-heuristics methods are applied to guide the exploration and exploitation of the search space for large scale optimization problems. These problems have attracted much attention from researchers who proposed developed a variety of techniques for locating the optimal solutions. Cultural Algorithm has been recently adopted to solve global numerical optimization problems. In this paper, a modified version of Cultural Algorithm (CA) that uses four knowledge sources in order to incorporate the information obtained from the objective function as well as constraint violation into knowledge structure in the belief space is proposed. The archived knowledge in the proposed approach will be used to enhance the way the belief space influences future generations of problem solvers. The first step is to use the four knowledge sources to guide the direction of the search to more promising solutions. The search is balanced between exploration and exploitation by dynamically adjusting the number of evaluations available for each type of knowledge source based on whether is primarily exploratory or exploitative. The second step selects one local search method to find the nearest solutions to those proposed by the knowledge sources. The proposed work is employed to solve seven global optimization problems in 50 and 100 dimensions, and an engineering application problem. Simulation results show how the approach speeds up the convergence process with very competitive results on such complex benchmarks when compared to other state-of-the-art algorithms.","PeriodicalId":380286,"journal":{"name":"2014 IEEE Symposium on Swarm Intelligence","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121710259","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: 4

A planner for autonomous risk-sensitive coverage (PARCov) by a team of unmanned aerial vehicles 由无人驾驶飞行器组成的自主风险敏感覆盖(PARCov)规划器

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011807

Alex Wallar, E. Plaku, D. Sofge

引用次数: 11

Hybrid cooperative co-evolution for large scale optimization 大规模优化的混合协同进化

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011815

Mohammed El-Abd

引用次数: 5

An artificial bee colony algorithm for minimum weight dominating set 最小权值支配集的人工蜂群算法

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011811

C. G. Nitash, Alok Singh

引用次数: 12

Evolving novel algorithm based on intellectual behavior of Wild dog group as optimizer 基于野狗群体智能行为优化的进化新算法

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011768

A. S. Buttar, A. K. Goel, Shakti Kumar

引用次数: 2

Fitness function evaluations: A fair stopping condition? 适应度函数评价:一个公平的停止条件?

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011793

A. Engelbrecht

引用次数: 53

A computational basis for the presence of sub-cultures in cultural algoithms 文化算法中亚文化存在的计算基础

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011813

Yousof A. Gawasmeh, R. Reynolds

{"title":"A computational basis for the presence of sub-cultures in cultural algoithms","authors":"Yousof A. Gawasmeh, R. Reynolds","doi":"10.1109/SIS.2014.7011813","DOIUrl":"https://doi.org/10.1109/SIS.2014.7011813","url":null,"abstract":"Cultural Algorithms are computational models of social evolution based upon principle of Cultural Evolution. A Cultural Algorithm consists of a Belief Space consisting of a network of active and passive knowledge sources and a Population Space of agents. The agents are connected via a social fabric over which information used in agent problem solving as passed. The knowledge sources in the Belief Space compete with each other in order to influence the decision making of agents in the Population Space. Likewise, the problem solving experiences of agents in the Population Space are sent back to the Belief Space and used to update the knowledge sources there. It is a dual inheritance system in which both the Population and Belief spaces evolve in parallel. In this paper we investigate why sub-cultures can emerge in the Population Space in response to the complexity of the problems presented to a Cultural System. This system is compared with other evolutionary approaches relative to a variety of benchmark problem of varying complexity. We show that the presence of sub-cultures can provide computational advantages in problem landscape that are generated by multiple independent processes. These advantages can increase problem solving efficiency along with the ability to dampen the impact of increase in problem complexity.","PeriodicalId":380286,"journal":{"name":"2014 IEEE Symposium on Swarm Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123657664","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

A bio-inspired approach to task assignment of multi-robots 多机器人任务分配的仿生方法

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011778

Xin Yi, Anmin Zhu, Zhong Ming

引用次数: 3