2015 IEEE Congress on Evolutionary Computation (CEC)最新文献_第6页

Efficient computation of two-dimensional solution sets maximizing the epsilon-indicator 有效的二维解集计算最大化的ε -指标

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256995

K. Bringmann, T. Friedrich, Patrick Klitzke

引用次数: 7

A novel boundary constraint-handling technique for constrained numerical optimization problems 一种新的约束数值优化问题边界约束处理技术

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257135

Efrén Juárez-Castillo, N. Pérez-Castro, E. Mezura-Montes

引用次数: 9

A novel block-shifting simulated annealing algorithm for the no-wait flowshop scheduling problem 无等待流水车间调度问题的一种新的移块模拟退火算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257232

Jianya Ding, Shiji Song, Rui Zhang, Siwei Zhou, Cheng Wu

引用次数: 13

Improved SRPSO algorithm for solving CEC 2015 computationally expensive numerical optimization problems 改进的SRPSO算法求解CEC 2015计算量大的数值优化问题

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257123

M. Tanweer, S. Sundaram, N. Sundararajan

{"title":"Improved SRPSO algorithm for solving CEC 2015 computationally expensive numerical optimization problems","authors":"M. Tanweer, S. Sundaram, N. Sundararajan","doi":"10.1109/CEC.2015.7257123","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257123","url":null,"abstract":"This paper presents an improved version of the recently proposed Self Regulating Particle Swarm Optimization (SRPSO) algorithm referred to as improved Self Regulating Particle Swarm Optimization (iSRPSO) algorithm. In the iSRPSO algorithm, the last two least performing particles are observed with different perception and they adopt a different learning strategy for velocity update. These particles get a directional update from the best particle and the next top three better performing particles for divergence of their search directions towards better solutions. This provides direction and momentum to these least performing particles and enhances their awareness of the search space. Performance of iSRPSO has been compared with SRPSO on a unimodal and a multimodal benchmark function from CEC2005 where a significant performance improvement closer to the optimum solution has been observed. Further, the performance of iSRPSO has been investigated using both the 10D and 30D CEC2015 bound constrained single-objective computationally expensive numerical optimization problems. The performance of iSRPSO on 10D problems have been compared with both the PSO and SRPSO algorithms where the solutions of iSRPSO are closer to the true optimum value compared to the other two algorithms.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123936633","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: 37

Renumber strategy enhanced particle swarm optimization for cloud computing resource scheduling 基于粒子群优化的云计算资源调度策略

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256982

Hai-Hao Li, Yu-Wen Fu, Zhi-hui Zhan, Jingjing Li

{"title":"Renumber strategy enhanced particle swarm optimization for cloud computing resource scheduling","authors":"Hai-Hao Li, Yu-Wen Fu, Zhi-hui Zhan, Jingjing Li","doi":"10.1109/CEC.2015.7256982","DOIUrl":"https://doi.org/10.1109/CEC.2015.7256982","url":null,"abstract":"Cloud computing offers unprecedented capacity to execute large-scale workflows in the “era of big data”. In 2014, a cost-minimization and deadline-constrained workflow scheduling (CMDCWS) model is firstly proposed by Rodriguez and Buyya, which is applicable for the business need of cloud computing that a workflow task should be finished by minimizing the execute cost within a deadline constraint. As scheduling cloud computing resources for workflow is an NP-hard problem, Rodriguez and Buyya proposed to use particle swarm optimization (PSO) to solve the CMDCWS problem. In traditional PSO for CMDCWS, each dimension in the particle position stands for each task and the value of the corresponding dimension stands for the index of the cloud resource that executes this task. However, this may have drawback because the value of each dimension does not relate to the resource characteristic but is only a meaningless index number. Therefore the learning behaviors among the particles do not make sense because learning from index number may not lead to better position. In this paper, we present a resource renumber strategy to encode the particle position and design a renumber PSO (RNPSO) for CMDCWS. In RNPSO, all the resources are re-ordered and re-numbered according to their computational ability, i.e., the cost per unit time. By this, the values of particle position can make sense and the positions difference between the well-performed and poorly-performed particles can guide poorly-performed particle to promising region. We conduct experiments on test cases with small, middle, and large scales to compare the performance of PSO and RNPSO. The results show that the resource renumber strategy is promising for enhancing the PSO performance.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129752759","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: 37

Generalisation and domain adaptation in GP with gradient descent for symbolic regression 符号回归梯度下降GP的概化与域自适应

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257017

Qi Chen, Bing Xue, Mengjie Zhang

{"title":"Generalisation and domain adaptation in GP with gradient descent for symbolic regression","authors":"Qi Chen, Bing Xue, Mengjie Zhang","doi":"10.1109/CEC.2015.7257017","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257017","url":null,"abstract":"Genetic programming (GP) has been widely applied to symbolic regression problems and achieved good success. Gradient descent has also been used in GP as a complementary search to the genetic beam search to further improve symbolic regression performance. However, most existing GP approaches with gradient descent (GPGD) to symbolic regression have only been tested on the “conventional” symbolic regression problems such as benchmark function approximations and engineering practical problems with a single (training) data set only and the effectiveness on unseen data sets in the same domain and in different domains has not been fully investigated. This paper designs a series of experiment objectives to investigate the effectiveness and efficiency of GPGD with various settings for a set of symbolic regression problems applied to unseen data in the same domain and adapted to other domains. The results suggest that the existing GPGD method applying gradient descent to all evolved program trees three times at every generation can perform very well on the training set itself, but cannot generalise well on the unseen data set in the same domain and cannot be adapted to unseen data in an extended domain. Applying gradient descent to the best program in the final generation of GP can also improve the performance over the standard GP method and can generalise well on unseen data for some of the tasks in the same domain, but perform poorly on the unseen data in an extended domain. Applying gradient descent to the top 20% programs in the population can generalise reasonably well on the unseen data in not only the same domain but also in an extended domain.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128596893","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: 38

An evolutionary algorithm for performance optimization at software architecture level 一种软件架构级性能优化的进化算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257147

Xin Du, Youcong Ni, Peng Ye, X. Yao, Leandro L. Minku, Ruliang Xiao

{"title":"An evolutionary algorithm for performance optimization at software architecture level","authors":"Xin Du, Youcong Ni, Peng Ye, X. Yao, Leandro L. Minku, Ruliang Xiao","doi":"10.1109/CEC.2015.7257147","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257147","url":null,"abstract":"Architecture-based software performance optimization can not only significantly save time but also reduce cost. A few rule-based performance optimization approaches at software architecture (SA) level have been proposed in recent years. However, in these approaches, the number of rules being used and the order of application of each rule are uncertain in the optimization process and these uncertainties have not been fully considered so far. As a result, the search space for performance improvement is limited, possibly excluding optimal solutions. Aiming to solve this problem, we propose an evolutionary algorithm for rule-based performance optimization at SA level named EA4PO. First, the rule-based software performance optimization at SA level is abstracted into a mathematical model called RPOM. RPOM can precisely characterize the mathematical relation between the usage of rules and the optimal solution in the performance improvement space. Then, a framework named RSEF is designed to support the execution of rule sequences. Based on RPOM and RSEF, EA4PO is proposed to find the optimal performance improvement solution. In EA4PO, an adaptive mutation operator is designed to guide the search direction by fully considering heuristic information of rule usage during the evolution. Finally, the effectiveness of EA4PO is validated by comparing EA4PO with a typical rule-based approach. The results show that EA4PO can explore a relatively larger space and get better solutions.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127215070","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: 10

Toward robustness against environmental change speed by Artificial Bee Colony algorithm based on local information sharing 基于局部信息共享的人工蜂群算法对环境变化速度的鲁棒性研究

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257055

R. Takano, Hiroyuki Sato, Tomohiro Harada, K. Takadama

引用次数: 3

Stochastic economic dispatch with solar farm integrated by Bacterial Swarm Algorithm 基于细菌群算法的太阳能发电场随机经济调度

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257314

Mengshi Li, T. Ji, Qinghua Wu, Peisong Wu

引用次数: 2

Comparative analysis of genetic based approach and Apriori algorithm for mining maximal frequent item sets 基于遗传的最大频繁项集挖掘方法与Apriori算法的比较分析

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256872

Mir Md. Jahangir Kabir, Shuxiang Xu, B. Kang, Zongyuan Zhao

{"title":"Comparative analysis of genetic based approach and Apriori algorithm for mining maximal frequent item sets","authors":"Mir Md. Jahangir Kabir, Shuxiang Xu, B. Kang, Zongyuan Zhao","doi":"10.1109/CEC.2015.7256872","DOIUrl":"https://doi.org/10.1109/CEC.2015.7256872","url":null,"abstract":"In the data mining research area, discovering frequent item sets is an important issue and key factor for mining association rules. For large datasets, a huge amount of frequent patterns are generated for a low support value, which is a major challenge in frequent pattern mining tasks. A Maximal frequent pattern mining task helps to resolve this problem since a maximal frequent pattern contains information about a large number of small frequent sub patterns. For this study we have developed a genetic based approach to find maximal frequent patterns using a user defined threshold value as a constraint. To optimize the search problems, a genetic algorithm is one of the best choices which mimics the natural selection procedure and considers global search mechanism which is good for searching solution especially when the search space is large. The use of evolutionary algorithm is also effective for undetermined solutions. Therefore, this approach uses a genetic algorithm to find maximal frequent item sets from different sorts of data sets. A low support value generates some large patterns which contain the information about huge amount of small frequent sub patterns that could be useful for mining association rules. We have applied this genetic based approach for different real data sets as well as synthetic data sets. The experimental results show that our proposed approach evaluates less nodes than the number of candidate item sets considered by Apriori algorithm, especially when the support value is set low.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129168980","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