2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)最新文献

A genetic approach in procedural content generation for platformer games level creation 平台游戏关卡创造中程序内容生成的遗传方法

2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2017-03-07 DOI: 10.1109/CSIEC.2017.7940160

Arman Balali Moghadam, M. Rafsanjani

引用次数: 11

Improving semantic web service discovery method based on QoS ontology 改进基于QoS本体的语义web服务发现方法

2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2017-03-07 DOI: 10.1109/CSIEC.2017.7940175

Pourya Farzi, R. Akbari, O. Bushehrian

引用次数: 8

Low delay digital IIR filter design using metaheuristic algorithms 采用元启发式算法设计低延迟数字IIR滤波器

2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2017-03-07 DOI: 10.1109/CSIEC.2017.7940159

Yaser Maghsoudi, M. Kamandar

{"title":"Low delay digital IIR filter design using metaheuristic algorithms","authors":"Yaser Maghsoudi, M. Kamandar","doi":"10.1109/CSIEC.2017.7940159","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940159","url":null,"abstract":"Digital IIR filter design by optimizing a fitness function with respect to coefficients of a filter with rational transfer function by meta-heuristic algorithms has been considered recently. Most researchers use a fitness function consisted of difference between magnitude response of desired filter and designed filter and the constraints such as linear phase, minimum phase and stability of designed filter. In this paper, a comprehensive fitness function for IIR digital filter design with 6 terms is proposed. A new term is added to fitness function to get a filter with low delay. Low delay filters are desirable for real time signal processing. This term is weighted partial energy of the impulse response of designed causal filter. Maximizing this term leads to concentration of energy of impulse response at its beginning, consequently a low delay filter. Low delay property leads to fast decaying of transient response and low delay between input and output of designed filter. Proposed fitness function also includes some terms to meet linear phase, minimum phase and stability constraints. Meta-heuristic optimization algorithms GA, GSA and PSO are used to optimize proposed fitness function. To evaluate efficiency of the proposed method, it will be used to design a low delay low pass filter and a low delay differentiator. Reported results show lower delay of designed filters by proposed method than designed ones by traditional methods.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128368207","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: 1

An energy-based clustering method for WSNs using artificial bee colony and genetic algorithm 基于人工蜂群和遗传算法的能量聚类方法

2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2017-03-07 DOI: 10.1109/CSIEC.2017.7940165

M. Zangeneh, M. Ghazvini

引用次数: 6

Proposing new method to improve gravitational fixed nearest neighbor algorithm for imbalanced data classification 提出了一种改进引力固定近邻算法的不平衡数据分类新方法

2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2017-03-07 DOI: 10.1109/CSIEC.2017.7940167

Bahareh Nikpour, Mahin Shabani, H. Nezamabadi-pour

{"title":"Proposing new method to improve gravitational fixed nearest neighbor algorithm for imbalanced data classification","authors":"Bahareh Nikpour, Mahin Shabani, H. Nezamabadi-pour","doi":"10.1109/CSIEC.2017.7940167","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940167","url":null,"abstract":"Classification of imbalanced data sets is one of the basic challenges in the field of machine learning and data mining. There have been many proposed methods for classification of such data sets up to now. In algorithmic level methods, new algorithms are created which are adapted to the nature of imbalanced data sets. Gravitational fixed radius nearest neighbor algorithm (GFRNN) is an algorithmic level method developed with the aim of enhancing k nearest neighbor classifier to acquire the ability of dealing with imbalanced data sets. This algorithm, utilizes the sum of gravitational forces on a query sample from its nearest neighbors in a fixed radius to determine its label. Simplicity and no need for parameter setting during the run of algorithm are the main advantages of this method. In this paper, a method is proposed for improving the performance of GFRNN algorithm in which mass assigning of each training sample is done based on the sum of gravitational forces from other training samples on it. The results obtained from applying the proposed method on 10 data sets prove the superiority of it compared with 5 other algorithms.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132773258","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

A home energy management system using Gray Wolf Optimizer in smart grids 智能电网中使用灰狼优化器的家庭能源管理系统

2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2017-03-07 DOI: 10.1109/CSIEC.2017.7940176

Seyyed-Farshad Kazemi, S. Motamedi, Saeed Sharifian

引用次数: 14

MOCSA: A Multi-Objective Crow Search Algorithm for Multi-Objective optimization 面向多目标优化的多目标乌鸦搜索算法

2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2017-03-07 DOI: 10.1109/CSIEC.2017.7940171

H. Nobahari, Ariyan Bighashdel

引用次数: 24

Stud Multi-Verse Algorithm Stud多重宇宙算法

2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2017-03-07 DOI: 10.1109/CSIEC.2017.7940155

Mostafa Meshkat, Mohsen Parhizgar

引用次数: 8

Improved particle swarm optimization through orthogonal experimental design 通过正交实验设计改进粒子群优化

2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2017-03-07 DOI: 10.1109/CSIEC.2017.7940168

A. Ebrahimi, Vajiheh Dehdeleh, A. Boroumandnia, V. Seydi

引用次数: 4

Improved PSO-based feature construction algorithm using Feature Selection Methods 基于特征选择方法的改进pso特征构建算法

2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2017-03-07 DOI: 10.1109/CSIEC.2017.7940173

A. Mahanipour, H. Nezamabadi-pour

引用次数: 13