2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)最新文献

Boosting Model of Attention Network Task 注意网络任务促进模型

2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2020-09-01 DOI: 10.1109/CSIEC49655.2020.9237299

Azadeh Haratiannezhadi, S. Setayeshi, J. Hatami

{"title":"Boosting Model of Attention Network Task","authors":"Azadeh Haratiannezhadi, S. Setayeshi, J. Hatami","doi":"10.1109/CSIEC49655.2020.9237299","DOIUrl":"https://doi.org/10.1109/CSIEC49655.2020.9237299","url":null,"abstract":"Human attention is a limited resource that makes it valuable. Moreover, it is the first step in human brain cognition. Multiple factors affect their performance. The cognitive model of attention helps to understand the complexity of its role and involved factors. This study aimed at developing a novel machine-learning system of human care by different psychological tasks and collected information, which able to predict attention scores. A web-based system is developed for multiple cognitive tasks to gather information. The proposed model evaluates the human attention performance level by the Attentional Network Test (ANT). Data were collected from 62 participants. Decision tree, AdaBoost, and Gradient boosting models were developed, and the hyperparameters model was tuned. The model selection is based on finding performance. The performance was analyzed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). AdaBoost regression shows the best performance with MAE: 0.69 and RMSE: 3.9198 in comparison with other models. The AdaBoost model ensures the rapid and accurate estimation performance of attention.","PeriodicalId":373376,"journal":{"name":"2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125408419","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: 0

Discrete Genetic Algorithm for Solving Task Allocation of Multi-robot Systems 求解多机器人系统任务分配的离散遗传算法

2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2020-09-01 DOI: 10.1109/CSIEC49655.2020.9237316

M. Soleimanpour-moghadam, H. Nezamabadi-pour

引用次数: 2

Design of Self Tuning PID Controller Based on Competitional PSO 基于竞争粒子群算法的自整定PID控制器设计

2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2020-09-01 DOI: 10.1109/CSIEC49655.2020.9237318

Seyed Mohamad Ali Tousi, Abbas Mostafanasab, M. Teshnehlab

{"title":"Design of Self Tuning PID Controller Based on Competitional PSO","authors":"Seyed Mohamad Ali Tousi, Abbas Mostafanasab, M. Teshnehlab","doi":"10.1109/CSIEC49655.2020.9237318","DOIUrl":"https://doi.org/10.1109/CSIEC49655.2020.9237318","url":null,"abstract":"In this work, a new particle swarm optimization (PSO)-based optimization algorithm, and the idea of a running match is introduced and employed in a non-linear system PID controller design. This algorithm aims to modify the formula of velocity calculating of the general PSO method to increase the diversity of the searching process. In this process of designing an optimal PID controller for a non-linear system, the three gains of the PID controller form a particle, which is a parameter vector and will be updated iteratively. Many of those particles then form a population. To reach the PID gains which are optimum, using modified velocity updating formula and position updating formula, the position of all particles of the population will be moved into the optimization direction. In the meanwhile, an objective function may be minimized as the performance of the controller get improved. To corroborate the controller functioning of this method, a non-linear system known as inverted pendulum will be controlled by the designed PID controller. The results confirm that the new method can show excellent performance in the non-linear PID controller design task.","PeriodicalId":373376,"journal":{"name":"2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114433082","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

2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) 2020第四届群体智能与进化计算会议(CSIEC)

2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2020-09-01 DOI: 10.1109/csiec49655.2020.9237292

引用次数: 0

International Fuzzy Systems Association 国际模糊系统协会

2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2020-09-01 DOI: 10.1109/csiec49655.2020.9237301

引用次数: 0

A Multi-agent PSO Algorithm to Determine the Membership percentage of Each Node in Overlapping Community in Social Networks 一种确定社会网络重叠社区中各节点成员百分比的多智能体粒子群算法

2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2020-09-01 DOI: 10.1109/CSIEC49655.2020.9237308

Mohammad Akafan, B. Minaei-Bidgoli, A. Bagheri

{"title":"A Multi-agent PSO Algorithm to Determine the Membership percentage of Each Node in Overlapping Community in Social Networks","authors":"Mohammad Akafan, B. Minaei-Bidgoli, A. Bagheri","doi":"10.1109/CSIEC49655.2020.9237308","DOIUrl":"https://doi.org/10.1109/CSIEC49655.2020.9237308","url":null,"abstract":"The social network embodies real-life social graphs. Detecting communities or clusters from these graphs is an ill-posed difficult task. The communities are identified using the adjacent nodes that have shared edges and similar features. One of the principal concerns after community detection is to identify the active nodes in a network who attend several communities. So, finding communities that are overlapped in a social network is an important topic in social network analysis. This paper introduces a multiagent algorithm particle swarm optimization. The proposed algorithm detects overlapping as well as non-overlapping communities. Following the detection of overlapping communities, this algorithm can identify those nodes leading to overlapping and ultimately it can determine the affiliation ratio of each node to the given community. The algorithm uses a special type of coding to identify the number of communities without any prior knowledge. In my method, the modularity measure is used as a fitness function to enhance particle swarm. Several tests show that the proposed algorithm which is called Fuzzy Overlapping Community Detection based on Multi-Agent Particle Swarm Optimization (FOCDMAPSO), is superior compared with four other competitive algorithms. This algorithm is implemented over six well-known datasets in the literature. Our algorithm is accomplished in detecting nodes in overlapping communities with high accurateness. Furthermore, the proposed algorithm can detect the affiliation percentage of each node that leads to overlapping communities which is a novel feature in the area of the social network.","PeriodicalId":373376,"journal":{"name":"2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"41 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120866863","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: 0

Design of Large-Scale Structures by an enhanced Metaheuristic utilizing Opposition-based Learning 基于对立学习的增强元启发式大规模结构设计

2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2020-09-01 DOI: 10.1109/CSIEC49655.2020.9237319

M. Shahrouzi, A. Salehi

引用次数: 1

Modification of Permutation Method in Multi-Criteria Decision Making Using PSO-SA PSO-SA多准则决策中置换方法的改进

2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2020-09-01 DOI: 10.1109/CSIEC49655.2020.9237309

Maryam Tehrani Zadeh, F. Rad, M. Yousefi, H. Hessami, Matin Rahmatian

{"title":"Modification of Permutation Method in Multi-Criteria Decision Making Using PSO-SA","authors":"Maryam Tehrani Zadeh, F. Rad, M. Yousefi, H. Hessami, Matin Rahmatian","doi":"10.1109/CSIEC49655.2020.9237309","DOIUrl":"https://doi.org/10.1109/CSIEC49655.2020.9237309","url":null,"abstract":"Permutation is one of the widely used methods in Multi-Criteria Decision Making (MCDM) for prioritizing alternatives. Making the best decision will become more challenging by growing the number of criteria. High computational cost is the main disadvantage of this method. In this paper, an innovative algorithm is applied for obtaining an optimized solution for the permutation method. In this research, a combined algorithm of particle swarm optimization (PSO) and simulated annealing (SA) is proposed and implemented for reducing the high calculation costs. This combined algorithm possesses the higher convergence properties of PSO in finding the best answer as well as holds the SA feature of avoiding from facing local minimum. This algorithm investigated two sets of numeric data. The results verify its highest efficiency in detecting an optimized sequence of prior alternatives choices at a shorter time. This work proved that the suggested methodology could be employed with high confidence for common permutation problems in case of the presence of many alternatives.","PeriodicalId":373376,"journal":{"name":"2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114494678","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: 0

Simplified Power Allocation method for AF-Based Multi-Antennas Multiple Relays Networks 基于af的多天线多中继网络的简化功率分配方法

2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2020-09-01 DOI: 10.1109/CSIEC49655.2020.9237311

M. Soleimanpour-moghadam, Mohammad Askarizade, S. Talebi

引用次数: 1

Biosensors Localization for Swarm Medical Delivery 群体医疗配送的生物传感器定位

2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2020-09-01 DOI: 10.1109/CSIEC49655.2020.9237305

Nasibeh Rady Raz, M. Akbarzadeh-T., S. Setayeshi

引用次数: 1