2018 IEEE Congress on Evolutionary Computation (CEC)最新文献_第9页

NGA-LP: A Robust and Improved Genetic Algorithm to Detect Communities in Directed Networks NGA-LP:一种鲁棒改进的有向网络群体检测遗传算法

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477955

Rodrigo Francisquini, M. C. Nascimento, M. Basgalupp

{"title":"NGA-LP: A Robust and Improved Genetic Algorithm to Detect Communities in Directed Networks","authors":"Rodrigo Francisquini, M. C. Nascimento, M. Basgalupp","doi":"10.1109/CEC.2018.8477955","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477955","url":null,"abstract":"Understanding the community structure of realworld networks is an important task to predict the dynamics of many complex systems. To this end, several optimization methods were developed to maximize the widely studied measure known as Modularity. Most of these methods use global information and, therefore, are computationally expensive to process large-scale networks. This paper proposes a genetic algorithm to detect communities in directed networks, named NGA-LP, that contains local genetic operators designed to have low computational cost. The primary advantage of NGA-LP is the local representation, where the vertices store the information of the individuals. This representation makes possible the use of local genetic operators which do not require global information. Moreover, NGA-LP combines a pair of crossover operators that are automatically chosen according to the characteristics of the network, guided by the quality of the solution. The goal of combining different crossover operators is to ensure the robustness and capability of handling with different networks in an adaptive fashion. In the computational tests carried out in this paper, the introduced algorithm achieved excellent results and outperformed the other benchmark algorithms, even for undirected networks.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129425685","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

Increasing Boosting Effectiveness with Estimation of Distribution Algorithms 利用分布估计算法提高Boosting的有效性

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477959

Henry E. L. Cagnini, M. Basgalupp, Rodrigo C. Barros

引用次数: 1

Evolving Dungeon Maps With Locked Door Missions 带有锁门任务的地下城地图

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477718

L. T. Pereira, Paulo V. S. Prado, C. Toledo

引用次数: 5

Particle-Swarm Optimization Control of Active-Power Filter for Harmonic Mitigation of Hybrid Electric-Unbalanced Traction-System 混合电力-不平衡牵引系统谐波抑制有源滤波器的粒子群优化控制

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477653

G. El-Saady, E. A. Ebrahim, H. Abdul‐Ghaffar, Y. Mohamed, A. El-Sayed

{"title":"Particle-Swarm Optimization Control of Active-Power Filter for Harmonic Mitigation of Hybrid Electric-Unbalanced Traction-System","authors":"G. El-Saady, E. A. Ebrahim, H. Abdul‐Ghaffar, Y. Mohamed, A. El-Sayed","doi":"10.1109/CEC.2018.8477653","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477653","url":null,"abstract":"Using of un-symmetrical solid-state control loads leads to huge amount of harmonics fed to the balanced three-phase systems. Dependently, the harmonics cause many problems with un-balanced and distorted supply waveforms. In this paper, three-phase 3-wire balanced source energizes a hybrid electric-unbalanced traction system that fed from two independent rectified single-phase supplies. Both trains are fed from a balanced three-phase 3-wire supply via V/V transformer. A proposed shunt active power filter (SAPF) is introduced to mitigate most of the harmonics and rebalance the supply. Intelligent PI-controller is used with self-tuning to regulate the voltage of the DC-link for the proposed filter. This smart controller and its parameters is tuned and optimized by using intelligent technique with the particle swarm optimization (PSO). PSO is one of the most prevailing methods to solve the non-linear optimization problems. Also, the system includes a hysteresis current regulator for generating the IGBT-triggering pulses of the inverter. The proposed system is designed, simulated and implemented with the help of the Matlab/Simulink software package. The test results are obtained and compared with and without optimization technique. With optimized algorithm, the total harmonic distortion (THD) is minimized; restoring the balance of the system, and the power factor of the supply is improved to unity. However, those results demonstrate that the proposed system is robust to non-linearity and more suitable for harmonic mitigation in electric traction.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130938470","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: 2

Immune-Inspired Optimization with Autocorrentropy Function for Blind Inversion of Wiener Systems Wiener系统盲反演的自相关函数免疫激励优化

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477724

S. A. Fernandez, D. Fantinato, J. Filho, R. Attux, Daniel G. Silva

引用次数: 2

Managing Quality of Service Through Intelligent Scheduling in Heterogeneous Wireless Communications Networks 基于智能调度的异构无线通信网络服务质量管理

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477871

David Lynch, David Fagan, S. Kucera, H. Claussen, M. O’Neill

{"title":"Managing Quality of Service Through Intelligent Scheduling in Heterogeneous Wireless Communications Networks","authors":"David Lynch, David Fagan, S. Kucera, H. Claussen, M. O’Neill","doi":"10.1109/CEC.2018.8477871","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477871","url":null,"abstract":"Small Cells are being deployed alongside pre-existing Macro Cells in order to satisfy demand during the current era of exponential growth in mobile traffic. Heterogeneous networks are economical because both cell tiers share the same scarce and expensive spectrum. However, customers at cell edges experience severe cross-tier interference in channel sharing Het-Nets, resulting in poor service quality. Techniques for improving fairness globally have been developed in previous works. In this paper, a novel method for service differentiation at the level of individual customers is proposed. The proposed algorithm redistributes spectrum on a millisecond timescale, so that premium customers experience minimum downlink rates exceeding a target threshold. System level simulations indicate that downlink rate targets of at least 1 [Mbps] are always satisfied under the proposed scheme. By contrast, naive scheduling achieves the 1 [Mbps] target only 83% of the time. Quality of service can be improved for premium customers without significantly impacting global fairness metrics. Flexible service differentiation will be key to effectively monetizing the next generation of 5G wireless communications networks.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"410 27","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120892151","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: 2

A Many-Objective Configuration Optimization for Building Energy Management 面向建筑能源管理的多目标配置优化

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477966

Tobias Rodemann

{"title":"A Many-Objective Configuration Optimization for Building Energy Management","authors":"Tobias Rodemann","doi":"10.1109/CEC.2018.8477966","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477966","url":null,"abstract":"For a commercial building or campus, the management of local energy production, storage, and consumption, promises substantial gains in efficiency and reduced costs and emissions. When facility managers are planning updates to an existing building complex, they face a variety of options for investment. This work targets to provide support for this investment decision by performing a many-objective optimization (MAO) of the system configuration considering initial investment cost, running costs, CO2 emissions, and system resilience. In our specific example the potential investment covers a photo voltaic (PV) system, a stationary battery, and a heat storage. We also consider potential changes to the operation of an existing co-generator for heat and power (CHP), by optimizing controller parameters. The proposed system is simulated using a Modelica-based software environment. In this work we show the results of our configuration optimization using the well-known NSGA-III algorithm and also consider the problem of variable run-times of the simulator on the optimization process especially for a parallel execution of fitness evaluations on a computing cluster.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126518131","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: 6

Decentralized Multi-Robot Mission Planning Using Evolutionary Computation 基于进化计算的分散多机器人任务规划

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477839

Sugandha Dumka, Smiti Maheshwari, R. Kala

{"title":"Decentralized Multi-Robot Mission Planning Using Evolutionary Computation","authors":"Sugandha Dumka, Smiti Maheshwari, R. Kala","doi":"10.1109/CEC.2018.8477839","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477839","url":null,"abstract":"The classic problem of robot motion planning asks the robot to go from $A$ to $B$ avoiding obstacles. Missions are challenging problems asking the robot to visit a set of sites to accomplish a mission. The mission planning problems are largely studied as a Travelling Salesman Problem involving combinatorial optimization. In this paper the problem is generalized to any Boolean expression, giving more expressing powers to specify missions like “Visit any one of three coffee machines” or “Visit any two of three instructors”, along with other mission sites to be mandatorily visited. The problem is solved using multiple robots in a decentralized manner. The Boolean expression is simplified into an ‘OR of AND’ format, which gives the flexibility to solve all the AND components and to select the minimum cost solution among them. Each of the AND components is a reduced multi-robot Travelling Salesman Problem solved by using k-medoids clustering and evolutionary computation. The results obtained by this approach are compared with the centralized algorithm and a master slave algorithm which uses a randomized algorithm for robot assignment, and for every such assignment the corresponding optimization problem of visiting the sites is solved for. The comparison depicts that as the problem size and the number of robots increase, the decentralized approach outperforms the rest enormously. The results are also tested on a Pioneer LX robot working in an office environment to carry dummy missions of everyday needs.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127750660","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: 3

Mobile Robot Path Planning by Improved Brain Storm Optimization Algorithm 基于改进头脑风暴优化算法的移动机器人路径规划

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477928

Eva Tuba, I. Strumberger, Dejan Zivkovic, N. Bačanin, M. Tuba

引用次数: 38

SHADE with Iterative Local Search for Large-Scale Global Optimization 基于迭代局部搜索的大规模全局优化

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477755

D. Molina, A. Latorre, F. Herrera

{"title":"SHADE with Iterative Local Search for Large-Scale Global Optimization","authors":"D. Molina, A. Latorre, F. Herrera","doi":"10.1109/CEC.2018.8477755","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477755","url":null,"abstract":"Global optimization is a very important topic in research due to its wide applications in many real-world problems in science and engineering. Among optimization problems, dimensionality is one of the most crucial issues that increases the difficulty of the optimization process. Thus, Large-Scale Global Optimization, optimization with a great number of variables, arises as a field that is getting an increasing interest. In this paper, we propose a new hybrid algorithm especially designed to tackle this type of optimization problems. The proposal combines, in a iterative way, a modern Differential Evolution algorithm with one local search method chosen from a set of different search methods. The selection of the local search method is dynamic and takes into account the improvement obtained by each of them in the previous intensification phase, to identify the most adequate in each case for the problem. Experiments are carried out using the CEC'2013 Large-Scale Global Optimization benchmark, and the proposal is compared with other state-of-the-art algorithms, showing that the synergy among the different components of our proposal leads to better and more robust results than more complex algorithms. In particular, it improves the results of the current winner of previous Large-Scale Global Optimization competitions, Multiple Offspring Sampling, MOS, obtaining very good results, especially in the most difficult problems.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"156 38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130478683","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: 60