2017 IEEE Congress on Evolutionary Computation (CEC)最新文献_第4页

Full Model Selection issue in temporal data through evolutionary algorithms: A brief review 通过进化算法的时间数据的全模型选择问题:简要回顾

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969602

N. Pérez-Castro, Aldo Márquez-Grajales, H. Acosta-Mesa, E. Mezura-Montes

引用次数: 0

Evolutionary algorithms to optimize low-thrust trajectory design in spacecraft orbital precession mission 航天器轨道进动任务低推力轨迹优化设计的进化算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969517

A. Shirazi, Josu Ceberio, J. A. Lozano

{"title":"Evolutionary algorithms to optimize low-thrust trajectory design in spacecraft orbital precession mission","authors":"A. Shirazi, Josu Ceberio, J. A. Lozano","doi":"10.1109/CEC.2017.7969517","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969517","url":null,"abstract":"In space environment, perturbations make the spacecraft lose its predefined orbit in space. One of these undesirable changes is the in-plane rotation of space orbit, denominated as orbital precession. To overcome this problem, one option is to correct the orbit direction by employing low-thrust trajectories. However, in addition to the orbital perturbation acting on the spacecraft, a number of parameters related to the spacecraft and its propulsion system must be optimized. This article lays out the trajectory optimization of orbital precession missions using Evolutionary Algorithms (EAs). In this research, the dynamics of spacecraft in the presence of orbital perturbation is modeled. The optimization approach is employed based on the parametrization of the problem according to the space mission. Numerous space mission cases have been studied in low and middle Earth orbits, where various types of orbital perturbations are acted on spacecraft. Consequently, several EAs are employed to solve the optimization problem. Results demonstrate the practicality of different EAs, along with comparing their convergence rates. With a unique trajectory model, EAs prove to be an efficient, reliable and versatile optimization solution, capable of being implemented in conceptual and preliminary design of spacecraft for orbital precession missions.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122191574","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

An empirical study of multifactorial PSO and multifactorial DE 多因子PSO与多因子DE的实证研究

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969407

Liang Feng, Wei Zhou, Lei Zhou, Siwei Jiang, J. Zhong, B. Da, Zexuan Zhu, Yang Wang

{"title":"An empirical study of multifactorial PSO and multifactorial DE","authors":"Liang Feng, Wei Zhou, Lei Zhou, Siwei Jiang, J. Zhong, B. Da, Zexuan Zhu, Yang Wang","doi":"10.1109/CEC.2017.7969407","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969407","url":null,"abstract":"Recently, the notion of Multifactorial Optimization (MFO) has emerged as a promising approach for evolutionary multi-tasking by automatically exploiting the latent synergies between optimization problems, simply through solving them together in an unified representation space [1]. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge between them. In [1], the efficacy of MFO has been studied by a specific mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. Here we further explore the generality of MFO when diverse population based search mechanisms are employed. In particular, in this paper, we present the first attempt to conduct MFO with the popular particle swarm optimization and differential evolution search. Two specific multi-tasking paradigms, namely multifactorial particle swarm optimization (MFPSO) and multifactorial differential evolution (MFDE) are proposed. To evaluate the performance of MFPSO and MFDE, comprehensive empirical studies on 9 single objective MFO benchmark problems are provided.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132313439","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: 90

An evolutionary schema for mining skyline clusters of attributed graph data 一种挖掘属性图数据天际线簇的进化模式

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969559

Wajdi Dhifli, Noemie Oliveira Da Costa, M. Elati

{"title":"An evolutionary schema for mining skyline clusters of attributed graph data","authors":"Wajdi Dhifli, Noemie Oliveira Da Costa, M. Elati","doi":"10.1109/CEC.2017.7969559","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969559","url":null,"abstract":"Graph clustering is one of the most important research topics in graph mining and network analysis. With the abundance of data in many real-world applications, the graph nodes and edges could be annotated with multiple sets of attributes that could be derived from heterogeneous data sources. Considering these attributes during the graph clustering could help in generating graph clusters with balanced and cohesive intra-cluster structure and nodes having homogeneous properties. In this paper, we propose a genetic algorithm-based graph clustering approach for mining skyline clusters over large attributed graphs based on the dominance relationship. Each skyline solution is optimized with respect to multiple fitness functions simultaneously where each function is defined over the graph topology or over a particular set of attributes that are derived from multiple data sources. We experimentally evaluate our approach on a real-world large protein-protein interaction network of the human interactome enriched with large sets of heterogeneous cancer associated attributes. The obtained results show the efficiency of our approach and how integrating node attributes of multiple data sources allows to obtain a more robust graph clustering than by considering only the graph topology.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128593346","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

Solving dynamic vehicle routing problem via evolutionary search with learning capability 基于学习能力的进化搜索求解动态车辆路径问题

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969403

L. Zhou, L. Feng, Abhishek Gupta, Y. Ong, K. Liu, C. Chen, E. Sha, B. Yang, B. Yan

引用次数: 15

A novel quantum-behaved particle swarm optimization with random selection for large scale optimization 基于随机选择的量子粒子群优化算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969641

Wei Fang, Lingzhi Zhang, Jianhong Zhou, Xiaojun Wu, Jun Sun

{"title":"A novel quantum-behaved particle swarm optimization with random selection for large scale optimization","authors":"Wei Fang, Lingzhi Zhang, Jianhong Zhou, Xiaojun Wu, Jun Sun","doi":"10.1109/CEC.2017.7969641","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969641","url":null,"abstract":"Large scale optimization has become a well-recognised field in many science and engineering applications and a variety of metaheuristic algorithms adopting cooperative coevolution (CC) framework with problem decomposition have been applied to solve them. In this paper, a novel decomposition strategy termed as random selection is proposed. In random selection strategy, only a small part of decision variables are randomly selected to form a group for evolving at every iteration and the maximum number of randomly selected decision variables are limited by the parameter RSSCALE. By random selection, the randomly selected searching subspace is explored sufficiently in each iteration and the whole search space can be fully covered after several iterations. We evaluate the random selection strategy by combining quantum-behaved particle swarm optimization (RSQPSO) and a comparative study is carried out on a set of benchmark functions between RSQPSO and four state-of-the-art algorithms, which were specially designed for large scale optimization. The comparative results show that the proposed approach performs well for solving large scale optimization problems.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129733597","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

Preference incorporation to solve multi-objective mission planning of agile earth observation satellites 结合偏好求解敏捷对地观测卫星多目标任务规划

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969463

Longmei Li, Feng Yao, N. Jing, M. Emmerich

{"title":"Preference incorporation to solve multi-objective mission planning of agile earth observation satellites","authors":"Longmei Li, Feng Yao, N. Jing, M. Emmerich","doi":"10.1109/CEC.2017.7969463","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969463","url":null,"abstract":"This paper investigates earth observation scheduling of agile satellite constellation based on evolutionary multiobjective optimization (EMO). The mission planning of agile earth observation satellite (AEOS) is to select and specify the observation activities to acquire images on the earth surface. This should be done in accordance with operational constraints and in order to maximize certain objectives. In this paper three objectives are considered, i. e. profit, quality and timeliness. Preference-based EMO methods are introduced to generate solutions preferable for the decision maker, whose preference is expressed by a reference point. An improved algorithm based on R-NSGA-II, which is named CD-NSGA-II, is proposed and compared with other algorithms in various scheduling scenarios. Results show that the chosen preference modeling paradigm allows to focus search on the interesting part of the Pareto front. Moreover, the tested algorithms behave differently with the change of reference point and degree of conflicts, among them CD-NSGA-II has the best overall performance. Suggestions on applying these algorithms in practice are also given in this paper.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132427854","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: 11

Social Grammatical Evolution with imitation learning for real-valued function estimation 社会语法演化与实值函数估计的模仿学习

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969490

N. Le, M. O’Neill, David Fagan, A. Brabazon

引用次数: 1

EvoHyp - a Java toolkit for evolutionary algorithm hyper-heuristics EvoHyp -一个用于进化算法超启发式的Java工具包

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969636

N. Pillay, D. Beckedahl

{"title":"EvoHyp - a Java toolkit for evolutionary algorithm hyper-heuristics","authors":"N. Pillay, D. Beckedahl","doi":"10.1109/CEC.2017.7969636","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969636","url":null,"abstract":"Hyper-heuristics is an emergent technology that has proven to be effective at solving real-world problems. The two main categories of hyper-heuristics are selection and generation. Selection hyper-heuristics select existing low-level heuristics while generation hyper-heuristics create new heuristics. At the inception of the field single point searches were essentially employed by selection hyper-heuristics, however as the field progressed evolutionary algorithms are becoming more prominent. Evolutionary algorithms, namely, genetic programming, have chiefly been used for generation hyper-heuristics. Implementing evolutionary algorithm hyper-heuristics can be quite a time-consuming task which is daunting for first time researchers and practitioners who want to rather focus on the application domain the hyper-heuristic will be applied to which can be quite complex. This paper presents a Java toolkit for the implementation of evolutionary algorithm hyper-heuristics, namely, EvoHyp. EvoHyp includes libraries for a genetic algorithm selection hyper-heuristic (GenAlg), a genetic programming generation hyper-heuristic (GenProg), a distributed version of GenAlg (DistrGenAlg) and a distributed version of GenProg (DistrGenProg). The paper describes the libraries and illustrates how they can be used. The ultimate aim is to provide a toolkit which a non-expert in evolutionary algorithm hyper-heuristics can use. The paper concludes with an overview of future extensions of the toolkit.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114946592","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: 11

A PSO-based hyper-heuristic for evolving dispatching rules in job shop scheduling 基于粒子群算法的作业车间调度规则演化超启发式算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969402

Su Nguyen, Mengjie Zhang

引用次数: 9