2017 IEEE Congress on Evolutionary Computation (CEC)最新文献_第8页

Hybrid biogeography-based optimization for solving vendor managed inventory system 基于混合生物地理的供应商管理库存系统优化求解

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969621

Zubair Ashraf, Deepika Malhotra, Pranab K. Muhuri, Q. Lohani

{"title":"Hybrid biogeography-based optimization for solving vendor managed inventory system","authors":"Zubair Ashraf, Deepika Malhotra, Pranab K. Muhuri, Q. Lohani","doi":"10.1109/CEC.2017.7969621","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969621","url":null,"abstract":"In the modern era of industrialization and globalization, distribution and control of goods are essential aspects for multinational corporations and strategic partners. Vendor managed inventory (VMI) is one of the well-known strategies of merchandizing between supplier and retailer. In this paper, we consider different number of suppliers and retailers to perform business under VMI system and formulate three: single-supplier and single-retailer, single-supplier and multi-retailer, and multi-supplier and multi-retailer VMI systems. The objective is to minimize the total cost of VMI system. Since it is a non-linear integer programming problem, this paper proposes a novel hybrid biogeography-based optimization algorithm to solve it. We enhance the proposed algorithm by embedding stochastic fractal search (SFS) in biogeography-based optimization (BBO). SFS algorithm is a newly developed powerful evolutionary algorithm to find global optimum much faster and efficiently. The diffusion process of SFS improved the exploitation ability of search in BBO. Our proposed algorithm is applied on all three versions of VMI systems under different constraints. We have considered suitable input data for all the different problems and obtained the results. By comparison, we show that the results outperformed for all VMI systems.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"26 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":"121924319","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: 8

Solving the Firefighter Problem with two elements using a multi-modal Estimation of Distribution Algorithm 用多模态分布估计算法求解两要素消防员问题

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969566

Piotr Lipiński

{"title":"Solving the Firefighter Problem with two elements using a multi-modal Estimation of Distribution Algorithm","authors":"Piotr Lipiński","doi":"10.1109/CEC.2017.7969566","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969566","url":null,"abstract":"The Firefighter Problem (FFP) is an optimization problem of developing an optimal strategy for assigning firemen to nodes of a given graph in successive iterations of a simulation of spread of fires in the graph. This paper focusses on an extension of the original FFP, namely the Bi-Firefighter Problem (FFP2), where the second element (water) is introduced. FFP2 corresponds to the practical optimization problems, where more than one disease is spreading in the environment, and the objective is to minimize the total loss. Since the loss may come from two different sources, each of which causes different damages, the objective function is more complex than in the case of the original FFP. In this paper, an evolutionary approach to FFP2, the EA-FFP2 algorithm, based on a multi-modal Estimation of Distribution Algorithm (EDA), is proposed. EA-FFP2 was validated on a number of benchmark FFP2 instances that were also solved by the branch and bound algorithms or the heuristic local search algorithms run for a large number of iterations for a long time. Computational experiments confirmed that EA-FFP2 was capable of solving FFP2 and finding solutions close to the optima determined by the branch and bound algorithms or to the quasi-optima determined by exhaustive local search.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"93 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":"127065654","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

Document clustering with evolved search queries 具有进化搜索查询的文档聚类

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969447

Laurence Hirsch, A. D. Nuovo

{"title":"Document clustering with evolved search queries","authors":"Laurence Hirsch, A. D. Nuovo","doi":"10.1109/CEC.2017.7969447","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969447","url":null,"abstract":"Search queries define a set of documents located in a collection and can be used to rank the documents by assigning each document a score according to their closeness to the query in the multidimensional space of weighted terms. In this paper, we describe a system whereby an island model genetic algorithm (GA) creates individuals which can generate a set of Apache Lucene search queries for the purpose of text document clustering. A cluster is specified by the documents returned by a single query in the set. Each document that is included in only one of the clusters adds to the fitness of the individual and each document that is included in more than one cluster will reduce the fitness. The method can be refined by using the ranking score of each document in the fitness test. The system has a number of advantages; in particular, the final search queries are easily understood and offer a simple explanation of the clusters, meaning that an extra cluster labelling stage is not required. We describe how the GA can be used to build queries and show results for clustering on various data sets and with different query sizes. Results are also compared with clusters built using the widely used k-means algorithm.","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":"134379156","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: 8

Genetic programming for skin cancer detection in dermoscopic images 皮肤镜图像中皮肤癌检测的遗传程序设计

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969598

Q. Ain, Bing Xue, Harith Al-Sahaf, Mengjie Zhang

{"title":"Genetic programming for skin cancer detection in dermoscopic images","authors":"Q. Ain, Bing Xue, Harith Al-Sahaf, Mengjie Zhang","doi":"10.1109/CEC.2017.7969598","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969598","url":null,"abstract":"Development of an effective skin cancer detection system can greatly assist the dermatologist while significantly increasing the survival rate of the patient. To deal with melanoma detection, knowledge of dermatology can be combined with computer vision techniques to evolve better solutions. Image classification can significantly help in diagnosing the disease by accurately identifying the morphological structures of skin lesions responsible for developing cancer. Genetic Programming (GP), an emerging Evolutionary Computation technique, has the potential to evolve better solutions for image classification problems compared to many existing methods. In this paper, GP has been utilized to automatically evolve a classifier for skin cancer detection and also analysed GP as a feature selection method. For combining knowledge of dermatology and computer vision techniques, GP has been given domain specific features provided by the dermatologists as well as Local Binary Pattern features extracted from the dermoscopic images. The results have shown that GP has significantly outperformed or achieved comparable performance compared to the existing methods for skin cancer detection.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"16 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":"127109856","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: 19

Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems 基于欧几里得邻域的集成正弦微分协方差矩阵自适应求解CEC2017基准问题

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969336

Noor H. Awad, Mostafa Z. Ali, P. N. Suganthan

{"title":"Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems","authors":"Noor H. Awad, Mostafa Z. Ali, P. N. Suganthan","doi":"10.1109/CEC.2017.7969336","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969336","url":null,"abstract":"Many Differential Evolution algorithms are introduced in the literature to solve optimization problems with diverse set of characteristics. In this paper, we propose an extension of the previously published paper LSHADE-EpSin that was ranked as the joint winner in the real-parameter single objective optimization competition, CEC 2016. The contribution of this work constitutes two major modifications that have been added to enhance the performance: ensemble of sinusoidal approaches based on performance adaptation and covariance matrix learning for the crossover operator. Two sinusoidal waves have been used to adapt the scaling factor: non-adaptive sinusoidal decreasing adjustment and an adaptive sinusoidal increasing adjustment. Instead of choosing one of the sinusoidal waves randomly, a performance adaptation scheme based on earlier success is used in this work. Moreover, covariance matrix learning with Euclidean neighborhood is used for the crossover operator to establish a suitable coordinate system, and to enhance the capability of LSHADE-EpSin to tackle problems with high correlation between the variables. The proposed algorithm, namely LSHADE-cnEpSin, is tested on the IEEE CEC2017 problems used in the Special Session and Competitions on Single Objective Bound Constrained Real-Parameter Single Objective Optimization. The results statistically affirm the efficiency of the proposed approach to obtain better results compared to other state-of-the-art algorithms.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"40 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":"130057597","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: 238

A multi-objective evolutionary algorithm for emergency logistics scheduling in large-scale disaster relief 大规模救灾应急物流调度的多目标进化算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969295

Xiaohui Gan, Jing Liu

引用次数: 8

Evolutionary multilabel hyper-heuristic design 进化多标签超启发式设计

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969624

Alejandro Rosales-Pérez, A. E. Gutiérrez-Rodríguez, J. C. Ortíz-Bayliss, H. Terashima-Marín, C. Coello

{"title":"Evolutionary multilabel hyper-heuristic design","authors":"Alejandro Rosales-Pérez, A. E. Gutiérrez-Rodríguez, J. C. Ortíz-Bayliss, H. Terashima-Marín, C. Coello","doi":"10.1109/CEC.2017.7969624","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969624","url":null,"abstract":"Nowadays, heuristics represent a commonly used alternative to solve complex optimization problems. This, however, has given rise to the problem of choosing the most effective heuristic for a given problem. In recent years, one of the most used strategies for this task has been the hyper-heuristics, which aim at selecting/generating heuristics to solve a wide range of optimization problems. Most of the existing selection hyper-heuristics attempt to recommend only one heuristic for a given instance. However, for some classes of problems, more than one heuristic can be suitable. With this premise, in this paper, we address this issue through an evolutionary multilabel learning approach for building hyper-heuristics. Unlike traditional approaches, in themultilabel formulation, the result could not be a single recommendation, but a set of potential heuristics. Due to the fact that cooperative coevolutionary algorithms allow us to divide the problem into several subproblems, it results in a natural approach for dealing with multilabel classification. The proposed cooperative coevolutionarymultilabel approach aims at choosing the most relevant patterns for each heuristic. For the experimental study included in this paper, we have used a set of constraint satisfaction problems as our study case. Our experimental results suggest that the proposed method is able to generate accurate hyper-heuristics that outperform reference methods.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"47 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":"124133005","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: 4

Novel Clique enumeration heuristic for detecting overlapping clusters 一种新的重叠聚类检测的团枚举启发式算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969466

R. Schmitt, P. Ramos, Rafael de Santiago, L. Lamb

引用次数: 2

A note on constrained multi-objective optimization benchmark problems 约束多目标优化基准问题研究

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969433

Ryoji Tanabe, A. Oyama

{"title":"A note on constrained multi-objective optimization benchmark problems","authors":"Ryoji Tanabe, A. Oyama","doi":"10.1109/CEC.2017.7969433","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969433","url":null,"abstract":"We investigate the properties of widely used constrained multi-objective optimization benchmark problems. A number of Multi-Objective Evolutionary Algorithms (MOEAs) for Constrained Multi-Objective Optimization Problems (CMOPs) have been proposed in the past few years. The C-DTLZ functions and Real-World-Like Problems (RWLPs) have frequently been used for evaluating the performance of MOEAs on CMOPs. In this paper, however, we show that the C-DTLZ functions and widely-used RWLPs have some unnatural problem features. The experimental results show that an MOEA without any Constraint Handling Techniques (CHTs) can successfully find well-approximated nondominated feasible solutions on the C1-DTLZ1, C1-DTLZ3, and C2-DTLZ2 functions. It is widely believed that RWLPs are MOEA-hard problems, and finding the feasible solutions on them is a very hard task. However, we show that the MOEA without any CHTs can find feasible solutions on widely-used RWLPs such as the speed reducer design problem, the two-bar truss design problem, and the water problem. Also, it is seldom that the infeasible solution simultaneously violates multiple constraints in the RWLPs. Due to the above reasons, we conclude that constrained multi-objective optimization benchmark problems need a careful reconsideration.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"27 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":"131882226","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: 33

Elitism and aggregation methods in partial redundant evolutionary swarms solving a multi-objective tasks 部分冗余进化群中的精英和聚集方法求解多目标任务

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969476

Ruby L. V. Moritz, Heiner Zille, Sanaz Mostaghim

{"title":"Elitism and aggregation methods in partial redundant evolutionary swarms solving a multi-objective tasks","authors":"Ruby L. V. Moritz, Heiner Zille, Sanaz Mostaghim","doi":"10.1109/CEC.2017.7969476","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969476","url":null,"abstract":"In evolutionary swarms adaptability and diversity are closely related concepts. In order to get a better understanding of their codependency we study a heterogeneous evolutionary multi-agent system with different rates of redundancy within the genetic material. The agents process a dynamic multi-objective task, where their genetic material defines their efficiency concerning the different objective functions of that task. One focus of this study is the influence of an elitist behavior performed by the agents during the evolutionary process, where an agent can decline the genetic material of another agent if it does not meet specific requirements. Further we analyze the impact of three different methods to aggregate the objective values into a single fitness value that is applicable for the evolutionary mechanism of the system. The results show that heterogeneity in the optimization behavior of the agents is very beneficial as it maintains a higher diversity in the system. The elitist behavior of the agents slows the evolutionary process but gives it a stronger pull towards qualitatively higher positions in the objective space. Indeed, the pace of the evolutionary process ultimately has a higher impact on the adaptability of the system than the amount of redundancy in the genetic information.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"15 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":"125061600","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