2015 IEEE Congress on Evolutionary Computation (CEC)最新文献_第3页

Evaluating landscape characteristics of dynamic benchmark functions 动态基准函数的景观特征评价

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257044

R. Bond, A. Engelbrecht, B. Ombuki-Berman

引用次数: 4

Particle swam optimization based reliability-redundancy allocation in a type-2 fuzzy environment 二类模糊环境下基于粒子游优化的可靠性冗余分配

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257027

Zubair Ashraf, Pranab K. Muhuri, Q. Lohani

引用次数: 23

Resource allocation between initialization and optimization under computational expensive environment 计算昂贵环境下初始化与优化之间的资源分配

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257037

Yi Sun, V. Li

引用次数: 1

Ship route evolutionary optimization of multiple ship companies for distributed coordination of resources 多船公司资源分布式协调下的航路演化优化

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257059

K. Takadama, Hiroyuki Sato, D. Watanabe, Eriko Azuma, T. Majima, M. Katuhara

{"title":"Ship route evolutionary optimization of multiple ship companies for distributed coordination of resources","authors":"K. Takadama, Hiroyuki Sato, D. Watanabe, Eriko Azuma, T. Majima, M. Katuhara","doi":"10.1109/CEC.2015.7257059","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257059","url":null,"abstract":"This paper proposes a ship route evolutionary optimization method for competitive ship companies in the industrial logistic network, where many alliances composed of several ship companies compete with others to acquire their resources (i.e., container) for maximizing their profits. One of the significant issues in the industrial logistic network is to find the best distribution of the resources in all alliances even in a competitive market. For this purpose, this paper explores the ship route optimization method for all competitive alliances, which can find their ship routes having higher profit than their actual routes through a good distributed coordination of resources. The intensive analysis of the results on the Pacific Ocean liner route with the actual data have revealed that the following implications: (1) even in competitive situation, the proposed evolutionary optimization method succeeds to find the ship routes of all alliances which can improve their own profits in comparison with those optimized by the conventional approach and those of actual routes, and (2) the ship routes generated by the proposed method have more anchor ports than the actual ship routes while keeping the ship constraints (e.g., the type of ships that each alliance has), which contributes to obtaining the appropriate resources as a good distributed coordination.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124645911","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

A hybrid development platform for evolutionary multi-objective optimization 进化多目标优化的混合开发平台

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257116

R. Shen, Jinhua Zheng, M. Li

{"title":"A hybrid development platform for evolutionary multi-objective optimization","authors":"R. Shen, Jinhua Zheng, M. Li","doi":"10.1109/CEC.2015.7257116","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257116","url":null,"abstract":"This paper introduces an optimization template library (OTL), a cross-platform C++ template library for multiobjective optimization. OTL has an object-oriented architecture, which allows that different modules can be arbitrarily combined with each other. Moreover, the C++ template technique is used to increase the flexibility of OTL. Meanwhile, generic programming is widely used in OTL, and the generic algorithms can be used to process different data structures. However, compared with C++, the Python script is more suitable for building the experimental platform. To ensure that all attributes of the experimental results can be fully maintained, a database is used to store the experimental data. Moreover, batch experiments can be easily defined in a set of configuration files; thus, the experiments can be executed automatically without human intervention. In addition, serial and various parallel execution modes are supported, and the user can easily switch the running mode to distributed computing to increase the computing speed. Finally, a highly customizable data visualization tool is created to play back the data sample stored in the database. From a series of comparative studies, the accuracy and running performance of OTL are verified by the statistical results.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130379668","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: 9

Evolution of 2D apoptotic cellular automata 二维细胞凋亡自动机的进化

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257151

Jennifer Garner, D. Ashlock

引用次数: 2

Application of metaheuristic algorithms in nano-process parameter optimization 元启发式算法在纳米工艺参数优化中的应用

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257212

M. S. Norlina, P. Mazidah, N. Sin, M. Rusop

{"title":"Application of metaheuristic algorithms in nano-process parameter optimization","authors":"M. S. Norlina, P. Mazidah, N. Sin, M. Rusop","doi":"10.1109/CEC.2015.7257212","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257212","url":null,"abstract":"This paper presents the adaptation of Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) in solving a nano-process parameter optimization problem. The nano-process in this study is involving the RF magnetron sputtering process. The performances of the algorithms are compared in this optimization problem. The performance of GA, PSO and GSA is evaluated based on the fitness of the optimized parameter combination, processing times and the results from comparison with the actual laboratory experiments. The purpose of this computational experiment is to obtain the most optimized parameter combination among the selected datasets. The source material used in this study is zinc oxide (ZnO) and the most optimized combination of the process parameters is expected to produce the desirable nanostructured ZnO thin film's electrical properties. The results have shown that GA could perform better than PSO and GSA by generating higher fitness values in 30 trial runs. However, GA has obtained the slowest execution time among the three algorithms. In this study, GSA has also produced an acceptable and promising result with faster execution time. When compared with the actual laboratory experiment, GA and GSA have generated more accurate optimization results. In terms of convergence of the algorithms, GA and GSA have shown more stable convergence compared to PSO. This study has shown that metaheuristic techniques are promising and reliable to be applied in solving this process parameter optimization problem.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129135463","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

Optimizing highly constrained truck loadings using a self-adaptive genetic algorithm 基于自适应遗传算法的高约束卡车装载优化

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256896

Sander van Rijn, M. Emmerich, Edgar Reehuis, Thomas Bäck

引用次数: 16

EPSO-based Gaussian Process for electricity price forecasting 基于epso的高斯过程电价预测

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256904

H. Mori, K. Nakano

{"title":"EPSO-based Gaussian Process for electricity price forecasting","authors":"H. Mori, K. Nakano","doi":"10.1109/CEC.2015.7256904","DOIUrl":"https://doi.org/10.1109/CEC.2015.7256904","url":null,"abstract":"In this paper, a new method is proposed for Locational Marginal Pricing (LMP) forecasting in Smart Grid. The marginal cost is required to supply electric power to incremental loads in case where a certain node increases power demands in a balanced power system. LMP plays an important role to maintain economic efficiency in electric power markets in a way that electricity flows from a low-cost area to high-cost ones and the transmission network congestion is alleviated. The power market players are interested in maximizing the profits and minimizing the risks through selling and buying electricity. As a result, it is of importance to obtain accurate information on electricity pricing forecasting in advance so that their aim is achieved. This paper presents the Gaussian Process (GP) technique that comes from the extension of Support Vector Machine (SVM) in which hierarchical Bayesian estimation is introduced to express the model parameters as the probabilistic variables. The advantage is that the model accuracy of GP is better than others. GP is integrated with k-means of clustering to improve the performance of GP. Also, this paper makes use of the Mahalanobis kernel in GP rather than the Gaussian one so that GP is generalized to approximate nonlinear systems. EPSO of evolutionary computation is applied to GP to determine the parameters of the kernel function. The effectiveness of the proposed method is demonstrated for real data of ISO New England in USA.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121754395","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

Imbalanced classification using genetically optimized cost sensitive classifiers 基于遗传优化代价敏感分类器的不平衡分类

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256956

Todd Perry, M. Bader-El-Den, Steven Cooper

{"title":"Imbalanced classification using genetically optimized cost sensitive classifiers","authors":"Todd Perry, M. Bader-El-Den, Steven Cooper","doi":"10.1109/CEC.2015.7256956","DOIUrl":"https://doi.org/10.1109/CEC.2015.7256956","url":null,"abstract":"Classification is one of the most researched problems in machine learning, since the 1960s a myriad of different techniques have been proposed. The purpose of a classification algorithm, also known as a `classifier', is to identify what class, or category an observation belongs to. In many real-world scenarios, datasets tend to suffer from class imbalance, where the number of observations belonging to one class greatly outnumbers that of the observations belonging to other classes. Class imbalance has been shown to hinder the performance of classifiers, and several techniques have been developed to improve the performance of imbalanced classifiers. Using a cost matrix is one such technique for dealing with class imbalance, however it requires a matrix to be either pre-defined, or manually optimized. This paper proposes an approach for automatically generating optimized cost matrices using a genetic algorithm. The genetic algorithm can generate matrices for classification problems with any number of classes, and is easy to tailor towards specific use-cases. The proposed approach is compared against unoptimized classifiers and alternative cost matrix optimization techniques using a variety of datasets. In addition to this, storage system failure prediction datasets are provided by Seagate UK, the potential of these datasets is investigated.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114762216","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: 24