检索结果-内蒙古大学图书馆

11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) / 11th International Conference on Knowledge Discovery and Information Retrieval (KDIR)

作者： Ghazal, Mohamed A. Ghoniemy, Samy Salama, Mostafa A. British Univ Egypt Dept Comp Sci Cairo Egypt

ISBN: (纸本)9789897583827

Process Mining is a research field that aims to develop new techniques to discover, monitor and improve real processes by extracting knowledge from event logs. This relatively young research discipline has evidenced efficacy in various applications, especially in application domains where a dynamic behavior needs to be related to process models. Process Model Discovery is presumably the most important task in Process Mining since the discovered models can be used as an objective starting points for any further process analysis to be conducted. There are various quality dimensions the model should consider during discovery such as Replay-Fitness, Precision, Generalization, and Simplicity. It becomes evident that Process Model Discovery, with its current given settings, is a multi-objective Optimization Problem. However, most existing techniques does not approach the problem as a multi-objective Optimization Problem. Therefore, in this work we propose the use of one of the most robust and widely used multi-objective Optimizers in Process Model Discovery, the NSGA-II algorithm. Experimental results on a real life event log shows that the proposed technique outperforms existing techniques in various aspects. Also this work tries to establish a benchmarking system for comparing results of multi-objective Optimization based Process Model Discovery techniques.

关键词： multi-objective Optimization Process Mining multi-objective evolutionary algorithms Process Model Discovery Non-dominated Sorting Genetic Algorithm II

来源：评论

学校读者我要写书评

暂无评论

multi-objective evolutionary simulation based optimization mechanism for a novel stochastic reliability centered maintenance problem

引用

SWARM AND evolutionary COMPUTATION 2018年 40卷 255-271页

作者： Rahmati, Seyed Habib A. Ahmadi, Abbas Karimi, Behrooz Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin Branch Qazvin Iran

This research develops a novel stochastic reliability-centered maintenance (RCM) mechanism within a new multi objective joint maintenance and production planning problem. RCM in this integrated problem is an agent that monitors and manages the maintenance functions of a stochastic complex production-planning problem, namely flexible job shop scheduling problem (FJSP). The novel developed RCM takes benefit of stochastic condition based maintenance (CBM) approach that works based on stochastic shocking scheme of machines during their process time. It activates the maintenance activities, including preventive and corrective maintenance, according to the degradation level of system reliability after shocks and not merely according to the predetermined intervals. In addition to the maintenance activation times, the maintenance durations of different kinds are also modeled stochastically. Furthermore, different types of stochastic maintenance costs are also considered alongside system reliability and complementation time (C-max). Moreover, as the problem belongs to the NP-Hard class of optimization problems, four multi-objective simulation based optimization (SBO) algorithms, called multi-objective biogeography based optimization (MOBBO) algorithm, Pareto envelope-based selection algorithm (PESA), new version of non-dominated sorting genetic algorithm (NSGAIII) and multi-objective evolutionary algorithm based on decomposition (MOEAD) are employed to solve the underlying problem. A novel visualization approach joint by Gant chart is also proposed to discuss the whole RCM scheme, systematically. Different test problems, statistical tests and outputs explain the problem and algorithms' performance explicitly.

关键词： Reliability-centered maintenance (RCM) Stochastic production model Condition-based maintenance (CBM) Shocking mechanism multi-objective simulation-based optimization (SBO) multi-objective evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Distributed NSGA-II Sharing Extreme Non-dominated Solutions for Improving Accuracy and Achieving Speed-up

Distributed NSGA-II Sharing Extreme Non-dominated Solutions ...

引用

IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Sato, Yuji Sato, Mikiko Miyakawa, Minami Hosei Univ Fac Comp & Informat Sci Tokyo Japan Tokai Univ Sch Informat & Telecommun Engn Tokyo Japan Sinshu Univ Fac Engn Dept Elect & Comp Engn Nagano Japan

ISBN: (纸本)9781728121536

A recent trend of multi-objective evolutionary algorithms is to approximate the Pareto optimal front with high accuracy by increasing the size of the population. On the other hand, the cconventional method NSGA-II algorithm widely used in multi-objective optimization ranks solutions using non-dominant sorting that increase in computational complexity proportional to the population size squared. In engineering applications, the increased execution time can be a problem. Also, even if standard techniques for parallel processing of genetic manipulation are applied to multi-objective evolutionary computation, it is difficult to speed up while maintaining the accuracy of the solution search. This paper proposes the distributed NSGA-II which performs a hierarchical non-dominated sorting in a many-core environment, and migration that shares the extreme solutions of the latest Pareto optimal solutions among all cores. In the evaluation, using the test functions provided by the NSGA-II source code and the constrained knapsack problem with two-objective and three-objective, we describe that our proposed method contributes to the improvement of solution diversity search and for enhancing solution accuracy and speeding up.

关键词： NSGA-II non-dominated sorting parallel and distributed processing many-core environment multi-objective evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Convex hull ranking algorithm for multi-objective evolutionary algorithms

引用

SCIENTIA IRANICA 2011年第6期18卷 1435-1442页

作者： Monfared, M. Davoodi Mohades, A. Rezaei, J. Amirkabir Univ Technol Dept Math & Comp Sci Lab Algorithms & Computat Geometry Tehran Iran Delft Univ Technol Fac Technol Policy & Management Dept Technol Strategy & Entrepreneurship NL-2600 AA Delft Netherlands Amirkabir Univ Technol Dept Math & Comp Sci Comp Sci Grp Tehran Iran

Due to many applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolutionary algorithms are based on the non-dominated principle, and their complexity depends on finding non-dominated fronts, this paper introduces a new method for ranking the solutions of an evolutionary algorithm's population. First, we investigate the relation between the convex hull and non-dominated solutions, and discuss the complexity time of the convex hull and non-dominated sorting problems. Then, we use convex hull concepts to present a new ranking procedure for multi-objective evolutionary algorithms. The proposed algorithm is very suitable for convex multi-objective optimization problems. Finally, we apply this method as an alternative ranking procedure to NSGA-II for non-dominated comparisons, and test it using some benchmark problems. (C) 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Convex hull Non-dominated solutions multi-objective evolutionary algorithms Complexity Ranking procedure

来源：评论

学校读者我要写书评

暂无评论

A multi-objective evolutionary Action Rule Mining Method

A Multi-Objective Evolutionary Action Rule Mining Method

引用

IEEE Congress on evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Daly, Grant Benton, Ryan Johnsten, Tom Univ S Alabama Dept Comp Sci Sch Comp Mobile AL 36688 USA

ISBN: (纸本)9781509060177

Action rules are rules that describe how to transition a decision attribute from an undesired state to a desired state, with the understanding that some attributes are stable and others are flexible. Stable attributes, such as "age", may not be changed, whereas flexible attributes, such as "interest rate", may be changed. Action rules have great potential in data mining, as they output easily interpretable rules which can immediately be useful to a decision maker. However, at present, the methods to generate all valid action rules are computationally expensive. To address this, methods have been proposed that prune swaths of the search space as rules are generated;this results in computational efficiency, at the expense of potentially not discovering many useful rules. In this work, a method, called multi-objective evolutionary Action Rule (MOEAR) mining, is introduced. MOEAR optimizes the discovery of action rules using standard evolutionary algorithm principles. Experimental results show that MOEAR is able to generate a large number of potentially interesting action rules, including those rules that could be categorized as "rare", while achieving good computational performance.

关键词： Action Rules multi-objective evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

On the Many-objective Pickup and Delivery Problem: Analysis of the Performance of Three evolutionary algorithms 16th

On the Many-Objective Pickup and Delivery Problem: Analysis ...

引用

16th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Garcia-Najera, Abel Lopez-Jaimes, Antonio Zapotecas-Martinez, Saul Univ Autonoma Metropolitana Dept Matemat Aplicadas & Sistemas Unidad Cuajimalpa Av Vasco de Quiroga 4871 Ciudad De Mexico 05300 Mexico

ISBN: (纸本)9783030028374;9783030028367

Many-objective optimization focuses on solving optimization problems with four or more objectives. Effort has been made mainly on studying continuous problems, with interesting results and for which several optimizers have been proposed. Nevertheless, combinatorial problems have not received as much attention, making this an open research area. An important result on continuous problems states that the problem does not necessarily becomes more difficult while more objectives are considered, but, does this result hold for combinatorial problems? This investigation takes this subject on by studying a many-objective combinatorial problem, particularly, the pickup and delivery problem (PDP), which is an important combinatorial optimization problem in the transportation industry and consists in finding a collection of routes with minimum cost. Traditionally, cost has been associated with the number of routes and the total travel distance, however, some other objectives emerge in many applications, for example, travel time, workload imbalance, and uncollected profit. If we consider all these objectives equally important, PDP can be tackled as a many-objective problem. This study is concerned with the study of the performance of three multi-objective evolutionary algorithms on the PDP varying the number of objectives, in order to analyze the change of PDP's difficulty when the number of objectives is increased. Results show that the problem becomes more difficult to solve while more objectives are considered.

关键词： Many-objective optimization Pickup and delivery problem multi-objective evolutionary algorithms Combinatorial optimization

来源：评论

学校读者我要写书评

暂无评论

multi-objective evolutionary Neural Network to Predict Graduation Success at the United States Military Academy

Multi-objective Evolutionary Neural Network to Predict Gradu...

引用

Complex Adaptive Systems Conference (CAS) - Cyber Physical Systems and Deep Learning

作者： Lesinski, Gene Corns, Steven US Mil Acad West Point NY 10996 USA Missouri Univ Sci & Technol Rolla MO 65409 USA

This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify students as: graduates, late graduates, or non-graduates. Results of the hybrid method show higher mean classification rates (88%) than the current methodology (80%) with a potential savings of $130M. Additionally, the proposed method is more efficient in that a less complex neural network topology is identified by the algorithm. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： evolutionary algorithms Neural network multi-objective evolutionary algorithms enrollment management student retention

来源：评论

学校读者我要写书评

暂无评论

A New Algorithm Using the Non-Dominated Tree to Improve Non-Dominated Sorting

引用

evolutionary COMPUTATION 2018年第1期26卷 89-116页

作者： Gustavsson, Patrik Syberfeldt, Anna Univ Skovde Sch Engn S-54134 Skovde Sweden

Non-dominated sorting is a technique often used in evolutionary algorithms to determine the quality of solutions in a population. The most common algorithm is the Fast Non-dominated Sort (FNS). This algorithm, however, has the drawback that its performance deteriorates when the population size grows. The same drawback applies also to other non-dominating sorting algorithms such as the Efficient Non-dominated Sort with Binary Strategy (ENS-BS). An algorithm suggested to overcome this drawback is the Divide-and-Conquer Non-dominated Sort (DCNS) which works well on a limited number of objectives but deteriorates when the number of objectives grows. This article presents a new, more efficient algorithm called the Efficient Non-dominated Sort with Non-Dominated Tree (ENS-NDT). ENS-NDT is an extension of the ENS-BS algorithm and uses a novel Non-Dominated Tree (NDTree) to speed up the non-dominated sorting. ENS-NDT is able to handle large population sizes and a large number of objectives more efficiently than existing algorithms for non-dominated sorting. In the article, it is shown that with ENS-NDT the runtime of multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) can be substantially reduced.

关键词： evolutionary computation k-d tree multi-objective evolutionary algorithms non-dominated sorting Pareto optimality runtime complexity

来源：评论

学校读者我要写书评

暂无评论

An investigation into many-objective optimization on combinatorial problems: Analyzing the pickup and delivery problem

引用

SWARM AND evolutionary COMPUTATION 2018年 38卷 218-230页

作者： Garcia-Najera, Abel Lopez-Jaimes, Antonio Univ Autanoma Metropolitana Dept Matemat Aplicadas & Sistemas Unidad Cuajimalpa Av Vasco de Quiroga 4871Col Santa Fe Cuajimalpa Mexico City 05348 DF Mexico

Many-objective optimization focuses on solving optimization problems with four or more objectives. Effort has been made mainly on studying continuous problems, with interesting results and for which several proposals have appeared. An important result states that the problem does not necessarily becomes more difficult while more objectives are considered. Nevertheless, combinatorial problems have not received an appropriate attention, making this an open research area. This investigation takes this subject on by studying a many objective combinatorial problem, particularly, the pickup and delivery problem (PDP), which is an important combinatorial optimization problem in the transportation industry and consists of finding a collection of routes with minimum cost. Traditionally, cost has been associated with the number of routes and the total travel distance, however, in many applications, some other objectives emerge, for example, travel time, workload imbalance, and uncollected profit. If we consider all these objectives equally important, PDP can be tackled as a many-objective problem. This study is concerned with the study of: (i) the performance of four representative multi-objective evolutionary algorithms on PDP varying the number of objectives, (ii) the properties of the many-objective PDP regarding scalability, i.e. the conflict between each pair of objectives and the proportion of non-dominated solutions as the number of objectives is varied, and finally (iii) the change of PDP's difficulty when the number of objectives is increased. Results show that the regarded objectives are actually in conflict and that the problem is more difficult to solve while more objectives are considered.

关键词： Many-objective optimization Pickup and delivery problem multi-objective evolutionary algorithms Combinatorial optimization

来源：评论

学校读者我要写书评

暂无评论

multi-objective SCHEDULING PROBLEM FOR HYBRID MANUFACTURING SYSTEMS WITH WALKING WORKERS

引用

INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2019年第5期26卷 625-650页

作者： Yilmaz, Omer Faruk Durmusoglu, Mehmet Bulent Karadeniz Tech Univ Dept Ind Engn Trabzon Turkey Istanbul Tech Univ Dept Ind Engn Istanbul Turkey

Hybrid manufacturing systems (HMSs) are attracting attention from academia and industry owing to concerns regarding fluctuations in customers' demands. An HMS combines traditional manufacturing cells and a functional area, leading to improved flexibility in meeting customers' demands Although information on HMSs is available in the existing academic literature, scheduling problems with walking workers for such systems are rarely addressed. This study explores a multi-objective scheduling problem in HMS and proposes an optimization model to achieve three objectives (i) minimization of average flow time, (ii) reducing the maximum number of workers, and (iii) minimization of the maximum number of workers changing. The model belongs to a non-deterministic polynomial-time hardness (NP-hard) problem class, and hence, non-dominated sorting algorithm-II (NSGA-II) with a local search procedure is employed. The proposed algorithm and several widely accepted multi-objective evolutionary algorithms are compared for six different cases. A set of evaluation metrics is used to evaluate the effectiveness of the proposed algorithm in finding desirable solutions. The computational results indicate that the proposed algorithm is superior to other metaheuristics. This study contributes to existing academic literature by investigating lot scheduling problem with walking workers in the context of hybrid manufacturing systems and provides guidelines for researchers and industry practitioners.

关键词： hybrid manufacturing system scheduling problem walking workers multi-objective evolutionary algorithms NSGA-II

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：