检索结果-内蒙古大学图书馆

9th International Conference on Information Technology and Applications in Biomedicine

作者： Kannas, Christos C. Nicolaou, Christos A. Pattichis, Constantinos S. Univ Cyprus Noesis Chemoinformat CY-1678 Nicosia Cyprus Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus Univ Cyprus Cyprus Inst Noesis Chemoinformat CY-1678 Nicosia Cyprus

ISBN: (纸本)9781424453788

Multi-objective evolutionary algorithms (MOEAs) have features that can be exploited to harness the processing power offered by modern multi-core CPUs. Modern programming languages offer the ability to use threads and processes in order to achieve parallelism that is inherent in multi-core CPUs. In this paper we present our parallel implementation of a MOEA algorithm and its application to the de novo drug design problem. The results indicate that using multiple processes that execute independent tasks of a MOEA, can reduce significantly the execution time required and maintain comparable solution quality thereby achieving improved performance.

关键词： evolutionary algorithms Multi-objective evolutionary algorithms parallel evolutionary algorithms parallel Multi-objective evolutionary algorithms parallel Processing

来源：评论

学校读者我要写书评

暂无评论

A parallel hybrid evolutionary algorithm for the optimization of broker virtual machines subletting in cloud systems

A parallel hybrid evolutionary algorithm for the optimizatio...

引用

8th International Conference on P2P, parallel, Grid, Cloud and Internet Computing (3PGCIC)

作者： Iturriaga, Santiago Nesmachnow, Sergio Dorronsoro, Bernabe Talbi, El-Ghazali Bouvry, Pascal Universidad de la República Uruguay University of Lille France University of Luxembourg Luxembourg

ISBN: (纸本)9780769550947

This article presents a new parallel hybrid evolutionary algorithm to solve the problem of virtual machines subletting in cloud systems. The problem deals with the efficient allocation of a set of virtual machine requests from customers into available pre-booked resources from a cloud broker, in order to maximize the broker profit. The proposed parallel algorithm uses a distributed subpopulations model, and a Simulated Annealing operator. The experimental evaluation analyzes the profit and makespan results of the proposed methods over a set of problem instances that account for realistic workloads and scenarios using real data from cloud providers. A comparison with greedy heuristics indicates that the proposed method is able to compute solutions with up to 133.8% improvement in the profit values, while accounting for accurate makespan results.

关键词： parallel evolutionary algorithms scheduling cloud computing

来源：评论

学校读者我要写书评

暂无评论

A parallel Divide-and-Conquer-Based evolutionary Algorithm for Large-Scale Optimization

引用

IEEE ACCESS 2019年 7卷 163105-163118页

作者： Yang, Peng Tang, Ke Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Univ Key Lab Evolving Intelligent Syst Guangdong Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China

Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas. As a powerful optimization tool for many real-world applications, evolutionary algorithms (EAs) fail to solve the emerging large-scale problems both effectively and computationally efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA that can not only produce high-quality solutions by solving sub-problems separately, but also benefits significantly from the power of parallel computing by solving the sub-problems simultaneously. Existing DC-based EAs that were thought to enjoy the same advantages of the proposed algorithm, are shown to be practically incompatible with the parallel computing scheme, unless some trade-offs are made by compromising the solution quality.

关键词： parallel evolutionary algorithms large-scale optimization divide-and-conquer

来源：评论

学校读者我要写书评

暂无评论

Increase in the quality of the prediction of a computational wildfire behavior method through the improvement of the internal metaheuristic

引用

FIRE SAFETY JOURNAL 2016年 82卷 49-62页

作者： Mendez-Garabetti, Miguel Bianchini, German Caymes-Scutari, Paola Laura Tardivo, Maria Univ Tecnol Nacl Fac Reg Mendoza Dept Ingn Sistemas Informac Lab Invest Comp Paralelo Distribuido LICPaD M5502AJE Mendoza Argentina Consejo Nacl Invest Cient & Tecn RA-1033 Buenos Aires DF Argentina Univ Nacl Cuyo Ctr Univ Inst Tecnol Univ RA-5500 Mendoza Argentina Univ Nacl Rio Cuarto Dept Computac Cordoba Argentina

Wildfires cause great losses and harms every year, some of which are often irreparable. Among the different strategies and technologies available to mitigate the effects of fire, wildfire behavior prediction may be a promising strategy. This approach allows for the identification of areas at greatest risk of being burned, thereby permitting to make decisions which in turn will help to reduce losses and damages. In this work we present an evolutionary-Statistical System with Island Model, a new approach of the uncertainty reduction method evolutionary-Statistical System. The operation of ESS is based on statistical analysis, parallel computing and parallel evolutionary algorithms (PEA). ESS-IM empowers and broadens the search process and space by incorporating the Island Model in the metaheuristic stage (PEA), which increases the level of parallelism and, in fact, it permits to improve the quality of predictions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Wildfire behavior prediction Simulation Uncertainty reduction parallel evolutionary algorithms Statistical System

来源：评论

学校读者我要写书评

暂无评论

Cooperative parallel grouping genetic algorithm for the one-dimensional bin packing problem

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2018年 125卷 157-170页

作者： Kucukyilmaz, Tayfun Kiziloz, Hakan Ezgi TED Univ Ankara Turkey Univ Turkish Aeronaut Assoc Ankara Turkey

evolutionary algorithms have been reported to be efficient metaheuristics for the optimization of several NP Hard combinatorial optimization problems. In addition to their ability to solve difficult and complex problems in reasonable execution times, parallelized versions of evolutionary algorithms are reported to explore and exploit the problem search space more effectively than their sequential counterparts. The Island Model, where the population of a given run is divided into semi isolated subpopulations, is a popular parallelization approach for evolutionary algorithms such as Grouping Genetic algorithms (GGA). Although the nature of GGAs is very suitable for coarse-grained parallel processing, designing an Island-parallel model for them is not a straightforward task. Selecting the communication topology, deciding migration and assimilation strategies, adjusting the migration rate and frequency, and using efficient diversification techniques are some of the important issues that needs to be covered in a successful Island-parallel Model. In this study, we propose a novel, scalable Island parallel GGA (IPGGA) for the well-known combinatorial optimization Problem 1D Bin-Packing (1DBPP). We provide a thorough experimental evaluation of the parallel model and report significant improvements on the Hard28 problem instances by outperforming the state-of-the-art genetic algorithms. Additionally, we analyze and evaluate the parallelization parameters of IPGGA with an emphasis on problem search-space diversity and report several interesting results.

关键词： 1D bin-packing Grouping genetic algorithm parallel evolutionary algorithms Search space diversity Migration topology Assimilation strategy

来源：评论

学校读者我要写书评

暂无评论

THE ISLAND MODEL AS A MARKOV DYNAMIC SYSTEM

引用

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2012年第4期22卷 971-984页

作者： Schaefer, Robert Byrski, Aleksander Smolka, Maciej AGH Univ Sci & Technol Dept Comp Sci PL-30059 Krakow Poland Jagiellonian Univ Inst Comp Sci PL-30348 Krakow Poland

parallel multi-deme genetic algorithms are especially advantageous because they allow reducing the time of computations and can perform a much broader search than single-population ones. However, their formal analysis does not seem to have been studied exhaustively enough. In this paper we propose a mathematical framework describing a wide class of island-like strategies as a stationary Markov chain. Our approach uses extensively the modeling principles introduced by Vose, Rudolph and their collaborators. An original and crucial feature of the framework we propose is the mechanism of inter-deme agent operation synchronization. It is important from both a practical and a theoretical point of view. We show that under a mild assumption the resulting Markov chain is ergodic and the sequence of the related sampling measures converges to some invariant measure. The asymptotic guarantee of success is also obtained as a simple issue of ergodicity. Moreover, if the cardinality of each island population grows to infinity, then the sequence of the limit invariant measures contains a weakly convergent subsequence. The formal description of the island model obtained for the case of solving a single-objective problem can also be extended to the multi-objective case.

关键词： genetic algorithms asymptotic analysis global optimization parallel evolutionary algorithms Markov chain modeling

来源：评论

学校读者我要写书评

暂无评论

On the numerical approximation of some inverse problems governed by nonlinear delay differential equation

引用

RAIRO-OPERATIONS RESEARCH 2022年第3期56卷 1553-1569页

作者： Nachaoui, Mourad Nachaoui, Abdeljalil Tadumadze, Tamaz Univ Sultan Moulay Slimane EMI Beni Mellal Morocco Univ Nantes Lab Math Jean Leray CNRS UMR6629 Nantes France Tbilisi State Univ Tbilisi Georgia

The paper deals with the approximate solving of an inverse problem for the nonlinear delay differential equation, which consists of finding the initial moment and delay parameter based on some observed data. The inverse problem is considered as a nonlinear optimal control problem for which the necessary conditions of optimality are formulated and proved. The obtained optimal control problem is solved by a method based on an improved parallel evolutionary algorithm. The efficiency of the proposed approach is demonstrated through various numerical experiments.

关键词： Inverse problem delay differential equation necessary optimality conditions numerical approximation parallel evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

A single front genetic algorithm for parallel multi-objective optimization in dynamic environments

引用

NEUROCOMPUTING 2009年第16-18期72卷 3570-3579页

作者： Camara, Mario Ortega, Julio de Toro, Francisco Univ Granada Dept Comp Technol & Architecture E-18071 Granada Spain Univ Granada Dept Signal Theory Telemat & Commun E-18071 Granada Spain

This paper proposes a new parallel evolutionary procedure to solve multi-objective dynamic optimization problems along with some measures to evaluate multi-objective optimization in dynamic environments. These dynamic optimization problems appear in quite different real-world applications with actual socio-economic relevance. In these applications, the objective functions, the constraints, and hence, also the solutions, can change over time and usually demand to be solved online whilst the size of the changes is unknown. Although parallel processing could be very useful in these problems to meet the solution quality requirements and constraints, to date, not many parallel approaches have been reported in the literature. Taking this into account, we introduce a multi-objective optimization procedure for dynamic problems that are based on PSFGA, a parallel evolutionary algorithm previously proposed by us for multi-objective optimization. It uses an island model where a process divides the population among the remaining processes and allows the communication and coordination among the subpopulations in the different islands. The proposed algorithm makes an exclusive use of non-dominating individuals for the selection and variation operator and applies a crowding mechanism to maintain the diversity and the distribution of the solutions in the Pareto front. We also propose a model to understand the benefits of parallel processing in multi-objective problems and the speedup figures obtained in our experiments. (C) 2009 Elsevier B.V. All rights reserved.

关键词： parallel multi-objective optimization Dynamic optimization problems parallel evolutionary algorithms Neuro-dynamic programming

来源：评论

学校读者我要写书评

暂无评论

A parallel MOEA with criterion-based selection applied to the Knapsack Problem

引用

APPLIED SOFT COMPUTING 2019年 80卷 358-373页

作者： Kantour, Nedjmeddine Bouroubi, Sadek Chaabane, Djamel USTHB LIFORCE Lab Fac Math Dept Operat Res PB 32 El Alia Algiers 16111 Algeria USTHB AMCD RO Lab Fac Math Dept Operat Res PB 32 El Alia Algiers 16111 Algeria

In this paper, we propose a parallel multiobjective evolutionary algorithm called parallel Criterion-based Partitioning MOEA (PCPMOEA), with an application to the Multiobjective Knapsack Problem (MOKP). The suggested search strategy is based on a periodic partitioning of potentially efficient solutions, which are distributed to multiple multiobjective evolutionary algorithms (MOEAs). Each MOEA is dedicated to a sole objective, in which it combines both criterion-based and dominance-based approaches. The suggested algorithm addresses two main sub-objectives: minimizing the distance between the current non-dominated solutions and the ideal point, and ensuring the spread of the potentially efficient solutions. Experimental results are included, where we assess the performance of the suggested algorithm against the above mentioned sub-objectives, compared with state-of-the-art results using well-known multi-objective metaheuristics. (C) 2019 Elsevier B.V. All rights reserved.

关键词： parallel evolutionary algorithms Multiobjective discrete optimization Multiobjective Knapsack Problem

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Island evolutionary Algorithm for the berth scheduling problem

引用

MEMETIC COMPUTING 2020年第1期12卷 51-72页

作者： Dulebenets, Maxim A. Florida State Univ Florida A&M Univ Dept Civil & Environm Engn 2525 Pottsdamer StBldg ASuite A124 Tallahassee FL 32310 USA

Increasing volumes of the seaborne containerized trade put additional pressure on marine container terminal operators. Long congestion periods have been reported at certain marine container terminals due to inability of the infrastructure to serve the growing demand, increasing number of megaships, port disruptions, and other factors. In order to alleviate congestion and avoid potential cargo delivery delays to the end customers, marine container terminal operators have to enhance the efficiency of their operations. This study focuses on improving the seaside operations at marine container terminals. A new Adaptive Island evolutionary Algorithm is proposed for the berth scheduling problem, aiming to minimize the total weighted service cost of vessels. The developed algorithm simultaneously executes separate evolutionary algorithms in parallel on its islands and exchanges individuals between the islands based on an adaptive mechanism, which allows more efficient exploration of the problem search space. A set of extensive computational experiments indicate that the optimality gaps of the Adaptive Island evolutionary Algorithm do not exceed 1.93% for the considered small-size problem instances. Furthermore, the proposed solution algorithm was compared against the other state-of-the-art metaheuristic algorithms and exhibited statistically significant improvements in terms of the objective function values.

关键词： Maritime transportation Berth scheduling problem Optimization parallel evolutionary algorithms Island model Adaptive mechanism Migration

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

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

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：