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distributed evolutionary algorithms for simulation optimization

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2000年第1期30卷 15-24页

作者： Pierreval, H Paris, JL French Inst Advanded Mech Engn Lab Informat Modelisat & Optimisat Syst Equipe Rech Syst Prod F-63175 Aubiere France

The optimization of such complex systems as manufacturing systems often necessitates the use of simulation. Unfortunately, most current existing methods for simulation optimization present certain major inconveniences. In particular, they generally cannot take into account nonnumerical variables (e.g., dispatching rules), they may be sensitive to local extremums and they are often time consuming. In this paper, the use of evolutionary algorithms is suggested for the optimization of simulation models. Several types of variables are taken into account. The reduction of computing cost is achieved through the parallelization of this method, which allows several simulation experiments to be run simultaneously. Emphasis is put on a distributed approach where several computers manage both their own local population of solutions and their own simulation experiments, exchanging solutions using a migration operator. After a first evaluation through a mathematical function with a known optimum, the benefits of this new approach are demonstrated through the example of a transport lot sizing and transporter allocation problem in a manufacturing flow shop system, which is solved using a distributed software implemented on a network of eight Sun workstations.

关键词： distributed evolutionary algorithms lot sizing manufacturing systems simulation-optimization

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distributed evolutionary algorithms to TSP with Ring Topology

Distributed Evolutionary Algorithms to TSP with Ring Topolog...

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4th International Symposium on Intelligence Computation and Applications

作者： Huang, Shanzhi Zhu, Li Zhang, Fang He, Yongxiang Xue, Haidong China Univ Geosci Fac Comp Sci Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9783642049613

distributed evolutionary algorithms (dEAs) are stochastic search methods applied successfully in many searches, optimization, and machine learning problems. This paper proposes a dEA based on IGT. The dEA uses a ring topology,with each population choosing a random individual from it in a migration interval. The individual will be sent to next population and replace the worst individual of the population. It can be seen from the experiment that dEA with Ring transmission can produce better solutions when the population size of a TSP problem is large enough. The numerical results show the utility, versatility, efficiency and potential value of the proposed distributed evolutionary algorithm.

关键词： TSP distributed evolutionary algorithms PVM cluster computing ring topologies

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Strategy for Individuals Distribution by Incident Nodes Participation in Star Topology of distributed evolutionary algorithms

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CYBERNETICS AND INFORMATION TECHNOLOGIES 2016年第1期16卷 80-88页

作者： Balabanov, Todor Zankinski, Iliyan Barova, Maria BAS Inst Informat & Commun Technol Sofia 1113 Bulgaria

One of the strongest advantages of distributed evolutionary algorithms (DEAs) is that they can be implemented in distributed environment of heterogeneous computing nodes. Usually such computing nodes differ in hardware and operating systems. distributed systems are limited by network latency. Some evolutionary algorithms (EAs) are quite suitable for distributed computing implementation, because of their high level of parallelism and relatively less intensive network communication demands. One of the most widely used topologies for distributed computing is the star topology. In a star topology there is a central node with global EA population and many remote computation nodes which are working on a local population (usually sub-population of the global population). This model of distributed computing is also known as island model. What is common for DEAs is an operation called migration that transfers some individuals between local populations. In this paper, the term 'distribution' will be used instead of the term 'migration', because it is more accurate for the model proposed. This research proposes a strategy for distribution of EAs individuals in star topology based on incident node participation (INP). Solving the Rubik's cube by a Genetic Algorithm (GA) will be used as a benchmark. It is a combinatorial problem and experiments are done with a C++ program which uses OpenMPI.

关键词： distributed evolutionary algorithms migration strategy incident nodes participation

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NOW-Based distributed Implementation of evolutionary algorithms

NOW-Based Distributed Implementation of Evolutionary Algorit...

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2001 International Symposium on distributed Computing and Applications to Business, Engineering and Science

作者： Xiong Shengwu~1 Chu Wujun~2 Institute of Computer Science and Technology,Wuhan University of Technology Wuhan,Hubei,430070,China

This paper mainly discusses the application of MPI in distributed evolutionary compulation on network of *** also introduces how to develop a parallel evolutionary algorithms program in distributed computing environment based on MPI and how to exploit computing resource of network of workstations.A computing model to solve the problems of function optimization using island model distributed evolutionary algorithms is constructed to demonstrate the effectiveness of the proposed model.

关键词： distributed evolutionary algorithms Message Passing Interface Abstract Device Interface Network of Workstations

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Hierarchical distributed metamodel-assisted evolutionary algorithms in shape optimization

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS 2007年第3期53卷 455-469页

作者： Karakasis, Marios K. Koubogiannis, Dimitrios G. Giannakoglou, Kyriakos C. Natl Tech Univ Athens Sch Mech Engn Lab Thermal Turbomachines Athens Greece

In aerodynamic shape optimization, the availability Of multiple evaluation models of different precision and hence computational cost can be efficiently exploited in a hierarchical evolutionary algorithm. Thus, in this work the demes of a distributed evolutionary algorithm are ordered in levels, with each level employing a different flow analysis method, giving rise to a hierarchical distributed scheme. The arduous task of exploring the design space is undertaken by deities consisting the lower hierarchy level, which use a low-cost flow analysis tool, namely a viscous-inviscid flow interaction method. promising solutions are directed towards the higher level, where these are further evolved based on a high precision/cost evaluation tool, viz. a Navier-Stokes equations solver. The final, optimal solution is obtained front the highest hierarchy level. At each level, metamodels, trained on-line on the outcome of evaluations with the level's analysis tool, are used. The role of metamodels is to allow a parsimonious use of computational resources by filtering the poorly performing individuals in each deme. The entire algorithm has been implemented so as to take advantage of a parallel computing system. The efficiency and effectiveness of the proposed hierarchical distributed evolutionary algorithm have been assessed in the design of a transonic isolated airfoil and a compressor cascade. Remarkable superiority over the conventional evolutionary algorithms has been monitored. Copyright (c) 2006 John Wiley & Sons, Ltd.

关键词： distributed evolutionary algorithms hierarchical optimization metamodels

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An Asynchronous distributed Cooperative Coevolutionary Algorithm for Multilayer Influence Maximization

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Yang, Guo Wei, Feng-Feng Hu, Xiao-Min Jeon, Sang-Woon Zhang, Jun Chen, Wei-Neng South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510006 Peoples R China South China Univ Technol State Key Lab Subtrop Bldg & Urban Sci Guangzhou 510006 Peoples R China Guangdong Univ Technol Dept Comp Sci Guangzhou 510006 Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea Nankai Univ Coll Artificial Intelligence Tianjin 30071 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea

The influence maximization (IM) problem in large-scale social networks has attracted great attention. Considering the interactions among multiple online social platforms, the multilayer IMproblem poses further challenges (i.e., high-simulation burden and low-optimization quality). To solve these problems, this article proposes a susceptible-exposed-infected1-infected2-infected12-vigilant (SE3IV) model to simulate the information spreading process in multilayer networks. The spreading dynamic is modeled by mean-field equations considering the effect of cross-layer propagation. To optimize the multilayer information maximization modeled by SE3IV, an asynchronous distributed cooperative coevolutionary algorithm (ADCA) is proposed. To improve the efficiency of the algorithm in multilayer networks, the multilayer community detection first decompresses the network into a single layer by dimension-based method. Then, the Louvain method is adopted to decompose the problems into subcomponents with lower dimensionality. The populations with the same size evolve corresponding subcomponents in an asynchronous and distributed way based on the pool model. Besides, an asynchronous communication mechanism is devised to manage the communication among the shared pool. An adaptive seeds regulation strategy is designed to adjust the number of seeds of subcomponents. Numerous experiments on different networks show that ADCA possesses good scalability and efficiency, especially in large-scale networks.

关键词： Nonhomogeneous media Social networking (online) Computational modeling Mathematical models Integrated circuit modeling Optimization Heuristic algorithms Search problems Scalability Regulation distributed evolutionary algorithms distributed optimization influence maximization (IM)

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Evolvable agents, a fine grained approach for distributed evolutionary computing: walking towards the peer-to-peer computing frontiers

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SOFT COMPUTING 2008年第12期12卷 1145-1156页

作者： Laredo, J. L. J. Castillo, P. A. Mora, A. M. Merelo, J. J. Univ Granada ETSIIT Dept Architecture & Comp Technol Granada Spain

In this work we propose a fine grained approach with self-adaptive migration rate for distributed evolutionary computation. Our target is to gain some insights on the effects caused by communication when the algorithm scales. To this end, we consider a set of basic topologies in order to avoid the overlapping of algorithmic effects between communication and topological structures. We analyse the approach viability by comparing how solution quality and algorithm speed change when the number of processors increases and compare it with an Island model based implementation. A finer-grained approach implies a better chance of achieving a larger scalable system;such a feature is crucial concerning large-scale parallel architectures such as peer-to-peer systems. In order to check scalability, we perform a threefold experimental evaluation of this model: first, we concentrate on the algorithmic results when the problem scales up to eight nodes in comparison with how it does following the Island model. Second, we analyse the computing time speedup of the approach while scaling. Finally, we analyse the network performance with the proposed self-adaptive migration rate policy that depends on the link latency and bandwidth. With this experimental setup, our approach shows better scalability than the Island model and a equivalent robustness on the average of the three test functions under study.

关键词： distributed evolutionary algorithms peer-to-peer systems

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An ensemble-based evolutionary framework for coping with distributed intrusion detection

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2010年第2期11卷 131-146页

作者： Folino, Gianluigi Pizzuti, Clara Spezzano, Giandomenico Natl Res Council CNR Inst High Performance Comp & Networking ICAR I-87036 Arcavacata Di Rende CS Italy

A distributed data mining algorithm to improve the detection accuracy when classifying malicious or unauthorized network activity is presented. The algorithm is based on genetic programming (GP) extended with the ensemble paradigm. GP ensemble is particularly suitable for distributed intrusion detection because it allows to build a network profile by combining different classifiers that together provide complementary information. The main novelty of the algorithm is that data is distributed across multiple autonomous sites and the learner component acquires useful knowledge from this data in a cooperative way. The network profile is then used to predict abnormal behavior. Experiments on the KDD Cup 1999 Data show the capability of genetic programming in successfully dealing with the problem of intrusion detection on distributed data.

关键词： Intrusion detection Ensemble classifiers distributed evolutionary algorithms

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distributed multi-objective evolutionary optimization using island-based selective operator application

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APPLIED SOFT COMPUTING 2019年 85卷 105757-000页

作者： Garcia-Sanchez, P. Ortega, J. Gonzalez, J. Castillo, P. A. Merelo, J. J. Univ Cadiz Dept Comp Sci & Engn ESI Cadiz Spain Univ Granada Dept Comp Architecture & Technol ETSIIT CITIC Granada Spain

Parallel island-model co-evolutionary algorithms are well-known methods, suitable for dealing with large multi-objective optimization problems. This paper proposes a version of these algorithms where each island modifies a fragment of the chromosome that encodes a possible solution to the problem. The objective of this paper is to demonstrate that automatically setting the size of the overlapping fragments depending on the number of islands obtains better results than using a fixed overlapping size. This method has been compared to other parallel evolutionary techniques considering a different number of islands, chromosome sizes and benchmarks. The analysis of the obtained experimental results, by using different metrics, shows that our approach can provide statistically significant improvements with respect to the base algorithm in high-dimensional, un-decomposable, multiobjective problems. This opens a very promising line to automatically adapt the overlapping sizes in this kind of algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-objective algorithms NSGA-II distributed evolutionary algorithms Island model

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Configuration of Multiproduct Kanban Systems Using a distributed evolutionary Algorithm

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IFAC Proceedings Volumes 1997年第19期30卷 167-172页

作者： J.L. Paris H. Pierreval Laboratoire inter-établissements d'Informatique de Modélisation et d'Optimisation des Systèmes Equipe de Recherche en Systèmes de Production de l'Institut Français de Mécanique Avancée I.F.M.A. - B.P. 265 F-63175 Aubière Cedex phone: +(33) 04-73-28-80-00 Fax: +(33) 04-73-28-81-00

Performances of multiproduct Kanban systems are strongly dependent on a set of parameters, such as the number of kanbans between machines, the transport lot sizes, the safety storage sizes and the sequencing rules. Configuring such systems consist of determining a value for each parameter in order to optimise a production performance criterion. Recent works have shown that simulation optimisation of Kanban systems can be efficientlY addressed using evolutionary algorithms. We propose a distributed evolutionary optimisation approach, which can manage several searches for solutions simultaneously. This new approach speeds up the search of the optimal value and enlarges the explored search space, giving therefore a better chance to find the optimal solution. The benefits of this approach are illustrated through the example of a multiproduct Kanban system.

关键词： Kanban systems simulation optimisation distributed evolutionary algorithms

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