Magdalena Bialic-Davendra

Discrete Self-Organising Migrating Algorithm for flow-shop scheduling with no-wait makespan

Abstract This paper introduces a novel Discrete Self-Organising Migrating Algorithm for the task of flow-shop scheduling with no-wait makespan. The new algorithm is tested with the small and medium Taillard benchmark problems and the obtained results are competitive with the best performing heuristics in the literature.

Scheduling the Lot-Streaming Flowshop scheduling problem with setup time with the chaos-induced Enhanced Differential Evolution

The dissipative Lozi chaotic map is embedded in the Enhanced Differential Evolution (EDE) algorithm, as a pseudorandom generator. This novel chaotic based algorithm is applied to the constraint based Lot-Streaming Flowshop scheduling problem. Two new and unique data sets generated using the Lozi and Dissipative maps are used to compare the chaos embedded EDE (EDEC) and the generic EDE utilising the venerable Mersenne Twister. In total, 100 data sets were tested by the two algorithms, for the idling and the non-idling case, with the EDEC algorithm consistently outperforming the generic version.

Scheduling flow shops with blocking using a discrete self-organising migrating algorithm

A novel approach of a discrete self-organising migrating algorithm is introduced to solve the flowshop with blocking scheduling problem. New sampling routines have been developed that propagate the space between solutions in order to drive the algorithm. The two benchmark problem sets of Carlier, Heller, Reeves and Taillard are solved using the new algorithm. The algorithm compares favourably with the published algorithms Differential Evolution, Tabu Search, Genetic Algorithms and their hybrid variants. A number of new upper bounds are obtained for the Taillard problem sets.

Chaos Driven Evolutionary Algorithm for the Traveling Salesman Problem

Donald Davendra1∗, Ivan Zelinka1, Roman Senkerik2 and Magdalena Bialic-Davendra3 1Department of Informatics, Faculty of Electrical Engineering and Computing Science, Technical University of Ostrava, Tr. 17. Listopadu 15, Ostrava 2Department of Informatics and Artificial Intelligence, Faculty of Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, Zlin 76001 3Department of Finance and Accounting, Faculty of Management and Economics, Mostni 5139, Zlin 76001 Czech Republic

Utilising the chaos-induced discrete self organising migrating algorithm to solve the lot-streaming flowshop scheduling problem with setup time

The Dissipative Lozi chaotic map is embedded in the discrete self organising migrating algorithm (DSOMA), as a pseudorandom generator. This novel chaotic based algorithm is applied to the constraint based lot-streaming flowshop scheduling problem. Two new and unique data sets generated using the Lozi and Delayed Logistic maps are used to compare the chaos embedded DSOMA and the generic DSOMA utilising the venerable Mersenne Twister. In total, 100 data sets were tested by these two algorithms, for the idling and the non-idling case. From the obtained results, the chaos variant algorithm is shown to significantly improve the performance of generic DSOMA.

The age of clusters and its influence on their activity preferences

AbstractThe aim of this paper is to determine whether there exist age dependent differences in the orientation of clusters’ activities. The literature depicts different approaches to the cluster evolution process, highlighting that clusters are subject to a life cycle that emphasizes different sets of activities in various stages of their development. These activities appear to follow a certain trajectory, whereby the successful completion of initial less-intensive activities stimulates a shift in focus to more demanding, long-term projects. The presented research verifies that clusters can pass through different stages of development, and examines in detail their preferences for jointly-undertaken activities. Research, conducted on a sample of clusters of different countries and ages, was carried out through the use of questionnaires and structured interviews with cluster managers. It is a sample of so-called organized clusters, which have their own internal structure and which are characterized by consc...

Clustered enhanced differential evolution for the blocking flow shop scheduling problem

A novel clustered population paradigm is presented in this paper which is based on Chaos principles of edges and attractors. Convergence in evolutionary algorithms is viewed as a manifestation through cyclic dynamics and thus a new population is developed which is clustered and separated through new segregation bias rules. This population is embedded on the Enhanced Differential Evolution and the flow shop scheduling problem with blocking is solved. The two flow shop benchmark problems of Rec/Car/Hel and Taillard are solved with this new approach and the results favorably compared with published results in literature. A total of 49 new upper bounds for the Taillard problems was obtained during experimentation.

Scheduling The Flow Shop With Blocking Problem With The Chaos-Induced Discrete Self Organising Migrating Algorithm.

The dissipative Lozi chaotic map is embedded in the Discrete Self OrganisingMigrating Algorithm (DSOMA) algorithm, as a pseudorandom number generator (PRNG). This novel chaotic based algorithm is applied to the flow shop with blocking scheduling problem. The algorithm is tested on the Taillard problem sets and compared favourably with published heuristics.

Creative Clusters in Visegrad Countries: Factors Conditioning Cluster Establishment and Development

Abstract Since the accession of the Visegrad Group of countries (V4) to the European Union, the importance of clusters has increased. With growing global competitiveness and EU 12 trends, a gradual awareness of creative industries is observed in V4 countries. Therefore, this article analyses creative clusters and factors conditioning their establishment and development. On the basis of a literature review and a questionnaire survey, a mapping of creative clusters was conducted. In addition, catalysts, main motives and key factors in the process of their establishment were identified, as were the activities and factors hampering their development. The scheme of cluster development is presented as the outcome of the qualitative analysis, along with a comparison to findings of other studies. Research findings show that trust building and administrative obstacles are among the main barriers, especially for design clusters and cultural clusters.

CUDA Based Enhanced Differential Evolution: a Computational Analysis

General purpose graphic programming unit (GPGPU) programming is a novel approach for solving parallel variable independent problems. The graphic processor core (GPU) gives the possibility to use multiple blocks, each of which contains hundreds of threads. Each of these threads can be visualized as a core onto itself, and tasks can be simultaneously sent to all the threads for parallel evaluations. This research explores the advantages of applying a evolutionary algorithm (EA) on the GPU in terms of computational speedups. Enhanced Differential Evolution (EDE) is applied to the generic permutative flowshop scheduling (PFSS) problem both using the central processing unit (CPU) and the GPU, and the results in terms of execution time is compared. INTRODUCTION During the later part of the past decade, a novel trend emerged where programmers started using the Graphics Processing Unit (GPU) for programming not graphic applications which usually was in the preview of the Central Processing Unit (CPU). The reasoning behind such a move was the possibility to achieving speedups of magnitude compared to optimized CPU implementations. GPU’s have evolved into fast, highly multi-threaded processors, with hundreds of cores and thousands of concurrent threads. These threads which can be invoked simultaneously, provide an excellent platform for parallel execution. A GPU is optimal when a problem has to be executed many times, can be isolated as a function and works independently on different data. One of the most challenging and computational demanding problems in engineering are the NP-Hard problems. These problems are computationally intractable, and often require the use of optimization algorithms. This research attempts to solve the challenging flowshop scheduling (FSS) problem using a novel Enhanced Differential Evolution (EDE) algorithm utilizing GPU programming. One of the most widespread programming architectures is the Compute Unified Device Architecture (CUDA) of Nvidia (NVIDIA, 2012). A number of research has been conducted on GPU programming involving evolutionary algorithms and these two architectures. Tabu Search has been used for the evaluating the FSS problem using CUDA by Czapinski and Barnes (2011). Genetic Algorithms (GA) has been been used to solve the traveling salesman problem by Chen et al. (2011), whereas a parallel GA approach has been done by Pospichal et al. (2010). The particle swarm algorithm has also been modified to be used by CUDA Mussi et al. (2011). More interestingly Genetic Programming has also found a niche in GPU programming (Robilliard et al., 2009). This research utilizes the Nvidia CUDA framework for GPU computation. The enhanced Differential Evolution (EDE) (Davendra and Onwubolu, 2009) is modified to the GPU framework and execution time for both the GPU and CPU variants are compared. This paper follows the following structure. Section 1 outlines the CUDA framework and syntax. Section 2 describes Differential Evolution (DE) and the EDE algorithms. The problem attempted in this research; flow shop scheduling is given in Section 3. Section 4 describes the code design on the GPU, whereas the experimentation and analysis (Section 5) compares the obtained results. The paper is concluded in Section 6. Proceedings 26th European Conference on Modelling and Simulation ©ECMS Klaus G. Troitzsch, Michael Mohring, Ulf Lotzmann (Editors) ISBN: 978-0-9564944-4-3 / ISBN: 978-0-9564944-5-0 (CD)