A TABU SEARCH FOR MULTIPLE MULTI-LEVEL REDUNDANCY ALLOCATION PROBLEM IN SERIES-PARALLEL SYSTEMS

The traditional RAP (Redundancy Allocation Problem) is to consider only the component redundancy at the lowest-level. A system can be functionally decomposed into system, module, and component levels. Modular redundancy can be more effective than component redundancy at the lowest-level. We consider a MMRAP (Multiple Multi-level Redundancy Allocation Problem) in which all available items for redundancy (system, module, and component) can be simultaneously chosen. A tabu search of memory-based mechanisms that balances intensification with diversification via the short-term and long-term memory is proposed for its solution. To the best of our knowledge, this is the first attempt to use a TS for MMRAP. Our algorithm is compared with the previous genetic algorithm for MMRAP on the new composed test problems as well as the benchmark problems from the literature. Computational results show that the tabu search outstandingly outperforms the genetic algorithm for all test problems

Stability Analysis and Design of Variable Step-Size P Algorithm Based on Fuzzy Robust Tracking of MPPT for Standalone/Grid Connected Power System

This research aims to design a modified P&O algorithm for the efficient tracking of maximum power point (MPPT) for standalone and grid-connected systems. The proposed research work modifies the P&O algorithm for the dc-dc converter where the fixed step size P&O algorithm is translated into variable step size with the help of ant colony optimization (ACO) to generate optimal parameters for the PID controller to generate a variable step size in the P&O algorithm. This variable step size is dependent upon the error that is the difference between the generated power and desired power. By doing this it improves the efficiency of the P&O algorithm and its limitations are overcome. Furthermore, the PV is extended to connect with a grid where the inverter is controlled by a fuzzy logic controller (FLC) so that the combined structure of variable P&O and fuzzy helps to achieve MPP efficiently. The robustness of the proposed work is compared with other state-of-the-art controllers to justify the effectiveness of the proposed work. Finally, a stability test of the system is carried out to verify the overall stability of the power system

Heuristiques efficaces pour l'optimisation de la performance des systèmes séries-parallèles

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Improving reliability of service oriented systems with consideration of cost and time constraints in clouds

Web service technology is more and more popular for the implementation of service oriented systems. Additionally, cloud computing platforms, as an efficient and available environment, can provide the computing, networking and storage resources in order to decrease the budget of companies to deploy and manage their systems. Therefore, more service oriented systems are migrated and deployed in clouds. However, these applications need to be improved in terms of reliability, for certain components have low reliability. Fault tolerance approaches can improve software reliability. However, more redundant units are required, which increases the cost and the execution time of the entire system. Therefore, a migration and deployment framework with fault tolerance approaches with the consideration of global constraints in terms of cost and execution time may be needed. This work proposes a migration and deployment framework to guide the designers of service oriented systems in order to improve the reliability under global constraints in clouds. A multilevel redundancy allocation model is adopted for the framework to assign redundant units to the structure of systems with fault tolerance approaches. An improved genetic algorithm is utilised for the generation of the migration plan that takes the execution time of systems and the cost constraints into consideration. Fault tolerant approaches (such as NVP, RB and Parallel) can be integrated into the framework so as to improve the reliability of the components at the bottom level. Additionally, a new encoding mechanism based on linked lists is proposed to improve the performance of the genetic algorithm in order to reduce the movement of redundant units in the model. The experiments compare the performance of encoding mechanisms and the model integrated with different fault tolerance approaches. The empirical studies show that the proposed framework, with a multilevel redundancy allocation model integrated with the fault tolerance approaches, can generate migration plans for service oriented systems in clouds with the consideration of cost and execution time

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available