Cloud Computing infrastructures and Grid Computing platforms are representatives of a new breed of systems that leverage the modularity paradigm to assemble large-scale dynamic applications from modules contributed by...
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Cloud Computing infrastructures and Grid Computing platforms are representatives of a new breed of systems that leverage the modularity paradigm to assemble large-scale dynamic applications from modules contributed by different, possibly untrustworthy providers. Increased susceptibility to faults, diminished accountability, and complex system configuration are major challenges when assembling and operating such systems. In this paper, we describe how to solve these problems by retrofitting module management systems with the ability to deploy modules to execution environments with adjustable degree of isolation. We give a formal definition of the underlying hierarchical Module Isolation Problem and devise an online algorithm to solve it in an incremental fashion. We discuss how to apply our approach to a state-of-the-art module management system and demonstrate its effectiveness by an experimental evaluation.
multilevel lot siZing (MLLS) problem is a combinational optimization problem which has been proved NP-hard without restrictive assumption on the product structure. Several evolutionary algorithms were developed to sol...
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A simple but powerful design method based on real-coded Genetic algorithms (GAs) to solve the minimization of the Total Harmonic Distortion (THD) criterion is obtained. GAs provides a much simpler approach to off-line...
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Spare optimization models for systems with components connected in series and parallel are presented. The component availability at any time is obtained through Poisson process theory and the availability of the serie...
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To find MST (Minimum Spanning Trees) in complete graph is a classical problem in operation research having network design as an important application. It is possible to solve MST problem efficiently, but its Biobjecti...
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For improving the intersection traffic capacity and reducing the vehicle emission, the solution that aim at the multi-object optimization was presented by using genetic algorithm (GA), and urban traffic microscopic si...
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The evolutionary algorithms are methods of search of solutions that are based in the natural beginning of the evolution. Inside the evolutionary algorithms, we can find both the AG (Genetic algorithms) and PSO (Partic...
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ISBN:
(纸本)9780415566971
The evolutionary algorithms are methods of search of solutions that are based in the natural beginning of the evolution. Inside the evolutionary algorithms, we can find both the AG (Genetic algorithms) and PSO (Particle Swarm optimization), being one of many applications that present these algorithms the design of sewer systems. The work presents the application of these methods to the design of sewer systems. The design of these networks with evolutionary algorithms is of great interest if there is had present that allows us to choose a solution between the different alternatives that verify the conditions imposed for the pipes and the slope of the area. Finally, we realize an analysis of the obtained results, comparing both methods and extracting the possible advantages and disadvantages of applying each of these methods in the design of water sewer systems.
Multiprocessor systems-on-chips (MPSoCs) are defined as one of the main drivers of the industrial semiconductors revolution. They are good candidates for systems and applications such as multimedia. Memory is becoming...
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The matching design problem of the ship-engine-propeller is a non-linear constrained multi-objective optimization problem which is performed based on multiple objectives,such as system efficiency and the life cycle co...
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Simulated Bee Colony algorithms model the behavior of honey bees and can be used to find approximate solutions to difficult combinatorial optimization problems. One of the disadvantages of algorithms based on the beha...
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ISBN:
(纸本)9781617820267
Simulated Bee Colony algorithms model the behavior of honey bees and can be used to find approximate solutions to difficult combinatorial optimization problems. One of the disadvantages of algorithms based on the behavior of natural systems is that these algorithms typically require the specification of several parameter values which are often highly sensitive with regards to their effect on solution convergence and accuracy. This study presents the results of the first known systematic investigation of Simulated Bee Colony parameter values. Experimental results targeting benchmark traveling salesman problem data sets suggest that 1.) the optimal percentages of active bees, inactive bees, and scout bees are approximately 75%, 10%, and 15% respectively;2.) the optimal probability that an inactive bee will be persuaded to accept the food source location of an active worker or scout bee is between 0.90 and 0.95;3.) the optimal probability that an active worker bee or a scout bee will accept a neighbor food source location with a worse measure of solution accuracy is between 0.001 and 0.005;4.) the optimal maximum number of times an active worker bee should visit a particular food source location solution is proportional to the number, Se, of possible neighbor solutions to any given solution and is between Se and 10 * S e.
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