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Handling Large Datasets in Parallel Metaheuristics: A Spares Management and Optimization Case Study

Handling Large Datasets in Parallel Metaheuristics: A Spares Management and Optimization Case Study,10.1109/WAINA.2011.112,Chee Shin Yeo,Elaine Wong K

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Handling Large Datasets in Parallel Metaheuristics: A Spares Management and Optimization Case Study  
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Parallel metaheuristics based on Multiple Inde- pendent Runs (MIR) and cooperative search algorithms are widely used to solve difficult optimization problems in diverse domains. A key step in assessing and improving the speed of global convergence of parallel metaheuristics is tracing solutions explored by the MIR-based algorithm. However, this generates large amounts of data, thus posing execution problems. This problem can be resolved by using a flow control workflow to govern the execution of the MIR-based parallel metaheuristics. Using a Spares Management and Optimization case study for the logistics industry, this paper analyzes the performance of the flow control workflow with different problem sizes. We show that by appropriately setting workflow parameters, namely: (1) stop criterion to limit the amount of data cached and exchanged, and (2) clustering policy to distribute/aggregate parallel processes to compute nodes selectively, the performance of the algorithm can be improved. The use of metaheuristics to solve problems in a wide range of domains has been exceedingly popular. Meta- heuristics unlike exact methods and heuristics exhibit two distinctive features. Firstly, random modifications (either from a population of possible solutions or around the neighbourhood of current solutions) are involved in deriving the approximated optimal solution. Secondly, heuristics are applied specifically on the solution space based on a belief of the topology of the space, as opposed to applying domain- specific heuristics. Because of the domain independence, metahueristics have been successfully applied to solve many different types of difficult optimization problems. These works have been described and compared in (1), (2), (3), (4), (5), laying ground for more advanced applications and performance improvements.
Conference: Advanced Information Networking and Applications - AINA , pp. 261-266, 2011
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