Data Partition and Communication on Parallel Heuristik Model Based on Clonal Selection Algorithm

Abstract: This research conducted experiments on population-based heuristic parallel algorithms, which are inspired by the clonal selection, called Clonal Selection Algorithm (CSA). Course-grained parallelism model applied to improve execution time. Inter-process communication overhead is addressed byadjusting communication frequencies and size of data communicated. Experiments on six parallelcomputing models represent all possible partitions and communications and using data of NP-Problem,Traveling Salesman Problem (TSP). The algorithm is implemented using model of message passinglibraries MPJExpress and ran in a cluster computation environment. Result shows the best parallelismmodel is achieved by partitioning initial population data at the beginning of communication and the end of generation. Communication frequency can be up to per 1% of a population size generated. Using four dataset from TSPLib, experiments show the effect of communication frequency that increased best cost, from 44.16% to 87.01% for berlin52.tsp; from 9.61% to 53.43% for kroA100.tsp, and from 12.22% to 17.18% for tsp225.tsp. With eight processors, using communication frequency will be reduced executiontime e.g 93.07%, 91.60%, 89.60%, 74.74% for burma14.tsp, berlin52.tsp, kroA100.tsp, and tsp225.tsp respectively. We conclude that frequency of communication greatly affects execution time, and also best cost. It improved execution time and best cost.

Keywords: clonal selection algorithm, parallel clonal selection algorithm, parallel heuristic model, data partition, coarse-grained communication, traveling salesman problem, message passing interface, MPJExpress

Author: Ayi Purbasari

Journal Code: jptkomputergg150028

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