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

Abstract

Researchers conducted experiments on parallel algorithms, which are inspired by the clonal selection, called Clonal Selection Algorithm (CSA). This algorithm is a population-based heuristic solution. Course-grained parallelism model applied to improve the execution time. Inter-process communication overhead is addressed by adjusting the communication frequencies and size of data communicated. In this research, conducted experiments on six parallel computing models represent all possible partitions and communications. Experiments conducted using data of NP-Problem, Traveling Salesman Problem (TSP). The algorithm is implemented using the model of message passing libraries using MPJExpress. Experiments conducted in a cluster computation environment. Result shows the best parallelism model is achieved by partitioning the initial population data at the beginning of communication and the end of generation. Communication frequency can be up to per 1% of the population size generated. Using four dataset from TSPLib, this reseache shows effect of the communication frequency that increased the 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 the execution time 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 the execution time, and also the best cost. It improved execution time and best cost.