A parallel firefly meta-heuristics algorithm for financial option pricing

In this paper we present a design and development of a parallel Firefly meta-heuristic algorithm for option pricing. We study the parallel algorithm for performance both theoretically and experimentally. Our implementation of the algorithm exhibits significant speedup for financial option pricing problem and demonstrates the utility of our parallel algorithm even when the problem size with large number of Fireflies is deployed. We also present a detailed analysis of the theoretical runtime cost of firefly algorithm on both the RAM and P-RAM models of computation. Moreover, we identify certain issues in the algorithm regarding global memory access pattern, which could be studied for further improvement.