Comparative analysis of Particle Swarm Optimisation & Cultural Algorithm in the Prediction of Foreign Exchange
Indian Banks’ buying and selling rates of foreign currency is directly linked to the daily exchange rates issued by Reserve Bank of India. Foreign exchange (Forex) market is essentially the world’s largest financial market. Forex data therefore, is of paramount importance even to an average consumer. The rate at which our domestic currency can be traded for in the global forex market decides the price we pay for daily consumables along with the interest rate on our loans and the money we spend on our vacations and shopping sprees. Hence, it makes sense to be able to predict the ups and downs of exchange rates. Whether you’re businessman or a trader, a home maker or a worker, it is for forex data to helps us lessen risks and boost returns. Previously, many AI techniques have been implemented for the prediction of exchange rates. This paper shows a comparative study of Particle Swarm Optimisation (PSO) technique & Cultural Algorithm (CA) to predict and optimise our results. PSO is a stochastic optimisation technique inspired by special behaviour of bird flocking and fish schooling. CA, on the other hand, is an arm of evolutionary computation where there is a knowledge component that is called the belief space in addition to the population component. Comparatively, CA based technique yields better results.