Optimizing Personalized Commitment with Particle Swarm Optimization

Nowadays, in Malaysia, many fresh graduates cannot afford to buy a house. The price of house increases, especially in urban area. Even though the price of the house grew over the years, the house is a basic need of humanity. However, the increased cost of housing leads to unaffordability to buy a house, especially most of them are just starting their job and earned insufficient payment to support their life. As to this, they need to choose the most affordable house. The affordable house in this context is to own a reasonably-priced and will not affect their financial constraint while having other commitments. This paper addresses the employment of Particle Swarm Optimization with the aim to obtain an optimal personalized commitment value to secure an affordable house. The characteristics of the affordable house, identification particle representation, and fitness function were determined. The findings demonstrate that the Particle Swarm Optimization offered acceptable results to assist the young graduates on their personalized commitment to buy an affordable house. The outcome has great potential in assisting fresh graduate and the other people to make a better decision in choosing the affordable house without affecting the other commitment on the use of salary.