Optimizing Personalized Commitment with Particle Swarm Optimization
Keywords:Housing Price, Particle Swarm Optimization, Personalized Commitment, Young Fresh Graduate.
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.
 Zainal Abidin Hashim. (2010). House Price and Affordability in Housing in Malaysia [ Harga Rumah dan Tahap Mampu Milik Rumah di Malaysia]. Akademika, 78, 37â€“46. Retrieved from http://pkukmweb.ukm.my/~penerbit/akademika/jademik.html
 Bujang, A. A., Jiram, W. R. A., Zarin, H. A., & Anuar, F. H. M. (2015). Measuring the Gen Y Housing Affordability Problem. International Journal of Trade, Economics and Finance, 6(1), 22â€“26. https://doi.org/http://dx.doi.org/10.7763/IJTEF.2015.V6.435
 JobStreet.com. (2014). Top 10 Best Paying Jobs in Malaysia. Retrieved December 14, 2016,
 Zairul, M. (2013). Housing dilemma among young starters in Malaysia. Elixir Online Journal, 58, 14923â€“14926.
 Johan, J., Yusof, A. M., & Chai, C. S. (2014). A Review of Housing Affordability in the City of Kuala Lumpur. 5th Annual Conference International Graduate Conference on Engineering, Science and Humanities 2014, (August 2014).
 Jumadi, N. (2010). The Relationship Between Demographic Factors and Housing Affordability. Malaysian Journal of Real Estate, 5(1), 49â€“58.
 Gan, Q., & Hill, R. J. (2009). Measuring housing affordability: Looking beyond the median. Journal of Housing Economics, 18(2), 115â€“125. https://doi.org/10.1016/j.jhe.2009.04.003
 Budget 2016 : More to be done for B40 households. (2015). Retrieved from http://www.freemalaysiatoday.com/category/nation/2015/10/15/budget-2016-more-to-be-done-for-b40-households
 Ling, O., Leh, H., Mansor, N. A., Nur, S., & Mohamed, A. (2016). The housing preference of young people in Malaysian urban areas : A case study Subang Jaya , Selangor, 7(7), 60â€“74.
 Tan, T.-H. (2012). Meeting first-time buyersâ€™ housing needs and preferences in greater Kuala Lumpur. Cities, 29(6), 389â€“396. https://doi.org/10.1016/j.cities.2011.11.016
 Ang, Y. (2012). Matching the Needs of Young First Time House Buyers In Urban Area, (April), 61. Retrieved from http://eprints.utar.edu.my/534/1/QS-0803939-01.pdf
 Thalmann, P. (2003). â€œHouse poorâ€ or simply â€œpoorâ€? Journal of Housing Economics, 12(4), 291â€“317. https://doi.org/10.1016/j.jhe.2003.09.004
 Kamal, E. M., Hassan, H., & Osmadi, A. (2016). Factors Influencing the Housing Price : Developers â€™ Perspective, 10(5), 1603â€“1609.
 Imran, M., Hashim, R., & Khalid, N. E. A. (2013). An overview of particle swarm optimization variants. Procedia Engineering, 53, 491-496.
 Yusoff, M., Ariffin, J., & Mohamed, A. (2015). DPSO based on a min-max approach and clamping strategy for the evacuation vehicle assignment problem. Neurocomputing, 148, 30-38.
 Yusoff, M., Shalji, S. M., & Hassan, R. (2014). Particle swarm optimization for single shear timber joint simulation. In InCIEC 2013 (pp. 117-126). Springer, Singapore.
 Imran, M., Hashim, R., & Khalid, N. E. A. (2012, September). Opposition based particle swarm optimization with student T mutation (OSTPSO). In Data Mining and Optimization (DMO), 2012 4th Conference on (pp. 80-85). IEEE.
 Shi, Y., & Eberhart, R. (1999). Empirical study of particle swarm optimization. In Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Service Center.
 Yusoff, M., Ariffin, J., & Mohamed, A. (2011, June). A multi-valued discrete particle swarm optimization for the evacuation vehicle routing problem. In International Conference in Swarm Intelligence (pp. 182-193). Springer, Berlin, Heidelberg.
 Eberhart, R. C., & Shi, Y. (2000). Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization. In Proceedings of the Congress on Evolutionary Computation (Vol. 1, pp. 84 - 88). La Jolla, CA.
 Das, S., Abraham, A., & Konar, A. (2008). Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives. Advances of Computational Intelligence in Industrial Systems, 116, 1-38.
View Full Article:
How to Cite
LicenseAuthors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under aÂ Creative Commons Attribution Licensethat allows others to share the work with an acknowledgement of the work''s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (SeeÂ The Effect of Open Access).