Awareness Level and Adoption of Modular Construction for Affordable Housing in Nigeria: Architects’ Perspective
Sholanke A. B.1, Opoko A. P.2, Onakoya A. O.3, Adigun T. F.4
1Sholanke A. B. is an Architect and a Lecturer at Covenant University, Ota, Ogun State, Nigeria.
2Opoko A. B. is an Architect and a Senior Lecturer at Covenant University, Ota, Ogun State, Nigeria.
3Onakoya A. O. is a graduate Architect currently undergoing her masters at Covenant University, Ota, Ogun State, Nigeria.
4Adigun T. F. is a graduate Architect currently undergoing her masters at Covenant University, Ota, Ogun State, Nigeria.
Manuscript received on 29 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 251-257 | Volume-8 Issue-9, July 2019 | Retrieval Number: JI8113078919/19©BEIESP | DOI: 10.35940/ijitee.I8113.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The work makes an examination of clustering methods in a multi-agent system which is fully decentralized. This has the goal of grouping agents that have similar data or objectives as in the case of traditional clustering. But, this adds to some more additional constraints wherein the agent will have to be in the same place as opposed to being collected within a centralized database. For doing this, it will connect to agents within a random network and will search for them in a peer-to-peer based fashion for the other agents that are similar. The primary aim here was to tackle the basic problem in clustering on the Internet scale thus creating methods where the agents may be grouped thus forming coalitions. For the purpose of investigating the decentralized approaches and their feasibility, the work presents the K-means clustering, the multi-agent Firefly Algorithm, (FA) and the Differential Evolution (DE). This is done for a reasonable number to times and will be surprisingly good. The results of the experiment prove that the multi-agent firefly clustering has better performance compared to that of a multi-agent K-Means clustering or a multi-agent DE clustering.
Keywords: Clustering, Data Mining, Differential Evolution (DE), Multi Agent Systems (MAS), K-Means Clustering, Clustering and Firefly Algorithm (FA).
Scope of the Article: Data Mining