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Journal of Engineering and Applied Sciences

ISSN: Online 1818-7803
ISSN: Print 1816-949x
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Multi-Parameter Optimization of Cost Entropy for Reinforced Concrete Office Building Projects using Ant Colony Optimization

M. Amusan Lekan, K. Ayo Charles, D. Owolabi James, P. Tunji-Olayeni and Ogunde Ayodeji
Page: 5018-5023 | Received 21 Sep 2022, Published online: 21 Sep 2022

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https://doi.org/10.36478/jeasci.2017.5018.5023

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Abstract

Ant colony optimization is one of the conventional techniques used in providing solution to multi-objective optimization situations where pareto optimal solution is desirable. The main aim of this research work is to develop an optimization system for cost-entropy optimization logic and this study has demonstrated succinctly the applicability of ant colony optimization algorithm in solving multi-conflicting objectives situation in cost entropy trade-off of reinforced concrete office building projects. Average costs of (14) elements of (20) selected projects were used for the analysis. The ant colony system devised was applied on 14 in 20 elements of the sampled projects. An optimal solution obtained was validated within the context of previous developed algorithm and was found to be consistent at prevailing currency equivalent. The model with entropy scale developed in this study would enable a builder or contactor load iteratively, cost implication of an unseen circumstance even on occasion of deferred cost reimbursement and would help in project cost monitoring. The phenoromone type of cost and entropy generated can be spread iteratively on element’s cost so as to forestall overrun and economic variants that can affect project cost negatively.

How to cite this article:

M. Amusan Lekan, K. Ayo Charles, D. Owolabi James, P. Tunji-Olayeni and Ogunde Ayodeji. Multi-Parameter Optimization of Cost Entropy for Reinforced Concrete Office Building Projects using Ant Colony Optimization.
DOI: https://doi.org/10.36478/jeasci.2017.5018.5023
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2017.5018.5023