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Modified dynamic opposite learning assisted TLBO for solving Time-Cost optimization in generalized construction projects by Mohammad Azim Eirgash; Vedat Toğan; Tayfun Dede; Hasan Basri Başağa is a Engineering article available to read on EtoBox.

What is Modified dynamic opposite learning assisted TLBO for solving Time-Cost optimization in generalized construction projects about?

This study proposes an improved version of teaching learning-based optimization (TLBO) with a modified dynamic-opposition learning (MDOL) strategy, called modified dynamic oppositional learning TLBO (MDOLTLBO) to narrow the gap between research and practice for generalized time–cost optimization. Instead of opposite numbers, this study employs modified dynamic opposite points for the population initialization and generation jumping. This novel feature of the proposed algorithm is intended to diminish the randomness of the initial population and minimize the search effort required to achieve the optimal set of time–cost alternatives in the search space. An iterative-based varying weighting factor for MDOL is introduced to adjust the range of the search space with the aim of balancing the dynamicity and diversity of the algorithm. Hence, the MDOLTLBO algorithm can identify diversified and high-quality solutions based on non-dominated solutions. Four different case studies of real construction projects, ranging from 29 to 290 activities, were investigated to demonstrate the applicability of the proposed model. The experimental findings verify that the proposed algorithm is efficient,

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Author

Mohammad Azim Eirgash; Vedat Toğan; Tayfun Dede; Hasan Basri Başağa

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)