A multi-objective improved teaching learning based optimization algorithm for time-cost trade-off problems

OpenAlex İngilizce

In this paper, a multi-objective optimization model based on modified adaptive weight approach and improved teaching-learning based optimization (MAWA-ITLBO) algorithm is proposed for the solution of time-cost trade-off problems. The MAWA-ITLBO algorithm is the improved version of basic MAWA-TLBO algorithm by adding the concept of number of teachers as well as adaptive teaching factor. The effects of these parameters in TLBO are investigated in order to demonstrate the variation of the Pareto front solution. Thereby, the performance of the MAWA-ITLBO is compared to the existing methods using a wellknown 18-activity benchmark problem. A 63-activity problem is also included in computational experiments to validate the efficiency of the proposed MAWA-ITLBO. The results obtained by using the MAWA-ITLBO are compared with those obtained by using the basic MAWA-TLBO, genetic algorithm (GA), and ant colony optimization (ACO) algorithms. The obtained results demonstrate that the utilized MAWA-ITLBO is able to provide a superior set of Pareto-front solutions than that of previously proposed models.