INFLUENCE OF JUMPING RATE ON OPPOSITION-BASED JAYA ALGORITHM FOR DISCRETE TIME COST TRADE-OFF OPTIMIZATION PROBLEMS

This paper aims to develop a new multi-objective optimization algorithm for handling construction time-cost trade-off problems (TCTPs). An intelligent strategy called opposition-based learning (OBL) is incorporated into the Jaya algorithm, resulting in the opposition-based Jaya Algorithm (OBJA). The proposed model introduces an innovative approach to opposition-based optimization by employing an iterative-based varying oppositional jumping rates. This adaptive strategy significantly contributes to increased population diversity and effective avoidance of local optima throughout both the initialization and generational phases of the optimization process. By systematically varying the opposition jumping rate, its impact on the algorithm's convergence speed, solution quality, and computational efficiency are evaluated. The experimental results demonstrate that an iterative-based varying opposition jumping rate significantly enhances OBJA's efficiency to explore and exploit the search space, leading to superior trade-off solutions. Hence, computational experiments on 9 and 19 activity problems reveal that an iterative-based varying opposition jumping rate result in high quality solution with reduced number of function evaluations. Furthermore, the OBJA model proved to be more successful than the non-dominated sorting GA (NSGA-II), multi-objective particle swarm optimzaiton (MOPSO), and plain Jaya algorithm for handling these complex TCTPs in construction project management.