Performance of Shuffled Frog-Leaping Algorithm in Finance-Based Scheduling

Abstract

Currently, meta-heuristics including the genetic algorithms (GA) and simulated annealing (SA) have been used extensively to solve non-deterministic polynomial-time hard (NP-hard) problems. Continued efforts of researchers to upgrade the performance of the meta-heuristics in use resulted in the evolution of new ones. Shuffled frog-leaping algorithm (SFLA) is one of the recently introduced heuristics. The few applications of the SFLA in the literature in different areas demonstrated the capacity of the SFLA to provide high-quality solutions. The main objective of this paper is to further bring the SFLA to the attention of researchers as a potential technique to solve the NP-hard combinatorial problem of finance-based scheduling. The performance of the SFLA is evaluated through benchmarking its results against those of the GA and SA. The traditional problem of generating infeasible solutions in scheduling problems is adequately tackled in the implementations of the GA, SA, and SFLA. Fairly large projects of 120 and 210 activities are used to compare the performance of the three meta-heuristics. Finally, the obtained results indicate that the SFLA improved the quality of solutions with a substantial reduction in the computational time.