New hybrid flower pollination algorithm with dragonfly algorithm and jaccard index to enhance solving university course timetable problem

Abstract

University course timetable problem (UCTP) is one of the problems on which many researches have been conducted over the years because of its importance in academic institutions. A nature-inspired metaheuristic optimization algorithm, Flower Pollination Algorithm (FPA) has been adapted, so-called Adapted FPA (AFPA), to cope with UCTP in the previous work. However, AFPA suffers from the stagnation problem because of the non-diversity in the population. To improve the diversity of the population, this work introduces new Hybrid FPA with two variants: JFPA provided the Jaccard index to determine similarities among categorical data and the greedy selection mechanism to improve the selection of the random solution, and DFPA applied the navigational characteristics of the Dragonfly Algorithm (DA) to help in the neighborhood relationship. The results in this study indicate that the proposed algorithms have better exploration ability and fast convergence rate in comparison; JFPA outperforms AFPA in 3 out of 4 datasets for both small and large datasets, and DFPA outperforms AFPA and GA in all datasets while it outperforms PSO in 3 out of 4 small datasets and 2 complex large datasets.

Optimization in University Student Timetables: A Comprehensive Literature Review

“…[28] General framework with four layers model with Layer 1 to Layer 3 is called Stemming Phase involving preprocessing steps, and the Layer 4 is called Solution Finding Phase with enhanced simulated annealing-based search [29] Model leveraged by artificial bee colonies (ABC), cloud theory-based simulated annealing (CTB-SA), and genetic algorithm (GA) [18] Two phase hybrid local search algorithm with Tabu Search with Sampling and Perturbation with Iterated Local Search (TSSP-ILS) and Simulated Annealing with Reheating (SAR) with two preliminary runs (SAR-2P) [30] Two-stage heuristic algorithm consists of Lecturer Grouping Stage and Group Allocation Stage [33] Two hybrid variants of flower pollination algorithm (FPA) which were Jaccard FPA (JFPA) which uses the Jaccard index and a greedy selection mechanism, and Dragonfly FPA (DFPA) which incorporates the navigational traits of the dragonfly algorithm (DA) [34] Hybridization of Self-Adaptive and Simulated Annealing Hyper-Heuristic approach [35] Sequential constructive algorithm and Fuzzy Logic [36] Multi-Agent System (MAS) incorporating Integer Programming (IP)…”

“…Sapul, Setthawong, and Setthawong [34] presented two hybrid variants of the flower pollination algorithm (FPA) which were Jaccard FPA (JFPA) which uses the Jaccard index and a greedy selection mechanism, and Dragonfly FPA (DFPA) which incorporates the navigational traits of the dragonfly algorithm (DA) to enhance diversity and neighbourhood relationships in the population. The results showed that the proposed JFPA and DFPA algorithms offer better exploration ability and faster convergence than previous methods; JFPA outperformed AFPA in 3 out of 4 datasets, and DFPA outperformed Adapted FPA (AFPA), GA, and PSO in various datasets, including both small and large datasets.…”