Adapted Flower Pollination Algorithm for Lecturer-Class Assignment

Sapul, Ma. Shiela C.; Setthawong, Rachsuda; Setthawong, Pisal

doi:10.1109/icaiit.2019.8834620

Cited by 2 publications

(2 citation statements)

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“…FPA was originally designed to solve continuous optimization problems, by modifying the FPA; it is possible to solve a subset of the UCTP problem and promising results proves that FPA can be used effectively to solve a combinatorial problem like UCTP [15]. FPA also suffers from a lack of a good balance between exploration and exploitation optimization process which may lead to a non-diverse population.…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of this paper is to design and develop two new hybrid flower pollination algorithms: JFPA and DFPA, which improve the performance of adapted-flower pollination (AFPA) algorithm [15] in solving the lecturer-course assignment problem and extend our work to solving university course timetable problem (UCTP). AFPA has demonstrated that FPA can be adapted to solve a combinatorial problem.…”

Section: Introductionmentioning

confidence: 99%

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

Sapul¹,

Setthawong²,

Setthawong³

2020

IJEECS

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%