A Hybrid Swarm-Based Approach to University Timetabling

Fong, Cheng Weng; Asmuni, Hishammuddin; McCollum, Barry

doi:10.1109/tevc.2015.2411741

Cited by 39 publications

(13 citation statements)

References 60 publications

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“…Conventional PSO often suffers from low exploration ability and premature convergence, especially for very high complex multimodal problems [ 34 ]. The hybrid strategies have been widely accepted to increase the performance of algorithms for obtaining better solutions [ 35 ]. According to a literature survey on Scopus database using “course timetable*” and “particle swarm” as keywords for document search in data records (e.g., article title, abstract, and keywords) covering the period from the last two decades, several variants of PSO and their hybridisations for solving the UCTP are reported in Table 1 .…”

Section: Pso Variants and Literature Reviewmentioning

confidence: 99%

Particle Swarm Optimisation Variants and Its Hybridisation Ratios for Generating Cost-Effective Educational Course Timetables

2021

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Due to the COVID-19 pandemic, many universities across the globe are unexpectedly accelerated to face another major financial crisis. An effective university course timetabling has a direct effect on the utilisation of the university resources and its operating costs. The university course timetabling is classified to be a Non-deterministic Polynomial (NP)-hard problem. Constructing the optimal timetables without an intelligence timetabling tool is extremely difficult task and very time-consuming. A Hybrid Particle Swarm Optimisation-based Timetabling (HPSOT) tool has been developed for optimising the academic operating costs. In the present study, two variants of Particle Swarm Optimisation (PSO) including Standard PSO (SPSO) and Maurice Clerc PSO (MCPSO) were embedded in the HPSOT program. Five combinations of Insertion Operator (IO) and Exchange Operator (EO) were also proposed and integrated within the HPSOT program aimed at improving the performance of the proposed PSO variants. The statistical design and analysis indicated that five combination results of IO and EO for hybrid SPSO and MCPSO were significantly better than those obtained from the original PSO variants for all eleven problem instances. The average computational times taken by the proposed hybrid methods were also faster than the conventional SPSO and MCPSO for all cases.

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Section: Pso Variants and Literature Reviewmentioning

confidence: 99%

Particle Swarm Optimisation Variants and Its Hybridisation Ratios for Generating Cost-Effective Educational Course Timetables

2021

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“…Novel approaches include hybrid approaches, fuzzy logic approaches and MAS. Hybrid approaches employ different methods to solve a problem in order to alleviate the weakness of a single method [15,16]. Fuzzy logic approaches aim to solve those problems which are hard to be quantitated and modelled as they do not have a precisive classification [17,18].…”

mentioning

confidence: 99%

“…To address this issue, this paper chooses a common real-world school timetabling scenario to study and takes educators' preferences towards units (also known as 'courses' in some literature) and the corresponding expertise level into account. Based on the chosen scenario, this paper presents a model of the STP and proposes a modified ABC algorithm to solve the problem because the ABC algorithm has successfully solved ETPs in the course [32,33] and examination [15,34,35] timetabling fields. In addition, this paper introduces a novel VSS method to reduce the search space.…”

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confidence: 99%

School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

Zhu¹,

Li²

2021

Preprint

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Although educational timetabling problems have been studied for decades, one type, the STP, has not developed as quickly as the other two types due to its diversity and complexity. Also, most of the STP research has only focused on the educators’ availabilities when studying the educator aspect, and the educators’ preferences and expertise have not been taken into consideration. This paper proposes a conceptual model for the school timetabling problem considering educators’ availabilities, preferences and expertise as a whole, and chooses a common real-world school timetabling scenario to study. A mathematical model is presented. A Virtual search space for dealing with the large search space is introduced, and the artificial bee colony algorithm is adapted and applied to the proposed model. The proposed approach is simulated with a random-generated large dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computer hardware environment, which significantly reduces computational costs. Compared to the traditional CP method, the proposed approach is more effective and can provide more satisfactory solutions in considering educators’ availabilities, preferences, and expertise levels.

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“…De tal manera que, en cada nueva ejecución o generación de solución, la técnica comience en un punto distinto [32]. Además de implementar mecanismos que generen cambios significativos en la estructura del candidato, con la intención de dotar a la estrategia la capacidad de explorar diferentes regiones a la original [36].…”

Section: Explotación Y Exploraciónunclassified

“…Durante el proceso de optimización, es necesario generar constantemente propuestas, por lo que se vuelve necesario un mecanismo que tenga la capacidad de calificar cada nueva respuesta construida [2]. La tarea se vuelve crucial para establecer si los cambios ejecutados han mejorado la solución con respecto a la inicial, o si por el contrario ha empeorado [33] [36].…”

Section: Función Objetivounclassified

Asignación de salones por medio de una hiper-heurística

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A Hybrid Swarm-Based Approach to University Timetabling

Cited by 39 publications

References 60 publications

Particle Swarm Optimisation Variants and Its Hybridisation Ratios for Generating Cost-Effective Educational Course Timetables

Particle Swarm Optimisation Variants and Its Hybridisation Ratios for Generating Cost-Effective Educational Course Timetables

School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

Asignación de salones por medio de una hiper-heurística

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