University Course Timetabling Using a Hybrid Harmony Search Metaheuristic Algorithm
01 Sep 2012-Vol. 42, Iss: 5, pp 664-681
TL;DR: A memetic computing technique designed for UCTP, called the hybrid harmony search algorithm (HHSA), is proposed and achieves the optimal solution for the small dataset with comparable results for the medium datasets.
Abstract: University course timetabling problem (UCTP) is considered to be a hard combinatorial optimization problem to assign a set of events to a set of rooms and timeslots. Although several methods have been investigated, due to the nature of UCTP, memetic computing techniques have been more effective. A key feature of memetic computing is the hybridization of a population-based global search and the local improvement. Such hybridization is expected to strike a balance between exploration and exploitation of the search space. In this paper, a memetic computing technique that is designed for UCTP, called the hybrid harmony search algorithm (HHSA), is proposed. In HHSA, the harmony search algorithm (HSA), which is a metaheuristic population-based method, has been hybridized by: 1) hill climbing, to improve local exploitation; and 2) a global-best concept of particle swarm optimization to improve convergence. The results were compared against 27 other methods using the 11 datasets of Socha et al. comprising five small, five medium, and one large datasets. The proposed method achieved the optimal solution for the small dataset with comparable results for the medium datasets. Furthermore, in the most complex and large datasets, the proposed method achieved the best results.
Citations
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TL;DR: This paper thoroughly reviews and analyzes the main characteristics and application portfolio of the so-called Harmony Search algorithm, a meta-heuristic approach that has been shown to achieve excellent results in a wide range of optimization problems.
324 citations
Cites methods from "University Course Timetabling Using..."
...Other considerations and adjustments to the original HS algorithm performed in Al-Betar et al. (2012a) include (1) novel repair methods for maintaining the feasibility of the solutions in the HM; and (2) a modification of the PAR procedure under which only pitch adjustments improving the quality of…...
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...A research work exemplifying different HS modifications can be found in Al-Betar et al. (2012a), where a hybrid HS algorithm is used in order to tackle the so-called university course timetabling problem (UCTP)....
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TL;DR: The distributed multi agent systems based approach (Cooperative Search method) is investigated due to its scalability which enables the timetabling of common events between departments and in this work a complete introduction of reliable datasets has been given to test and evaluation of the structure of considered algorithms.
170 citations
01 Oct 2016
TL;DR: The simulation results show that the tournament-based harmony search algorithm is capable of achieving better quality solutions than many of the well-popular optimization methods.
Abstract: Graphical abstractDisplay Omitted HighlightsThe tournament-based harmony search (THS) algorithm is proposed for economic load dispatch (ELD) problem.In THS, the random selection in the memory consideration is replaced by the tournament selection to observe the natural selection strategy.The proposed THS is tested using three different tournament size values for four test ELD systems.The results suggest that the THS with higher tournament size is efficient for ELD.New results appeared using THS for ELD when THS was compared with 43 methods published in 33 articles. This paper proposes a tournament-based harmony search (THS) algorithm for economic load dispatch (ELD) problem. The THS is an efficient modified version of the harmony search (HS) algorithm where the random selection process in the memory consideration operator is replaced by the tournament selection process to activate the natural selection of the survival-of-the-fittest principle and thus improve the convergence properties of HS. The performance THS is evaluated with ELD problem using five different test systems: 3-units generator system; two versions of 13-units generator system; 40-units generator system; and large-scaled 80-units generator system. The effect of tournament size (t) on the performance of THS is studied. A comparative evaluation between THS and other existing methods reported in the literature are carried out. The simulation results show that the THS algorithm is capable of achieving better quality solutions than many of the well-popular optimization methods.
86 citations
TL;DR: In this article, the improved Artificial Bee Colony (IABC) was used to solve the OPF problem in electric power grids. But, the problem of finding the best combination of two solution vectors based on limited trials instead of exhaustive trials was not addressed.
79 citations
TL;DR: A novel PSO-based method is investigated for solving highly constrained UCSP in which basic PSO operations are transformed to tackle combinatorial optimization task of UCSP and a few new operations are introduced to PSO to solve UCSP efficiently.
Abstract: The University Course Scheduling Problem (UCSP) is a highly constrained real-world combinatorial optimization task. Solving UCSP means creating an optimal course schedule by assigning courses to specific rooms, instructors, students, and timeslots by taking into account the given constraints. Several studies have reported different metaheuristic approaches for solving UCSP including Genetic Algorithm (GA) and Harmony Search (HS) algorithm. Various Swarm Intelligence (SI) optimization methods have also been investigated for UCSP in recent times and a few Particle Swarm Optimization (PSO) based methods among them with different adaptations are shown to be effective. In this study, a novel PSO-based method is investigated for solving highly constrained UCSP in which basic PSO operations are transformed to tackle combinatorial optimization task of UCSP and a few new operations are introduced to PSO to solve UCSP efficiently. In the proposed method, swap sequence-based velocity computation and its application are developed to transform individual particles in order to improve them. Selective search and forceful swap operation with repair mechanism are the additional new operations in the proposed method for updating particles with calculated swap sequences as velocities. The proposed PSO with selective search (PSOSS) method has been tested on an instance of UCSP for the Computer Science and Engineering Department of Khulna University of Engineering & Technology which has many hard and soft constraints. Experimental results revealed the effectiveness and the superiority of the proposed method compared to other prominent metaheuristic methods (e.g., GA, HS).
77 citations
References
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06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
"University Course Timetabling Using..." refers background in this paper
...position of the best quality particle in the entire swarm is the global best [75]....
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01 Feb 2001
TL;DR: A new heuristic algorithm, mimicking the improvisation of music players, has been developed and named Harmony Search (HS), which is illustrated with a traveling salesman problem (TSP), a specific academic optimization problem, and a least-cost pipe network design problem.
Abstract: Many optimization problems in various fields have been solved using diverse optimization al gorithms. Traditional optimization techniques such as linear programming (LP), non-linear programming (NL...
5,136 citations
TL;DR: A survey of the nowadays most important metaheuristics from a conceptual point of view and introduces a framework, that is called the I&D frame, in order to put different intensification and diversification components into relation with each other.
Abstract: The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
3,287 citations
"University Course Timetabling Using..." refers background or methods in this paper
...The process to hybridize a local search-based method within a population-based method has gained much interest [3]....
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...However, they go through a trajectory in the search space without doing a wider scan of the entire search space [3]....
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...For example, Blum and Roli [3] in an influential article on metaheuristics concluded “In summary, population-based methods are better in identifying promising areas in the search space, whereas trajectory methods are bet-...
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...In a nutshell, “Mixing and hybridizing is often better than purity” [3]....
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...For example, Blum and Roli [3] in an influential article on metaheuristics concluded “In summary, population-based methods are better in identifying promising areas in the search space, whereas trajectory methods are better in exploring promising areas in the search space....
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TL;DR: The impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented and the proposed algorithm can find better solutions when compared to HS and other heuristic or deterministic methods.
1,782 citations
"University Course Timetabling Using..." refers background or methods in this paper
...Furthermore, several improved and hybridized variants of the HSA have been proposed [12], [21]–[24], and surveyed in [25]....
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...Compared with other metaheuristic methods, the HSA has several characteristics: It stipulates fewer mathematical requirements and iteratively generates a new solution after considering all the existing solutions [12]; it has a novel stochastic derivative which reduces the number of iterations that are required to converge toward local minima [13]; it can handle both discrete and continuous variables, and others such as simplicity, flexibility, adaptability, generality, and scalability....
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TL;DR: A new harmony search (HS) meta-heuristic algorithm-based approach for engineering optimization problems with continuous design variables conceptualized using the musical process of searching for a perfect state of harmony using a stochastic random search instead of a gradient search.
1,714 citations
"University Course Timetabling Using..." refers background in this paper
...This section presents a detailed overview of the basics of the HSA as described in [28], [65], and [66]....
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