Abstract
Office-space allocation (OFA) problem is a class of complex optimization problems that distributes a set of limited entities to a set of resources subject to satisfying set of constraints. Due to the complexity of OFA, numerous metaheuristic-based techniques have been proposed. Artificial Bee Colony (ABC) algorithm is a swarm intelligence, metaheuristic techniques that have been utilized successfully to solve several formulations of university timetabling problems. This paper presents an adaptation of ABC algorithm for solving OFA problem. The adaptation process involves integration of three neighbourhood operators with the components of the ABC algorithm in order to cope with rugged search space of the OFA. The benchmark instances established by University of Nottingham namely Nothingham dataset is used in the evaluation of the proposed ABC algorithm. Interestingly, the ABC is able to produced high quality solution by obtaining two new results, one best results and competitive results in comparison with the state-of-the-art methods.
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Bolaji, A.L., Michael, I., Shola, P.B. (2017). Optimization of Office-Space Allocation Problem Using Artificial Bee Colony Algorithm. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_37
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DOI: https://doi.org/10.1007/978-3-319-61824-1_37
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