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Optimization of Office-Space Allocation Problem Using Artificial Bee Colony Algorithm

机译:人工蜂群算法优化办公空间分配问题

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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, meta-heuristic 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.
机译:办公空间分配(OFA)问题是一类复杂的优化问题,该问题将一组有限的实体分配给一组资源,而这些资源要满足一组约束条件。由于OFA的复杂性,已经提出了许多基于元启发式的技术。人工蜂群(ABC)算法是一种群智能,元启发式技术,已成功地用于解决大学时标问题的几种表述。本文提出了一种用于解决OFA问题的ABC算法。适应过程包括将三个邻域运算符与ABC算法的组件集成在一起,以应对OFA崎search的搜索空间。由诺丁汉大学建立的基准实例(即Nothingham数据集)用于评估所提出的ABC算法。有趣的是,与最新方法相比,ABC能够通过获得两项新结果,一项最佳结果和一项竞争性结果来生产高质量的解决方案。

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