Simulated annealing: theory and applications
Simulated annealing: theory and applications
A study of genetic crossover operations on the facilities layout problem
Computers and Industrial Engineering
A genetic approach to the quadratic assignment problem
Computers and Operations Research - Special issue on genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Swarm intelligence: power in numbers
Communications of the ACM - Evolving data mining into solutions for insights
Three Methods to Automate the Space Allocation Process in UK Universities
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
High performance scientific and engineering computing
Ant colony system with communication strategies
Information Sciences—Informatics and Computer Science: An International Journal
Robotics and Computer-Integrated Manufacturing
Continuous ant colony optimization in a SVR urban traffic forecasting model
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A multi-metaheuristic combined ACS-TSP system
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
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In this paper, a new heuristic combinatorial optimisation algorithm, called ant colony optimisation (ACO), is applied to the space-planning problem of determining an optimal assignment of activities (administrative functions/personnel) to locations (offices) for an organisation housed in an office block. This problem arises, for example, when a commercial organisation wishes to reduce (i.e. minimise) the amount of physical movement within its building(s) (e.g. flow of paperwork and personnel) in an attempt to improve operational efficiency. The ACO algorithm is motivated by analogy with natural phenomena, in particular, the ability of a colony of ants to "optimise their collective endeavours". In this paper, the biological background for ACO is explained and its computational implementation is presented in a space-planning context. The particular implementation of ACO makes use of a tabu search (TS) local improvement phase to give a computationally enhanced algorithm (ACOTS). Two examples are then used to show that ACOTS is a useful and viable optimisation technique to obtain layout designs for large-scale space-planning problems.