An Elitist GRASP Metaheuristic for the Multi-objective Quadratic Assignment Problem
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Designing difficult office space allocation problem instances with mathematical programming
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
The office-space-allocation problem in strongly hierarchized organizations
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
Improving parallel local search for SAT
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Office-space-allocation problem using harmony search algorithm
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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We investigate cooperative local search to improve upon known results of the office-space-allocation problem in universities and other organizations. A number of entities (e.g., research students, staff, etc.) must be allocated into a set of rooms so that the physical space is utilized as efficiently as possible while satisfying a number of hard and soft constraints. We develop an asynchronous cooperative local search approach in which a population of local search threads cooperate asynchronously to find better solutions. The approach incorporates a cooperation mechanism in which a pool of genes (parts of solutions) is shared to improve the global search strategy. Our implementation is single-processor and we show that asynchronous cooperative search is also advantageous in this case. We illustrate this by extending four single-solution metaheuristics (hill-climbing, simulated annealing, tabu search, and a hybrid metaheuristic) to population-based variants using our asynchronous cooperative mechanism. In each case, the population-based approach performs better than the single-solution one using comparable computation time. The asynchronous cooperative metaheuristics developed here improve upon known results for a number of test instances.