Hybrid population-based metaheuristic approaches for the space allocation problem

A hybrid population-based metaheuristic for the space allocation problem in academic institutions is presented that is based upon previous experiments using a range of techniques including hill-climbing, simulated annealing, tabu search and genetic algorithms. The proposed approach incorporates the best characteristics of each technique, makes an automatic selection of the parameters according to the problem characteristics and surpasses the performance of these standard techniques in terms of the solution quality evaluated with a penalty function. This approach incorporates local search heuristics, adaptive cooling schedules and population-based techniques. Our experiments show that this technique produces competitive solutions for the space allocation problem. In this problem, it is often desirable to obtain a set of candidate solutions so that the decision maker can select the best among them. By controlling a common cooling schedule for the whole population in the simulated annealing component, it is possible to find one excellent solution or to produce a population of good solutions