External archive hybrid genetic algorithm for unequal area facility layout problem

Layout problems are known to be complex and are generally NP-hard. As a subclass of facility layout problem (FLP), the unequal area facility layout problem (UA-FLP) is also difficult to find a satisfactory solution within acceptable computation time. Although much research has been carried out in this area, the search efficiency is far from sufficient to handle the UA-FLPs with a large number of facilities. Aiming to solve this problem, this paper proposes an external archive hybrid genetic algorithm (HGA) which uses flexible bay structure to represent solution. In HGA, a novel area-based greedy initialization strategy is used to produce initial population which guarantees the feasibility of initial individuals. Offspring is produced by individuals from current population and external archive in the hope that the search is guided in promising search space. Part of the offspring is further improved by a hill-climbing method to enhance the HGA's exploitability. All these combined to present an efficient HGA for the UA-FLP. Experimental results demonstrate that the HGA is able to obtain highly competitive results compared to other peer algorithms.