An Adaptive Local Search Based Genetic Algorithm for Solving Multi-objective Facility Layout Problem

Due to the combinatorial nature of the facility layout problem (FLP), several heuristic and meta-heuristic approaches have been developed to obtain good rather than optimal solutions. Unfortunately, most of these approaches are predominantly on a single objective. However, the real-world FLPs are multi-objective by nature and only very recently have meta-heuristics been designed and used in multi-objective FLP. These most often use the weighted sum method to combine the different objectives and thus, inherit the well-known problems of this method. This paper presents an adaptive local search based genetic algorithm (GA) for solving the multi-objective FLP that presents the layouts as a set of Pareto-optimal solutions optimizing both quantitative and qualitative objectives simultaneously. Unlike the conventional local search, the proposed adaptive local search scheme automatically determines whether local search is used in a GA loop or not. The results obtained show that the proposed algorithm outperforms the other competing algorithms and can find near-optimal and non-dominated solutions by optimizing multiple criteria simultaneously.