Pareto Optimal Based Evolutionary Approach for Solving Multi-Objective Facility Layout Problem
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
An adaptive local search based genetic algorithm for solving multi-objective facility layout problem
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
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Facility Layout Problem (FLP) is an emerging problem in the manufacturing industries due to the fact that the computational complexity increases with the number of departments, which leads it to a combinatorial optimization problem. Evolutionary algorithms have successfully been applied to FLP by many researchers. Unfortunately, most of these researches are predominantly on a single objective. Previously, we proposed an evolutionary approach for multi-objective FLP using Pareto optimality [1]. Simulation results indicate that it was capable of maintaining consistency and convergence of the trade-off, nondominated layout solutions. However, sometimes the solutions may be too diverse and the gap between the best and average solution is too large. This paper extends this idea by incorporating local search in the form of jumping gene operations introduced in Jumping Gene Genetic Algorithm (JGGA). Experimental results reveal that our proposed approach can search for the near-optimal and non-dominated solutions with better convergence and controlled-diversity by optimizing multiple criteria simultaneously.