An adaptive local search based genetic algorithm for solving multi-objective facility layout problem

Authors:
Kazi Shah Nawaz Ripon;Kyrre Glette;Mats Høvin;Jim Torresen
Affiliations:
Department of Informatics, University of Oslo, Norway;Department of Informatics, University of Oslo, Norway;Department of Informatics, University of Oslo, Norway;Department of Informatics, University of Oslo, Norway
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

Citing 6
Cited 1

Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming

Journal of Heuristics
Hybrid genetic algorithm with adaptive local search scheme for solving multistage-based supply chain problems

Computers and Industrial Engineering
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
Multi-objective evolutionary approach for solving facility layout problem using local search

Proceedings of the 2010 ACM Symposium on Applied Computing
How to solve a multicriterion problem for which pareto dominance relationship cannot be applied? a case study from medicine

KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Using pareto-optimality for solving multi-objective unequal area facility layout problem

Proceedings of the 13th annual conference on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 multiobjective 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 nondominated solutions by optimizing multiple criteria simultaneously.