Decision support system for the school districting problem
Operations Research
Genetic algorithms with cluster analysis for production simulation
Proceedings of the 29th conference on Winter simulation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
VISI Physical Design Automation: Theory and Practice
VISI Physical Design Automation: Theory and Practice
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Genetic Algorithm and Graph Partitioning
IEEE Transactions on Computers
A decision support system for the electrical power districting problem
Decision Support Systems
Theory of Computing Systems
An Improved Min-Cut Algonthm for Partitioning VLSI Networks
IEEE Transactions on Computers
Exact and Heuristic Algorithms for the Weapon-Target Assignment Problem
Operations Research
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Graph partitioning by multi-objective real-valued metaheuristics: A comparative study
Applied Soft Computing
Multiobjective evolutionary algorithm for redesigning sales territories
ICCL'11 Proceedings of the Second international conference on Computational logistics
Grouping genetic operators for the delineation of functional areas based on spatial interaction
Expert Systems with Applications: An International Journal
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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The graph partitioning problem has numerous applications in various scientific fields. It usually involves the effective partitioning of a graph into a number of disjoint sub-graphs/zones, and hence becomes a combinatorial optimization problem whose worst case complexity is NP-complete. The inadequacies of exact methods, like linear and integer programming approaches, to handle large-size instances of the combinatorial problems have motivated heuristic techniques to these problems. In the present work, a multi-objective evolutionary algorithm (MOEA), a kind of heuristic techniques, is developed for partitioning a graph under multiple objectives and constraints. The developed MOEA, which is a modified form of NSGA-II, is applied to four randomly generated graphs for partitioning them by optimizing three common objectives under five general constraints. The applications show that the MOEA is successful, in most of the cases, in achieving the expected results by partitioning a graph into a variable number of zones.