Multiobjective quadratic assignment problem solved by an explicit building block search algorithm – MOMGA-IIa

  • Authors:

  • Richard O. Day;Gary B. Lamont

  • Affiliations:

  • Department of Electrical Engineering, Graduate School of Engineering & Management, Air Force Institute of Technology, WPAFB (Dayton), OH;Department of Electrical Engineering, Graduate School of Engineering & Management, Air Force Institute of Technology, WPAFB (Dayton), OH

  • Venue:
  • EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

The multi-objective quadratic assignment problem (mQAP) is an non-deterministic polynomial-time complete (NPC) problem with many real-world applications. The application addressed in this paper is the minimization of communication flows in a heterogenous mix of Organic Air Vehicles (OAV). A multi-objective approach to solving the general mQAP for this OAV application is developed. The combinatoric nature of this problem calls for a stochastic search algorithm; moreover, two linkage learning algorithms, the multi-objective fast messy genetic algorithm (MOMGA-II) and MOMGA-IIa, are compared. Twenty-three different problem instances having three different sizes (10, 20, and 30) plus two and three objectives are solved. Results indicate that the MOMGA-IIa resolves all pareto optimal points for problem instances