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An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes

  • Rodrigo Lankaites PinheiroEmail author
  • Dario Landa-Silva
  • Wasakorn Laesanklang
  • Ademir Aparecido Constantino
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 966)

Abstract

Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many-objective problems is often a difficult task even for modern multiobjective algorithms. In some cases, multiple instances of the problem scenario present similarities in their fitness landscapes. That is, there are recurring features in the fitness landscapes when searching for solutions to different problem instances. We propose a methodology to exploit this characteristic by solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We use three goal-based objective functions and show that on benchmark instances of the multiobjective vehicle routing problem with time windows, the methodology is able to produce good results in short computation time. The methodology allows to combine the effectiveness of state-of-the-art multiobjective algorithms with the efficiency of goal programming to find good compromise solutions in problem scenarios where instances have similar fitness landscapes.

Keywords

Multi-criteria decision making Goal programming Pareto optimisation Multiobjective vehicle routing 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rodrigo Lankaites Pinheiro
    • 1
    • 2
    Email author
  • Dario Landa-Silva
    • 2
  • Wasakorn Laesanklang
    • 3
  • Ademir Aparecido Constantino
    • 4
  1. 1.Webroster Ltd.PeterboroughUK
  2. 2.ASAP Research Group, School of Computer ScienceUniversity of NottinghamNottinghamUK
  3. 3.Department of Mathematics, Faculty of ScienceMahidol UniversityNakhon PathomThailand
  4. 4.Departamento de InformáticaUniversidade Estadual de MaringáMaringáBrazil

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