TY - GEN
T1 - An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes
AU - Pinheiro, Rodrigo Lankaites
AU - Landa-Silva, Dario
AU - Laesanklang, Wasakorn
AU - Constantino, Ademir Aparecido
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Goal programming
KW - Multi-criteria decision making
KW - Multiobjective vehicle routing
KW - Pareto optimisation
UR - http://www.scopus.com/inward/record.url?scp=85064060850&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-16035-7_8
DO - 10.1007/978-3-030-16035-7_8
M3 - Conference contribution
AN - SCOPUS:85064060850
SN - 9783030160340
T3 - Communications in Computer and Information Science
SP - 134
EP - 152
BT - Operations Research and Enterprise Systems - 7th International Conference, ICORES 2018, Revised Selected Papers
A2 - Demange, Marc
A2 - Parlier, Greg H.
A2 - Liberatore, Federico
PB - Springer Verlag
T2 - 7th International Conference on Operations Research and Enterprise Systems, ICORES 2018
Y2 - 24 January 2018 through 26 January 2018
ER -