Conic Scalarization Method in Multiobjective Optimization and Relations with Other Scalarization Methods


The paper presents main features of the conic scalarization method in multiobjective optimization. The conic scalarization method guarantees to generate all proper efficient solutions and does not require any kind of convexity or boundedness conditions. In addition the preference and reference point information of the decision maker is taken into consideration by this method. In this paper, relations with other scalarization methods are investigated and it is shown that some efficient solutions computed by the Pascoletti-Serafini and the Benson’s scalarization methods, can be obtained by the conic scalarization method.