Efficient search for trade-offs by adaptive range multi-objective genetic algorithms

Trade-offs is one of important elements for engineering design problems characterized by multiple conflicting objectives that needs to be simultaneously improved. Further, in many problems such as aerodynamic design, due to computational reasons, only a limited number of evaluations can be allowed for industrial use. This paper proposes new efficient Multi-Objective Evolutionary Algorithms (MOEAs), Adaptive Range Multi-Objective Genetic Algorithms (ARMOGAs), to identify trade-offs among objectives using a small number of function evaluations. The search performance of ARMOGAs is examined by using four different multi-objective analytical test problems. ARMOGAs are also compared with another MOEA. Although the number of evaluations is limited, ARMOGAs showed good performance. In addition, Sequential Quadratic Programming and Dynamic Hill Climber methods are applied to obtain trade-offs for the same problems. These gradient-based methods had some difficulties in identifying trade-offs.