NBI and MOGA-II, two complementary algorithms for Multi-Objective optimizations
نویسندگان
چکیده
The NBI-NLPQLP optimization method is tested on several multi-objective optimization problems. Its performance is compared to that of MOGA-II: since NBI-NLPQLP is based on the classical gradientbased NLPQLP, it is fast and accurate, but not as robust, in comparison with the genetic algorithm. Furthermore a discontinuous Pareto frontier can give rise to problems in the NBI’s convergence. In order to overcome this problem, a hybridization technique coupled with a partitioning method is proposed.
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