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A NEWTON-TYPE GLOBALLY CONVERGENT INTERIOR-POINT METHOD TO SOLVE MULTI-OBJECTIVE OPTIMIZATION PROBLEMS
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作者 Jauny Prajapati Debdas Ghosh Ashutosh Upadhayay 《Journal of Computational Mathematics》 SCIE CSCD 2024年第1期24-48,共25页
This paper proposes an interior-point technique for detecting the nondominated points of multi-objective optimization problems using the direction-based cone method.Cone method decomposes the multi-objective optimizat... This paper proposes an interior-point technique for detecting the nondominated points of multi-objective optimization problems using the direction-based cone method.Cone method decomposes the multi-objective optimization problems into a set of single-objective optimization problems.For this set of problems,parametric perturbed KKT conditions are derived.Subsequently,an interior point technique is developed to solve the parametric perturbed KKT conditions.A differentiable merit function is also proposed whose stationary point satisfies the KKT conditions.Under some mild assumptions,the proposed algorithm is shown to be globally convergent.Numerical results of unconstrained and constrained multi-objective optimization test problems are presented.Also,three performance metrics(modified generational distance,hypervolume,inverted generational distance)are used on some test problems to investigate the efficiency of the proposed algorithm.We also compare the results of the proposed algorithm with the results of some other existing popular methods. 展开更多
关键词 Cone method Interior point method Merit function Newton method Global convergence
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