In this paper we investigate how to efficiently apply Approximate-Karush–Kuhn–Tucker proximity measures as stopping criteria for optimization algorithms that do not generate approximations to Lagrange multipliers. We prove that the KKT error measurement tends to zero when approaching a solution and we develop a simple model to compute the KKT error measure requiring only the solution of a non-negative linear least squares problem. Our numerical experiments on a Genetic Algorithm show the efficiency of the strategy.
Highlights:
- We use AKKT as stopping criteria for GA.
- AKKT is used for optimality condition.
- Our approach showed to be very efficient.
Keywords:
Condizioni di ottimo; Algoritmi di ottimizzazione; Arresto criteri; Gli algoritmi genetici
Conditions d’optimalité; Les algorithmes d’optimisation; Critères d’arrêt; Les algorithmes génétiques
Optimalitätsbedingungen; Optimierungsalgorithmen; Abbruchkriterien; genetische Algorithmen
Condiciones de optimalidad; Algoritmos de optimización; Detener criterios; Algoritmos genéticos
Optimality शर्तों; अनुकूलन एल्गोरिदम; मानदंड रोकना; आनुवंशिक एल्गोरिदम
最適条件。最適化アルゴリズム。基準を停止。遺伝的アルゴリズム
最优性条件;优化算法;停止准则;遗传算法
最優性條件;優化算法;停止準則;遺傳算法
최적 조건; 최적화 알고리즘; 기준을 중지; 유전자 알고리즘