An outer-approximation algorithm for a class of mixed-integer nonlinear programs
Mathematical Programming: Series A and B
A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Penalty guided genetic search for reliability design optimization
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Optimization Using A Penalty Function
Proceedings of the 5th International Conference on Genetic Algorithms
Ant Colony Optimization
Scatter search for chemical and bio-process optimization
Journal of Global Optimization
Extended ant colony optimization for non-convex mixed integer nonlinear programming
Computers and Operations Research
pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization
Structural and Multidisciplinary Optimization
Solving extremely difficult MINLP problems using adaptive resolution Micro-GA with tabu search
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Optimal camera placement to measure distances regarding static and dynamic obstacles
International Journal of Sensor Networks
A genetic algorithm based augmented Lagrangian method for constrained optimization
Computational Optimization and Applications
The ant colony optimisation solving continuous problems
International Journal of Computational Intelligence Studies
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A new and universal penalty method is introduced in this contribution. It is especially intended to be applied in stochastic metaheuristics like genetic algorithms, particle swarm optimization or ant colony optimization. The novelty of this method is, that it is an advanced approach that only requires one parameter to be tuned. Moreover this parameter, named oracle, is easy and intuitive to handle. A pseudo-code implementation of the method is presented together with numerical results on a set of 60 constrained benchmark problems from the open literature. The results are compared with those obtained by common penalty methods, revealing the strength of the proposed approach. Further results on three real-world applications are briefly discussed and fortify the practical usefulness and capability of the method.