Combining Meta-Heuristics to Effectively Solvethe Vehicle Routing Problems with Time Windows
Artificial Intelligence Review
Efficient synthesis of out-of-core algorithms using a nonlinear optimization solver
Journal of Parallel and Distributed Computing - Special issue: 18th International parallel and distributed processing symposium
EURASIP Journal on Wireless Communications and Networking
Violation-guided learning for constrained formulations in neural-network time-series predictions
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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Abstract: The paper presents a problem-independent framework that unifies various mechanisms for solving discrete constrained nonlinear programming (NLP) problems whose functions are not necessarily differentiable and continuous. The framework is based on the first-order necessary and sufficient conditions in the theory of discrete constrained optimization using Lagrange multipliers. It implements the search for discrete-neighborhood saddle points (SP/sub dn/) by performing ascents in the original-variable subspace and descents in the Lagrange-multiplier subspace. Our study on the various mechanisms shows that CSAGA, a combined constrained simulated annealing and genetic algorithm, performs well. Finally, we apply iterative deepening to determine the optimal number of generations in CSAGA.