Artificial neural networks used in optimization problems
Tài liệu tham khảo
Ríos-Mercado, 2015, Optimization problems in natural gas transportation systems: a state-of-the-art review, Appl. Energy, 147, 536, 10.1016/j.apenergy.2015.03.017
Wang, 2015, Two-echelon logistics distribution region partitioning problem based on a hybrid particle swarm optimization–genetic algorithm, Expert Syst. Appl., 42, 5019, 10.1016/j.eswa.2015.02.058
Salari, 2015, Combining ant colony optimization algorithm and dynamic programming technique for solving the covering salesman problem, Comput. Ind. Eng., 83, 244, 10.1016/j.cie.2015.02.019
Zheng, 2015, A multi-agent optimization algorithm for resource constrained project scheduling problem, Exp. Syst. Appl., 42, 6039, 10.1016/j.eswa.2015.04.009
Lanza-Gutierrez, 2015, Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for Relay Node deployment in Wireless Sensor Networks, Appl. Soft Comput., 30, 675, 10.1016/j.asoc.2015.01.051
Ploskas, 2015, Efficient GPU-based implementations of simplex type algorithms, Appl. Math. Comput., 250, 552, 10.1016/j.amc.2014.10.096
Kuhn, 1951, Nonlinear Programming, 481
Wang, 2014, The hybrid genetic algorithm with two local optimization strategies for traveling salesman problem, Comput. Ind. Eng., 70, 124, 10.1016/j.cie.2014.01.015
Li, 2014, Evacuation dynamic and exit optimization of a supermarket based on particle swarm optimization, Phys A: Stat Mech. Appl., 416, 157, 10.1016/j.physa.2014.08.054
Kolonko, 2009, Some new results on simulated annealing applied to the job shop scheduling problem, Eur. J. Oper. Res., 113, 123, 10.1016/S0377-2217(97)00420-7
İnkaya, 2015, Ant Colony Optimization based clustering methodology, Appl. Soft Comput., 28, 301, 10.1016/j.asoc.2014.11.060
Yang, 2010, An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem, Appl. Soft Comput., 10, 653, 10.1016/j.asoc.2009.08.040
Bououden, 2015, An ant colony optimization-based fuzzy predictive control approach for nonlinear processes, Inf. Sci., 299, 143, 10.1016/j.ins.2014.11.050
Ñanculef, 2014, A novel Frank–Wolfe algorithm. Analysis and applications to large-scale SVM training, Inf. Sci., 285, 66, 10.1016/j.ins.2014.03.059