Job shop scheduling by simulated annealing
Operations Research
Proceedings of the 21st international conference on Computers and industrial engineering
Simulated annealing algorithms for continuous global optimization: convergence conditions
Journal of Optimization Theory and Applications
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Epistasis in Genetic Algorithms: An Experimental Design Perspective
Proceedings of the 6th International Conference on Genetic Algorithms
Building Better Test Functions
Proceedings of the 6th International Conference on Genetic Algorithms
An orthogonal simulated annealing algorithm for large floorplanning problems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
Branch-and-bound and simulated annealing algorithms for a two-agent scheduling problem
Expert Systems with Applications: An International Journal
A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization
Mathematics and Computers in Simulation
Heuristic-based neural networks for stochastic dynamic lot sizing problem
Applied Soft Computing
Hi-index | 12.05 |
Recent research shows that simulated annealing with orthogonal array based neighbourhood functions can help in the search for a solution to a parametrical problem which is closer to an optimum when compared with conventional simulated annealing. Previous studies of simulated annealing analyzed only the main effects of variables of parametrical problems. In fact, both main effects of variables and interactions between variables should be considered, since interactions between variables exist in many parametrical problems. In this paper, an improved orthogonal array based neighbourhood function (IONF) for simulated annealing with the consideration of interaction effects between variables is described. After solving a set of parametrical benchmark function problems where interaction effects between variables exist, results of the benchmark tests show that the proposed simulated annealing algorithm with the IONF outperforms significantly both the simulated annealing algorithms with the existing orthogonal array based neighbourhood functions and the standard neighbourhood functions. Finally, the improved orthogonal array based simulated annealing was applied on the optimization of emulsified dynamite packing-machine design by which the applicability of the algorithm in real world problems can be evaluated and its effectiveness can be further validated.