A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Digital IIR filter design using differential evolution algorithm
EURASIP Journal on Applied Signal Processing
A New Differential Evolution for Discontinuous Optimization Problems
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Enhanced Pareto Particle Swarm Approach for Multi-Objective Optimization of Surface Grinding Process
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 02
Solving rotated multi-objective optimization problems using differential evolution
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
IEEE Transactions on Evolutionary Computation
Optimal approximation of linear systems by a differential evolution algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm
IEEE Transactions on Neural Networks
Applying modified NSGA-II for bi-objective supply chain problem
Journal of Intelligent Manufacturing
Hi-index | 12.05 |
An improved differential evolution algorithm, named the Taguchi-sliding-based differential evolution algorithm (TSBDEA), is proposed in this work to solve the problem of optimization for the surface grinding process. The purpose of this work is to optimize the grinding variables such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, using a multi-objective function model with a weighted approach, simultaneously subject to a comprehensive set of process constraints. The TSBDEA, a powerful global numerical optimization method, combines the differential evolution algorithm (DEA) with the Taguchi-sliding-level-method (TSLM). The TSLM is used as the crossover operation of the DEA. Then, the systematic reasoning ability of the TSLM is provided to select the better offspring to achieve the crossover, and consequently enhance the DEA. Therefore, the TSBDEA can be statistically sound and quickly convergent. The illustrative cases of both rough-grinding and finish-grinding are given to demonstrate the applicability of the proposed TSBDEA, and the computational results show that the proposed TSBDEA can obtain better results than the methods presented in the literatures.