Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
A novel hybrid immune algorithm for global optimization in design and manufacturing
Robotics and Computer-Integrated Manufacturing
A new design optimization framework based on immune algorithm and Taguchi's method
Computers in Industry
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization
Information Sciences: an International Journal
Information Sciences: an International Journal
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
Information Sciences: an International Journal
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
A comparative study of population-based optimization algorithms for turning operations
Information Sciences: an International Journal
Comparison of evolutionary-based optimization algorithms for structural design optimization
Engineering Applications of Artificial Intelligence
A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing
Applied Soft Computing
Performance analysis of the coarse-grained parallel model of the artificial bee colony algorithm
Information Sciences: an International Journal
Information Sciences: an International Journal
Estimation of distribution algorithm for a class of nonlinear bilevel programming problems
Information Sciences: an International Journal
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Selection of cutting parameters in machining operations is an essential task to reduce cost of the products and increase quality. This paper presents an optimization approach based on artificial bee colony algorithm for optimal selection of cutting parameters in multi-pass turning operations. The objective is to find the optimized cutting parameters in the turning operations. A comparison of evolutionary-based optimization techniques to solve multi-pass turning optimization problems is presented. The results of the proposed approach for the case studies are compared with previously published results by using other optimization techniques in the literature.