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
Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm
Information Sciences: an International Journal
Differential evolution in constrained numerical optimization: An empirical study
Information Sciences: an International Journal
Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms
Information Sciences: an International Journal
Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization
Information Sciences: an International Journal
An effective memetic differential evolution algorithm based on chaotic local search
Information Sciences: an International Journal
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach
Information Sciences: an International Journal
Adaptive population tuning scheme for differential evolution
Information Sciences: an International Journal
A harmony search algorithm for nurse rostering problems
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
Hybridising harmony search with a Markov blanket for gene selection problems
Information Sciences: an International Journal
Scheduling a log transport system using simulated annealing
Information Sciences: an International Journal
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In manufacturing industry, turning operations are used to remove unwanted sections of a part to obtain the final product. In this paper a comparison of state-of-the-art optimization techniques to solve multi-pass turning optimization problems is presented.Furthermore, a hybrid technique based on differential evolution algorithm is introduced for solving manufacturing optimization problems. The results have demonstrated the superiority of the hybrid approach over the other techniques like artificial bee colony algorithm, differential evolution algorithm, hybrid particle swarm optimization algorithm, hybrid artificial immune-hill climbing algorithm, hybrid taguchi-harmony search algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm and an improved simulated annealing algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required.