Tabu search for nonlinear and parametric optimization (with links to genetic algorithms)
Discrete Applied Mathematics - Special volume: viewpoints on optimization
Genetic algorithms and tabu search: hybrids for optimization
Computers and Operations Research - Special issue on genetic algorithms
Practical genetic algorithms
Achieving World Class Manufacturing through Process Control
Achieving World Class Manufacturing through Process Control
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
Design and Analysis of Experiments
Design and Analysis of Experiments
Handbook of Metaheuristics
Expert Systems with Applications: An International Journal
Desirability improvement of committee machine to solve multiple response optimization problems
Advances in Artificial Neural Systems
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Multiple-response grinding process is usually too complex to optimise, requiring a large number of interacting process variables and responses. Experimentation techniques, such as factorial design, fractional factorial design and Response Surface Methodology (RSM) that may be used for this process are too difficult to implement for production lines involving grinding and other necessary operations. For grinding process involving continuous variable, non-linear and multiple-response optimisation problem, the potential of Tabu Search (TS) strategy needs to be explored either in its original form or its variant. In this paper, integrating Artificial Neural Network (ANN) and composite desirability function with a Modified Tabu Search (MTS) strategy, based on Mahalanobis multivariate distance approach to identify tabu move, with scatter search intensification scheme is proposed for the above-mentioned problem. Computational results show that MTS provides better consistency in terms of sample mean and standard deviation of composite desirability measures than that of real-coded GA.