Abstract
Computational intelligent has been accepted and recognized by global research scientists, decision makers and practicing researchers in recent years as powerful artificial intelligent techniques, for solving unlimited number of complex real-world problems particularly related to research area of unpredictable and uncertain optimization. Under the uncertain and turbulence environment, classical and traditional approaches are unable to obtain a complete satisfactory solution with higher level of degree of satisfaction for the real world practical problems on optimization. Therefore, new global computational intelligent optimization methods are required to handle these issues seriously. One such method is hybrid evolutionary computation, a generic, flexible, robust, beneficial and versatile framework for solving complex and unpredictable problems of global optimization and search in real world application problems.
Highlights
Computational intelligent has been accepted and recognized by global research scientists, decision makers and practicing researchers in recent years as powerful artificial intelligent techniques, for solving unlimited number of complex real-world problems related to research area of unpredictable and uncertain optimization
Among the 15 techniques, three best techniques are selected based on the percentage of quality solution respect to level of satisfaction and the degree of possibility
An intelligent performance analysis table is constructed to the convenience of decision makers and implementers to select the niche optimization techniques in order to apply in the real world problem solving approach related to industrial engineering problems [4]
Summary
Short Note on Computational Intelligent Techniques for Industrial Production Systems Computational intelligent has been accepted and recognized by global research scientists, decision makers and practicing researchers in recent years as powerful artificial intelligent techniques, for solving unlimited number of complex real-world problems related to research area of unpredictable and uncertain optimization. Under the uncertain and turbulence environment, classical and traditional approaches are unable to obtain a complete satisfactory solution with higher level of degree of satisfaction for the real world practical problems on optimization.
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