EditorialSpecial issue on hybrid evolutionary systems for manufacturing processes
Introduction
From past to present, classical evolutionary algorithms, such as genetic algorithms, genetic programming, evolutionary programming and evolution strategy, have contributed to optimize a wide range of manufacturing processes, whose demands to be more robust, more flexible, more responsive, more complex and more efficient are ever increasing. In today's competitive market, manufacturing process problems have to be solved with impeccable quality in short computational time. In general, reasonable results regarding particular manufacturing processes can be obtained by applying classical evolutionary algorithms which may not achieve the most convincing solution with the highest quality for a particular manufacturing process. To achieve the highest quality solution for a particular manufacturing process, integration of special techniques into the classical evolutionary algorithm is usually required. The resulting systems are called hybrid evolutionary systems. Usually hybrid evolutionary systems are integrated with classical optimization methods, heuristic algorithms or other computational intelligence methods in order to enhance the effectiveness of the classical evolutionary algorithms. Literature of evolutionary computation shows that the hybrid evolutionary systems are usually able to obtain higher quality solutions with smaller computational time than those obtained by the classical evolutionary algorithms for a particular manufacturing process.
Although the application oriented research with hybrid evolutionary systems has currently reached an impressive state, there are a number of critical issues regarding the design of hybrid evolutionary systems with the rapidly growing complexity of manufacturing processes and demanding manufacturing qualities.
This special issue aims to bring together researchers from academia and industry to report and review the latest progress in application oriented research with hybrid evolutionary systems, to explore new applications in manufacturing processes, to design new hybrid evolutionary systems for solving specific problems in manufacturing processes and finally to create awareness of hybrid evolutionary systems for a wider audience of practitioners. Target authors included research students, researchers and scientists from computational intelligence, manufacturing or product design engineers involved high precisions and high quality design in manufacturing processes or product design manufacturing or product design professionals
The following areas of hybrid evolutionary systems for manufacturing processes were considered:
- 1)
Developing hybrid evolutionary systems for solving real world problems in manufacturing processes.
- 2)
Designing hybrid evolutionary systems by co-operating evolutionary algorithms with other modern optimization algorithms (such as particle swarm optimization, simulated annealing, artificial immune system, ant colony etc.).
- 3)
Modifying evolutionary operations by integrating with computational intelligence techniques (such as fuzzy systems, neural networks, support vector machines etc.) so as to produce an effective hybrid evolutionary system.
- 4)
Enhancing evolutionary algorithms by co-operating with classical optimization or statistical methods.
- 5)
Using hybrid evolutionary systems for handling constrained, multi-objective and large scale optimization problems in manufacturing processes.
- 6)
Applying hybrid evolutionary systems in real manufacturing processes such as scheduling, allocation etc.
- 7)
Parallel hybrid evolutionary systems for practical applications in manufacturing processes.
- 8)
Using hybrid evolutionary systems for solving time-varying optimization problems in manufacturing processes.
Section snippets
The papers in the special issue
We received 65 high quality papers which have been considered for this special issue. After the strict review process, 23 papers were selected to be included in this special issue. They can be classified by two categories which are involved with combinatorial problems and parametrical problems for manufacturing processes. The 1st to 13th papers contribute on solving combinatorial problems on manufacturing processes, while the 15th to 23rd papers contribute on those for parametrical problems.
Special issue guest editors:
Dr. Kit Yan Chan, Department of Electrical and Computer Engineering, Curtin University, WA, Australia
Prof. Tharam Dillon, Department of Computer Science and Computer Engineering, La Trobe University, Australia
Dr. Hak Keung Lam, School of Natural and Mathematical Sciences, King's College London, U.K.
Dr. Steve S.H. Ling, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia
Prof. Hung T. Nguyen, Faculty of Engineering and Information Technology, University
Acknowledgments
Finally, the guest editors of this special issue would like to thank Prof. Rajkumar Roy, Editor of the Applied Soft Computing journal, for providing us with the opportunity to edit this special issue. Thanks to Ms. Kalpana Balaraman and Ms. JoJo Xu for providing the assistance for this special issue on time. We would also like to thank the authors for submitting their valuable research outcomes as well as the reviewers who have critically evaluated the papers. We sincerely hope that readers