A Novel Hybrid Genetic Algorithms and Pattern Search Techniques for Industrial production Planning

Soft computing has attracted many research scientists, decision makers and practicing researchers in recent years as powerful computational intelligent techniques, for solving unlimited number of complex real-world problems particularly related to research area of optimization. Under the uncertain and turbulence environment, classical and traditional approaches are unable to obtain a complete solution with satisfaction for the real-world problems on optimization. Therefore, new global optimization methods are required to handle these issues seriously. One such method is hybrid Genetic algorithms and Pattern search, a generic, flexible, robust, and versatile framework for solving complex problems of global optimization and search in real-world applications.

[1]  Yixin Chen,et al.  Hybrid Evolutionary And Annealing Algorithms For Nonlinear Discrete Constrained Optimization , 2003, Int. J. Comput. Intell. Appl..

[2]  D. Dennett.  Darwin's Dangerous Idea , 1995 .

[3]  Tien-Fu Liang,et al.  Interactive Multi-Objective Transportation Planning Decisions Using Fuzzy, Linear Programming , 2008, Asia Pac. J. Oper. Res..

[4]  Robert Michael Lewis,et al.  Pattern Search Methods for Linearly Constrained Minimization , 1999, SIAM J. Optim..

[5]  Zbigniew Michalewicz,et al.  A Hierarchy of Evolution Programs: An Experimental Study , 1993, Evolutionary Computation.

[6]  Robert Mifflin,et al.  A superlinearly convergent algorithm for minimization without evaluating derivatives , 1975, Math. Program..

[7]  Pandian Vasant,et al.  Introduction to Fuzzy Logic and Fuzzy Linear Programming , 2008 .

[8]  Pandian Vasant,et al.  Fuzzy Production Planning and its Application to Decision Making , 2006, J. Intell. Manuf..

[9]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[10]  M. J. Box.  A New Method of Constrained Optimization and a Comparison With Other Methods , 1965, Comput. J..

[11]  David M. Himmelblau,et al.  Constrained Nonlinear Optimization by Heuristic Programming , 1969, Oper. Res..

[12]  Fernando Jiménez,et al.  Fuzzy Optimization with Multi-Objective Evolutionary Algorithms: a Case Study , 2007, 2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making.

[13]  Pandian Vasant,et al.  Simulating Theory-of Constraint Problem with Novel Fuzzy Compromise Linear Programming Model , 2008 .

[14]  J. Dennis,et al.  Pattern search algorithms for mixed variable general constrained optimization problems , 2003 .

[15]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

[16]  Virginia Torczon,et al.  On the Convergence of Pattern Search Algorithms , 1997, SIAM J. Optim..

[17]  Antonio F. Gómez-Skarmeta,et al.  Nonlinear Optimization with Fuzzy Constraints by Multi-Objective Evolutionary Algorithms , 2004, Fuzzy Days.

[18]  Pandian Vasant,et al.  Soft-sensing of level of satisfaction in TOC product-mix decision heuristic using robust fuzzy-LP , 2007, Eur. J. Oper. Res..

[19]  Pandian Vasant,et al.  Improved Tabu Search Recursive fuzzy method for Crude Oil Industry , 2012, Int. J. Model. Simul. Sci. Comput..

[20]  Pandian Vasant,et al.  Fuzzy technique for optimization of objective function with uncertain resource variables and technological coefficients , 2009, 2009 Innovative Technologies in Intelligent Systems and Industrial Applications.

[21]  Hans-Paul Schwefel,et al.  Direct search for optimal parameters within simulation models , 1979 .

[22]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[23]  Wang Honggang,et al.  The hybrid genetic algorithm for solving nonlinear programming , 1997, 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335).

[24]  Frédéric Adam,et al.  Encyclopedia of Decision Making and Decision Support Technologies , 2008 .

[25]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[26]  Charles Audet,et al.  Analysis of Generalized Pattern Searches , 2000, SIAM J. Optim..

[27]  Pandian Vasant,et al.  Detection of level of satisfaction and fuzziness patterns for MCDM model with modified flexible S-curve MF , 2007, Appl. Soft Comput..

[28]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[29]  Charles Audet.  Convergence Results for Generalized Pattern Search Algorithms are Tight , 2004 .

[30]  Charles L. Karr,et al.  Genetic algorithms for fuzzy controllers , 1991 .

[31]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[32]  S. K. Mukherjee,et al.  A fully fuzzified, intelligent theory-of-constraints product-mix decision , 2008 .

[33]  Pandian Vasant,et al.  Fuzzy optimization of units products in mix-product selection problem using fuzzy linear programming approach , 2006, Soft Comput..

[34]  D. Keefer.  Simpat: Self-Bounding Direct Search Method for Optimization , 1973 .

[35]  Charles Audet,et al.  Convergence Results for Pattern Search Algorithms are Tight , 2002 .

[36]  Tamara G. Kolda,et al.  Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods , 2003, SIAM Rev..

[37]  Mordecai Avriel,et al.  Mathematical Programming for Industrial Engineers , 1997 .

[38]  Georgi M. Dimirovski,et al.  Application of Fuzzy Optimization in Forecasting and Planning of Construction Industry , 2008 .