TitleHybrid Mesh Adaptive Direct Search and Genetic Algorithms Techniques for industrial production systems
Journal titleArchives of Control Sciences
Divisions of PASNauki Techniczne
Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.
Aims and Scope: Archives of Control Sciences publishes papers in the broadly understood field of control science and related areas while promoting the closer integration of the Polish, as well as other Central and East European scientific communities with the international world of science.
PublisherCommittee of Automatic Control and Robotics PAS
ReferencesAudet C. (2007), Mesh adaptive direct search algorithms for constrained optimization, SIAM Journal on Optimization, 17, 1, 188, doi.org/10.1137/040603371 ; Audet C. (2004), Convergence results for pattern search algorithms are tight, Optimization and Engineering, 5, 2, 101, doi.org/10.1023/B:OPTE.0000033370.66768.a9 ; Abramson M. (2004), Generalized pattern searches with derivative information, Mathematical Programming, 100, 3. ; Audet C. (2003), Analysis of generalized pattern searches, SIAM Journal on Optimization, 13, 3, 889, doi.org/10.1137/S1052623400378742 ; Chapman S. (2002), MATLAB programming for engineers. ; Coope I. (2001), On the convergence of grid-based methods for unconstrained optimization, SIAM Journal on Optimization, 11, 4, 859, doi.org/10.1137/S1052623499354989 ; Davis C. (1954), Theory of positive linear dependence, American J. of Mathematics, 76, 733, doi.org/10.2307/2372648 ; Gilat A. (2005), MATLAB. An introduction with applications. ; Gen M. (1996), Genetic Algorithms and Engineering Design, doi.org/10.1002/9780470172254 ; Honggang W. (1997), The hybrid genetic algorithm for solving nonlinear programming, null. ; Goldberg D. (1989), Genetic Algorithms in search optimization and machine learning. ; Jiménez F. (2006), Multi-objective evolutionary computation and fuzzy optimization, Int. J. of Approximate Reasoning, 43, 59, doi.org/10.1016/j.ijar.2006.02.001 ; MATLAB user's Guide. The MathWorks, 2007. ; Vasant P. (2009), Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function, Engineering Applications of Artificial Intelligence, 22, 4-5, 767, doi.org/10.1016/j.engappai.2009.03.010 ; Vasant P. (2010), Hybrid simulated annealing and genetic algorithms for industrial production management problems, Int. J. of Computational Methods, 7, 2, 279, doi.org/10.1142/S0219876210002209 ; Vasant P. (2010), Hybrid pattern search and simulated annealing for fuzzy production planning problems, Computers and Mathematics with Application, 60, 4, 1058, doi.org/10.1016/j.camwa.2010.03.063