NEW META-HEURISTIC OPTIMIZATION ALGORITHM USING NEURONAL COMMUNICATION

A new meta-heuristic method, based on Neuronal Communication (NC), is introduced in this article. The neuronal communication illustrates how data is exchanged between neurons in neural system. Actually, this pattern works efficiently in the nature. The present paper shows it is the same to find the global minimum. In addition, since few numbers of neurons participate in each step of the method, the cost of calculation is less than the other comparable meta-heuristic methods. Besides, gradient calculation and a continuous domain are not necessary for the process of the algorithm. In this article, some new weighting functions are introduced to improve the convergence of the algorithm. In the end, various benchmark functions and engineering problems are examined and the results are illustrated to show the capability, efficiency of the method. It is valuable to note that the average number of iterations for fifty independent runs of functions have been decreased by using Neuronal Communication algorithm in comparison to a majority of methods.

[1]  L. Schmit,et al.  Approximation concepts for efficient structural synthesis , 1976 .

[2]  E. Sandgren,et al.  Nonlinear Integer and Discrete Programming in Mechanical Design Optimization , 1990 .

[3]  John R. Koza,et al.  Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems , 1990 .

[4]  K. Lee,et al.  A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice , 2005 .

[5]  F. Glover.  HEURISTICS FOR INTEGER PROGRAMMING USING SURROGATE CONSTRAINTS , 1977 .

[6]  Paulo Rizzi,et al.  Optimization of multi-constrained structures based on optimality criteria , 1976 .

[7]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[8]  A. Kaveh,et al.  A new meta-heuristic method: Ray Optimization , 2012 .

[9]  Siamak Talatahari,et al.  An improved ant colony optimization for constrained engineering design problems , 2010 .

[10]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[11]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Ramin Sedaghati,et al.  Benchmark case studies in structural design optimization using the force method , 2005 .

[13]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[14]  Behrooz Farshi,et al.  Sizing optimization of truss structures by method of centers and force formulation , 2010 .

[15]  V. Venkayya.  Design of optimum structures , 1971 .

[16]  L. A. Schmit,et al.  Some approximation concepts for structural synthesis , 1973 .

[17]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[18]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[19]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[20]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[21]  Q. H. Wu,et al.  A heuristic particle swarm optimizer for optimization of pin connected structures , 2007 .

[22]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[23]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..

[24]  Carlos A. Coello Coello,et al.  An empirical study about the usefulness of evolution strategies to solve constrained optimization problems , 2008, Int. J. Gen. Syst..

[25]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[26]  Ali Kaveh,et al.  Analysis, design and optimization of structures using force method and genetic algorithm , 2006 .

[27]  Luciano Lamberti,et al.  Move limits definition in structural optimization with sequential linear programming. Part I: Optimization algorithm , 2003 .

[28]  K. G. Sharma,et al.  Minimum weight design of trusses using improved move limit method of sequential linear programming , 1987 .

[29]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[30]  Ioannis G. Tsoulos,et al.  Modifications of real code genetic algorithm for global optimization , 2008, Appl. Math. Comput..

[31]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.