GenAnneal: Genetically modified Simulated Annealing

Abstract

A modification of the standard Simulated Annealing (SA) algorithm is presented for finding the global minimum of a continuous multidimensional, multimodal function. We report results of computational experiments with a set of test functions and we compare to methods of similar structure. The accompanying software accepts objective functions coded both in Fortran 77 and C++. Program summary: Title of program:GenAnneal. Catalogue identifier:ADXI_v1_0. Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADXI_v1_0. Program available from: CPC Program Library, Queen's University of Belfast, N. Ireland. Computer for which the program is designed and others on which it has been tested: The tool is designed to be portable in all systems running the GNU C++ compiler. Installation: University of Ioannina, Greece on Linux based machines. Programming language used:GNU-C++, GNU-C, GNU Fortran 77. Memory required to execute with typical data: 200 KB. No. of bits in a word: 32. No. of processors used: 1. Has the code been vectorized or parallelized?: No. No. of bytes in distributed program, including test data, etc.:84 885. No. of lines in distributed program, including test data, etc.:14 896. Distribution format: tar.gz. Nature of physical problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a non-linear system of equations via optimization, employing a "least squares" type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Typical running time: Depending on the objective function. Method of solution: We modified the process of step selection that the traditional Simulated Annealing employs and instead we used a global technique based on grammatical evolution. © 2006 Elsevier B.V. All rights reserved.