PDoublePop: An implementation of parallel genetic algorithm for function optimization

Abstract

A software for the implementation of parallel genetic algorithms is presented in this article. The underlying genetic algorithm is aimed to locate the global minimum of a multidimensional function inside a rectangular hyperbox. The proposed software named PDoublePop implements a client–server model for parallel genetic algorithms with advanced features for the local genetic algorithms such as: an enhanced stopping rule, an advanced mutation scheme and periodical application of a local search procedure. The user may code the objective function either in C++ or in Fortran77. The method is tested on a series of well-known test functions and the results are reported. Program summaryProgram title: PDoublePopCatalogue identifier: AFBJ_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AFBJ_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU GPL v3No. of lines in distributed program, including test data, etc.: 26048No. of bytes in distributed program, including test data, etc.: 161286Distribution format: tar.gzProgramming language: GNU-C++, GNU-C, GNU Fortran - 77, MPI.Computer: The tool has been tested on Linux and FreeBSD. The tool is designed to be portable in all systems running the GNU C++ compiler, with Open MPI or LAM MPI.Operating system: Any running the GNU C++ compiler.Has the code been vectorised or parallelized?: YesRAM: 200KBClassification: 4.9, 4.12, 6.5.Nature of problem:A series of problems in science and engineering usually can be formulated as a problem of minimizing a function of many variables. The so called local optimization techniques are frequently trapped in local minima, that it sub optimal solutions. For that reason researchers should use more advanced methods that aim to estimate the global minimum of the function.Solution method:A stopping rule and a periodical application of local search are utilized in conjunction with parallel genetic algorithms in order to solve optimization problems. The parallelization is performed using the MPI programming interface.Running time:Depending on the objective function.