p-MEMPSODE: Parallel and irregular memetic global optimization

Published: 1 January 2015| Version 1 | DOI: 10.17632/mnh3x6kcdt.1
C. Voglis, P.E. Hadjidoukas, K.E. Parsopoulos, D.G. Papageorgiou, I.E. Lagaris, M.N. Vrahatis


Abstract A parallel memetic global optimization algorithm suitable for shared memory multicore systems is proposed and analyzed. The considered algorithm combines two well-known and widely used population-based stochastic algorithms, namely Particle Swarm Optimization and Differential Evolution, with two efficient and parallelizable local search procedures. The sequential version of the algorithm was first introduced as MEMPSODE (MEMetic Particle Swarm Optimization and Differential Evolution) and publ... Title of program: p-MEMPSODE Catalogue Id: AEXJ_v1_0 Nature of problem Numerical global optimization of real valued functions is an indispensable methodology for solving a multitude of problems in science and engineering. Many problems exhibit a number of local and/or global minimizers, expensive function evaluations or require real-time response. In addition, discontinuities of the objective function, non-smooth and deceitful landscapes constitute challenging obstacles for most optimization algorithms. Versions of this program held in the CPC repository in Mendeley Data AEXJ_v1_0; p-MEMPSODE; 10.1016/j.cpc.2015.07.011 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)



Computational Physics, Computational Method