Vorob'eva

This paper deals with a co-evolutionary algorithm of global optimisation, Co-PSO, based on particle swarm optimisation. Implementation of this algorithm developed in MATLAB was described in this work. This software is oriented to a parallel operation of the given number of PSO algorithms at the logical level. These algorithms use various neighbourhood topologies of particles and/or various values of their free parameters. Results of a comprehensive numerical experiment on performance study of Co-PSO and its implementation were also presented. The experiment was based on Rosenbrock, Himmelblau and Rastrigin benchmark functions. Experimental results show superiority of the Co-PSO algorithm over the canonical PSO. A three-criterion problem of optimal control for a spacecraft during its reentry was solved with the use of Co-PSO. Methods of additive scalar convolution and reduction of the optimal control problem to a nonlinear programming problem were used in this work.

This article deals with co-algorithmic hybridization of two Particle Swarm Optimization (PSO) algorithms, using click-type and ring-type neighborhood topologies of particles. Each of these topologies has their disadvantages which can be eliminated by using co-algorithmic hybridization. The authors compared the efficiency of canonical PSO and its co-algorithmic modification and found optimal values of free parameters.