10.30684/ etj.29.5.16


The meaning of the Particle Swarm Optimization (PSO) refers to a relatively
new family of algorithms that may be used to find optimal (or near optimal)
solutions to numerical and qualitative problems.
The genetic algorithm (GA) is an adaptive search method that has the ability for a
smart search to find the best solution and to reduce the number of trials and time
required for obtaining the optimal solution.
The aim of this paper is to use the PSO to solve some kinds of two variables function
which submits to optimize function filed. We investigate a comparison study between
PSO and GA to this kind of problems. The experimental results reported will shed
more light into which algorithm is best in solving optimization problems.
The work shows the iteration results obtained with implementation in Delphi
version 6.0 visual programming language exploiting the object oriented tools of
this language.