Ahmed Shawki Jaber
Abstract
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 ...
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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.
