Special Issue on

Particle Swarm Optimization and Genetic Algorithm

PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. In past several years, PSO has been successfully applied in many research and application areas. It is demonstrated that PSO gets better results in a faster, cheaper way compared with other methods.

In this special issue, we invite front-line researchers and authors to submit original research and review articles that explore particle swarm optimization and genetic algorithm. In this special issue, potential topics include, but are not limited to:

• Algorithm principle
• Algorithm parameters
• Discrete particle swarm optimization
• Geometric particle swarm optimization
• Particle swarm optimization and genetic algorithm

Authors should read over the journal’s For Authors carefully before submission. Prospective authors should submit an electronic copy of their complete manuscript through the journal’s Paper Submission System.

Please kindly specify the “Special Issue” under your manuscript title. The research field “Special Issue - Particle Swarm Optimization and Genetic Algorithm” should be selected during your submission.

Special Issue timetable:

Guest Editor:

For further questions or inquiries

Please contact Editorial Assistant at

apm@scirp.org