|Global Journal of Technology and Optimization is top most indexed journal in Particle Swarm Optimization. GJTO publishes: Original research articles, Case studies, rapid communications Critical reviews, letters to the editor, conference proceedings, surveys, opinions. Our comprehensive team helps you to distribute your published paper.
Particle Swarm Optimization (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.
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