Review on Single & Multi-objective Optimization Techniques

In this paper a comparison between the single and multi-objective based Optimization techniques including GA, PSO and Hybrid will be presented. The hybrid technique is combined of two attractive evolutionary techniques, Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA) to enhance the search process by improving the diversity, and the convergence toward the preferred solution. This is not only modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). This enables a faster convergence without degrading the estimated set of solutions. Indeed, the population diversity is correctly conserved during the optimization process. The previously experiment done by the researchers are retested to show the comparison among the optimization techniques.