ABSTRACT
Low volatile phosphonium Ionic Liquids (ILs) have proved to be better solvents as compared to volatile organic solvents from the liquid-liquid equilibrium (LLE) experiments. Particle swarm optimization (PSO) is an evolutionary algorithm based on social behavior of birds in swarm. The initial position and velocity of each particle are initiated randomly. During simulation, each particle in swarm (population) updates its position and velocity based on its experience as well as neighbors' experience within the search space. PSO is robust as it evaluates fewer function values during simulation than Genetic Algorithm (GA). PSO shows more success rate to reach the target optimum value than Simulated Annealing (SA). PSO is also more preferable than Ant colony optimization (ACO) due to high success rate and solution quality. The PSO is a stochastic optimization method based on swarm intelligence. Inertial, cognitive and social components that play a major role in effectiveness and performance of PSO update the vectors iteratively.
