Abstract This paper presents Combined Heuristic Optimization Techniques of Particle Swarm Optimization (PSO) algorithm with Simulated Annealing (SA). Particle Swarm Optimization is Swarm Intelligence based algorithm to find a solution to an optimization problem in search space. SA is a generic probabilistic metaheuristic for locating the global minimum of a given function in a large search space. In standard PSO the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on suboptimal solutions that are not even guaranteed to local optimal solution. The proposed system improves the solution by incorporating the working principles of SA to Standard PSO to diversify the particle position. Experiment results are examined with benchmark functions. It demonstrates that the proposed PSO outperforms the standard PS
