Combined Heuristic Optimization Techniques for Global Minimization

This paper presents Combined Heuristic Optimization Techniquesof Particle Swarm Optimization (PSO) algorithm with SimulatedAnnealing (SA). Particle Swarm Optimization is Swarm Intelligencebased algorithm to find a solution to an optimization problem insearch space. SA is a generic probabilistic metaheuristic for locatingthe global minimum of a given function in a large search space. Instandard PSO the non-oscillatory route can quickly cause a particleto stagnate and also it may prematurely converge on suboptimalsolutions that are not even guaranteed to local optimal solution. Theproposed system improves the solution by incorporating the workingprinciples of SA to Standard PSO to diversify the particle position. Experiment results are examined with benchmark functions. Itdemonstrates that the proposed PSO outperforms the standard PSO