Multi-Objective Optimization of Solar Thermal Energy Storage Using Hybrid of Particle Swarm Optimization and Multiple Crossover and Mutation Operator

Increasing of net energy storage (Q net) and discharge time of phase change material (t PCM), simultaneously, are important purpose in the design of solar systems. In the present paper, Multi-Objective (MO) based on hybrid of Particle Swarm Optimization (PSO) and multiple crossover and mutation operator is used for Pareto based optimization of solar systems. The conflicting objectives are Q net and t PCM and design variables are the geometrical parameters of solar system. The Pareto results of MO hybrid of PSO and multiple crossover and mutation operator methods are compared with that of multi-objective genetic algorithms (NSGA II). It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of solar systems can be discovered.