Resumen
Stochastic optimization algorithms are usually evaluated based on performance on high dimensional benchmark functions and results of these tests determine the direction of development. Benchmark functions however, do not emulate complex engineering problems. In this paper a power plant optimization problem is presented and solved under different constraints with multiple elite dependent and single elite dependent swarm intelligence. Although on benchmark problems multiple elite dependent algorithms usually outperform single elite dependent ones, if search space is represented by simulation software, diversity not just increases iterations but computation time as well and because of that conventional PSO (particle swarm optimization) exceeds modified ones.