Environmental-economic dispatch using stochastic fractal search algorithm

Stochastic fractal search (SFS) is one of the latest metaheuristic global optimization algorithms, which has been introduced in 2015. It is a very promising algorithm and outperforms many of existing well-known metaheuristic algorithms. This paper uses SFS algorithm to solve the highly nonlinear environmental-economic dispatch problem in power systems operations considering generator physical constraints, gas emission level, and transmission line losses and limits. To verify the effectiveness of using this algorithm, it has been applied in this paper to different commonly used test systems. The results are compared with previous results obtained by 8 different methods such as genetic algorithm-based methods, particle swarm optimization-based methods, differential evolution-based methods, and gravitational search algorithm. Results will reveal that the optimal SFS-based optimal solution can reduce the total system production cost and will also show that the optimal solutions obtained using the SFS algorithm perform better than many of the commonly used global optimization techniques, where the proposed algorithm has achieved a nearer global optimal solution compared to the other approaches with a short time.