Optimal Power Flow Based on Novel Multi-objective Artificial Fish Swarm Algorithm

Computer technology provides new possibilities for handling the many-objective optimal power flow (MOOPF) problems with high-dimension and non-differentiability. As one of typical intelligent algorithms, the novel multi-objective artificial fish swarm algorithm (NMAFSA) is proposed to solve the MOOPF problems and realize the economical operation of power systems. The NMAFSA algorithm, which combines with optimal solution guidance (OSG) principle and non-inferior retention (NIR) mechanism, is effective to reduce the fuel cost, emission and power loss. Compared with the representative many-objective particle swarm optimization (MPSO) and non-dominated sorting genetic algorithm-II (NSGA-II), the superiority and adaptability of presented NMAFSA algorithm are validated. Six simulation trials are carried out on MATLAB software, including the dual-objective and triple-objective optimizations on three different scale power systems. Detailed results demonstrate that the suggested NMAFSA algorithm with stable-operation and fast-convergence has great potential to deal with the MOOPF problems more efficiently. Furthermore, the generation distance (GD) index also quantitatively proves that the NMAFSA algorithm can obtain the well-distributed Pareto front (PF).