<span>The economic dispatch is used to find the best optimal output of power generation at the lowest operating cost of each generator, to fulfill the requirements of the consumer. To get a practical solution, several constraints have to be considered, like transmission losses, the valve point effect, prohibited operating region, and emissions. In this research, the valve point effect is to be considered which increases the complexity of the problem due to its ripple effect on the fuel cost curve. Economic load dispatch problems are well-known optimization problems. Many classical and meta-heuristic techniques have been used to get better solutions. However, there is still room for improvement to get an optimal solution for the economic dispatch problem. In this paper, an Improved Flower Pollination Algorithm with dynamic switch probability and crossover operator is proposed to solve these complex optimization problems. The performance of our proposed technique is analyzed against fast evolutionary programming (FEP), modified fast evolutionary programming (MFEP), improved fast evolutionary programming (IFEP), artificial bee colony algorithm (ABC), modified particle swarm optimization (MPSO) and standard flower pollination algorithm (SFPA) using three generator units and thirteen thermal power generation units, by including the effects of valve point loading unit and without adding it. The proposed technique has outperformed other methods in terms of the lowest operating fuel cost.</span>
Hybrid particle swarm optimization: Evolutionary programming approach for solving generation maintenance scheduling problem
