Hybrid Optimization Algorithms for Economic and Emission Dispatch Optimization

The Artificial Bee Colony (ABC) algorithm is an optimization technique that uses populations and randomness. However, the standard ABC algorithm has limitations, like early convergence and easily getting stuck in local minima in some cases. Hybridizing optimization algorithms is one of the techniques for improving the algorithm’s effectiveness, adaptability, and applicability to various fields of research and application. In this paper, an optimization algorithm named Bee Eel Forage Algorithm (BEFA) has been proposed. BEFA algorithm is developed based on hybridizing techniques where it uses the employed bee phase and scout bee phase from the ABC algorithm and uses the resting phase from the Electric Eel Foraging Optimization (EEFO) algorithm. The efficacy of the BEFA is validated by comparing it with the ABC algorithm and the EEFO algorithm by using four mathematical benchmark functions. Moreover, the applicability of the BEFA is tested with the application of the IEEE 26 bus system to optimize the environmental and economic load dispatch problem. The results show that the BEFA algorithm outperforms the two predecessor algorithms for solving four benchmark functions and successfully minimizes the costs and emissions for the IEEE 26 bus system.