Combined economic and emission power dispatch problems through multi-objective Honey Badger optimizer

Honey Badger algorithm (HBA) is an intelligent adaptive meta-heuristic optimization algorithm with few parameters, fast convergence and good convergence accuracy for single-objective problems. However, many real-world optimization problems involve multiple conflicting objectives that need to be optimized simultaneously. A new multi-objective Honey Badger algorithm is proposed to solve the combined economic and environmental power scheduling problem. The proposed MOHBA combines the HBA with the Pareto dominance principle to produce a non-dominated solution. It uses an external elite storage mechanism with congested distance ordering to maintain the diversity of the distribution during the evolution of the Pareto optimal solutions. Furthermore, a fuzzy decision strategy is used to select the best compromise solution from the obtained Pareto bound. Then, to validate the performance of the proposed MOHBA, 20 different benchmark test functions are used to test it against other multi-objective optimization techniques. Moreover, the method is implemented on the multi-objective CEEPD problem for the IEEE 30-bus 6 generator and IEEE 118-bus 14 generator systems. Various objective function s in a multi-objective optimization space is confirmed by comparative studies with minimization schemes and fuzzy decision strategies are utilized to achieve the best scheduling solution for energy and emissions savings. The predominance of the algorithm and its potentiality to handle CEEPD problem several other algorithms.