Comparison of popular metaheuristic optimization algorithms for the optimal design of DC-DC converters

DC-DC converters are an important area of power electronics. They have been used as the power converter interface for power point tracking in photovoltaic systems. The design of the optimized DC-DC converter thus is an important area for the research community. Design optimization of a DC-DC Buck, Boost, Synchronous Buck and Double Buck converters to reduce overall operational losses is the subject of investigation in this study. The ideal design requires selecting the most suitable values for circuit inductance, capacitance, and switching frequency to guarantee functioning in continuous conduction mode (CCM) and continuous voltage mode. The selected design constraints are the ripple content in voltage and current, and bandwidth for operation in CCM. A total of twenty eight (28) recently developed and popular existing metaheuristic optimization algorithms are utilized to select the optimized DC-DC converter’s design. For identifying the best algorithm and to carry out a performance analysis established optimization algorithms like the Grey Wolf Optimizer (GWO), Moth Flame Optimization Algorithm , Particle Swarm optimization, Whale Optimization Algorithm (WOA) and Firefly Algorithm are selected. The simulated results indicate that majority of algorithms are able to select the best design for the converter topologies within the selected constraint criterion’s. The efficacy of an algorithm is determined based on statistical studies, convergence characteristics, computational time and robustness. It is noted that the algorithm that most effectively solves the current optimization problem is the WOA.