Abstract:

Nature-inspired metaheuristic algorithms are becoming more popular for solving optimization problems because they offer many advantages over traditional numerical optimization techniques. The capacity of nature-inspired algorithms to discover answers in a short amount of time is their most significant benefit. Their speed is one of their most advantageous characteristics, yet it is also one of their biggest liabilities. As a result of the increased speed of operation, there is a possibility of subsidence in the local optimum, the discovery of single solutions for optimization problems containing more than one global minimum, difficulty in replicating the achieved results, and complicated configuration due to the large number of control parameters.