Performance evaluation of anti-predatory nature-inspired algorithm for small scale to very high scale problems

Anti-Predatory Nature-Inspired Algorithm (APNIA) is designed to emulate the anti-predatory behaviour of frogs in order to solve optimization problems. It has a good convergence rate and offers a good balance between exploration and exploitation. It preserves diversity during the optimization process. This work aims to test the strength and resilience of APNIA by finding the optimal solution for small to very high-scale unconstrained optimization problems. This work also evaluates the convergence analysis of APNIA. The performance and convergence measures of APNIA are evaluated against the well-known nature-inspired algorithms and over well-known problems through the four experimental evaluations. Our experimental evaluations interpret the efficient and effective performance of APNIA.