Hybrid Pelican Komodo Algorithm

In this work, a new metaheuristic algorithm, namely the hybrid pelican Komodo algorithm (HPKA), has been proposed. This is developed by hybridizing two shortcoming algorithms: Pelican Optimization Algorithm (POA) and Mlipir (KMA). Through hybridization, proposed designed to adapt advantages of both POA KMA. Several improvisations regarding are as follows. First, replaces randomized target with preferred in first phase. Second, four possible movements selected stochastically Third, second phase, agent’s current location problem space width control local space. then challenged tackle theoretical real-world optimization problems. The result shows that better than grey wolf optimizer (GWO), marine predator (MPA), KMA, solving 14, 12, 18 functions. Meanwhile, creates 109%, 46%, 47%, 1% total capital gain rather GWO, MPA, POA, respectively portfolio problem.