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

Purba Daru Kusuma and Ashri Dinimaharawati, “Hybrid Pelican Komodo Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130607

@article{Kusuma2022,
title = {Hybrid Pelican Komodo Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130607},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130607},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Purba Daru Kusuma and Ashri Dinimaharawati}
}