Guided Pelican Algorithm
Purba Daru Kusuma, Anggunmeka Luhur Prasasti, J Lee, M Ahmadian, A Salehipour, T Cheng, J Fowler, L Monch, Z Peya, M Akhnand, T Sultana, M Rahman, P Kusuma, M Kallista, S Ngo, J Jaafar, I Aziz, B Anh, P Kusuma, A Albana, H Moshtaghi, A Eshlagy, M Motadel, Y Qawqzeh, M Alharbi, A Jaradat, K Sattar, D Freitas, L Lopes, F Dias, A Suyanto, A Ariyanto, Ariyanto, F Rezaei, H Safavi, M Elaziz, S Sappagh, M Betar, T Abuhmed, A Faramarzi, M Heidarinejad, S Mirjalili, A Gandomi, M Dehghani, M Mardaneh, J Guerrero, O Malik, V Kumar, M Dehghani, Z Montazeri, S Saremi, A Dehghani, O Malik, K Haddad, J Guerrero, M Dehghani, Z Montazeri, H Givi, J Guerrero, G Dhiman, W Chang, W Cheng, A Ibrahim, F Anayi, M Packianather, O Alomari, F Awad, A, M Mahmood, P Trojovsky, M Dehghani, M Dehghani, S Hubalovsky, P Trojovsky, M Noroozi, H Mohammadi, E Efatinasab, A Lashgari, M Eslami, B Khan, M Dehghani, S Hubalovsky, P Trojovsky, M Dehghani, P Trojovsky, F Zeidabadi, S Doumari, M Dehghani, O Malik, M Dehghani, Z Montazeri, A Dehghani, R Mendoza, H Samet, J Guerrero, G Dhiman, F Zeidabadi, M Dehghani, O Malik, P Kusuma
Abstract
This paper presented a new metaheuristic technique, namely the guided pelican algorithm (GPA). GPA has the improvements for a shortcoming algorithm, namely the pelican optimization algorithm (POA), that mimics the behaviour of pelican birds during hunting prey. It improves the original POA in three ways. First, GPA replaces the randomized target with the global best solution as a deterministic target in phase one. Second, GPA replaces the pelican's current location with the search space size in determining the local search space size in phase two. Third, GPA implements multiple candidates in both phases rather than a single candidate as it is used in the original POA. Simulation is implemented to observe GPA's performance in optimizing both theoretical and real-world problems. GPA is compared with four algorithms: marine predator algorithm (MPA), particle swarm optimization (PSO), komodo mlipir algorithm (KMA), and POA. The result shows that GPA outperforms all sparing algorithms in optimizing most benchmark functions. GPA is also implemented to optimize the portfolio problem. The result shows that GPA outperforms all sparing algorithms in optimizing the portfolio problem. It outperforms three sparing algorithms in optimizing the portfolio problem. Its performance is 9%, 11%, and 13%, better than PSO, MPA, and KMA consecutively. Meanwhile, its less than 1% worse than POA.