Application of Harris Hawks and Whale Optimization Algorithm with Constraint Handling Techniques: A comparative study

Yıl 2021, Cilt: 4 Sayı: 2, 76 - 85, 23.09.2021

Zeynep Garip Murat Erhan Çimen Ali Fuat Boz

https://doi.org/10.38016/jista.857881

Cited By: 3

Öz

This study focuses on the effect of meta-heuristic algorithms inspired by nature in solving constrained optimization problems. Death penalty, static penalty, dynamic penalty, barrier function and Deb feasibility rule, which are the constraint handling techniques, were tested on Whale Optimization (WOA) and Harris Hawk Optimization (HHO) algorithms. Unconstrained and constrained benchmark functions and optimal power flow minimization problem were used to test the performance of algorithms. Furthermore, in order to compare the performance of WOA and HHO algorithms in optimal power flow, it was used with algorithms found in the literature.. As a result, it has been observed that algorithms integrated into constraint handling methods are effective in solving constrained optimization problems.

Anahtar Kelimeler

Meta-heuristic Algorithm, Constraint Handling Methods, Optimal Power Flow;, Optimization

Harris Şahinleri ve Balina Optimizasyon Algoritmalarının Kısıt İşleme Teknikleriyle Uygulaması: Karşılaştırmalı bir çalışma

Öz

Bu çalışmada kısıtlı optimizasyon problemlerin çözümünde, doğadan ilham alan meta-sezgisel algoritmaların etkisine odaklanılmıştır. Kısıtlı ihlal tekniklerinden olan ölüm ceza, statik ceza, dinamik ceza, bariyer fonksiyon ve Deb uygulanabilirlik kuralı Balina Optimizasyon (BOA) ve Harris Şahini Optimizasyon (HŞO) algoritmaları üzerinde test edilmiştir. Algoritmaların performansını test etmede kısıtsız ve kısıtlı benchmark fonksiyonları ve optimal güç akışı minimizasyon problemi kullanılmıştır. Ayrıca BOA ve HŞO algoritmaların optimal güç akışında gösterdikleri performanslarını karşılaştırmak amacıyla literatürde bulunan algoritmalarla kullanılmıştır. Sonuç olarak kısıt ihlal yöntemlerine entegre edilmiş algoritmaların kısıtlı optimizasyon problemlerin çözümünde etkili olduğu görülmüştür

Anahtar Kelimeler

Meta-sezgisel Algoritma, Kısıt İhlal Yöntemleri, Optimal Güç Akışı, Optimizasyon.

Kaynakça

• Akay B., Karaboga D., 2011. A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems. Applied Soft Computing, 11, 3021–3031.
• Aljarah, I., Chen, H., Faris, H., Heidari, A. A., Mafarja, M., Mirjalili, S. 2019. Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872.
• Amaratunga, G.A.J., Biswas, P.P., Mallipeddi, R., Suganthan P.N., 2018.Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Engineering Applications of Artificial Intelligence, 68, 81–100.
• Babalik, A., Cinar, A.C., Kiran M.S., 2018, A modification of tree-seed algorithm using Deb’s rules for constrained optimization. Applied Soft Computing, 63, 289–305.
• Batık, Z. G., Boz, A. F., Çimen, M.E., Karayel D.2019. The Chaos-Based Whale Optimization Algorithms Global Optimization, Chaos Theory and Applications, 1, 51-63.
• Birogul, S. 2019. Hybrid harris hawk optimization based on differential evolution (HHODE) algorithm for optimal power flow problem. IEEE Access, 7, 184468-184488.
• Bouktir, T.,Slimani, L., Mahdad, B. 2008. Optimal power dispatch for large scale power system using stochastic search algorithms. International Journal of Power and Energy Systems, 28(2), 118.
• Chen S., Gu Y., Jiang, S., Nouioua, M., Li Z., Zhang, S., 2019. FSB-EA: Fuzzy search bias guided constraint handling technique for evolutionary algorithm. Expert Systems with Applications, 119, 20–35.
• Chen, H., Wang, M., Zha X., 2020. A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems. Applied Mathematics and Computation, 369, 124872.
• Deb, K., Gandomi, A.H., 2020 .Implicit constraints handling for efficient search of feasible solutions. Comput. Methods Appl. Mech. Engrg. 363, 112917. Dzubera, J., Mathias, K., Rana S., D. Whitley, 1996. Evaluating Evolutionary Algorithms. Artif Intell, 85, 245-276.
• Fan, Q.-W., He, X.-S., Karamanoglu M. and Yang, X.-S., 2019. Comparison of Constraint-Handling Techniques for Metaheuristic Optimization. ICCS 2019: Computational Science, 357-366.
• Garcia R., Jacob, B. P., Lemonge, A., Lima, B., 2017. A rank-based constraint handling technique for engineering design optimization problems solved by genetic algorithms. Computers and Structures, 187, 77–87.
• Guimarães, S., Lima, B., Rodrigues, M., 2018. E-BRM: A constraint handling technique to solve optimization problems with evolutionary algorithms. Applied Soft Computing, 72, 14–29.
• Haklı H., 2019. A novel approach based on elephant herdıng optımızatıon for constraıned optımızatıon problems. Selcuk Univ. J. Eng. Sci. Tech., 7,405-419.
• Hatamlou, A., Mirjalili, S., Mirjalili, S. M., 2016. Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27, 495-513.
• He W., Peng, X., Peng, X., Qu, C. 2020. Harris Hawks optimization with information Exchange, Applied Mathematical Modelling, 84, 52–75.
• Islam, M.Z., Abdul Wahab, N.I., Veerasamy, V., Hizam, H., Mailah, N.F., Guerrero, J.M., Mohd Nasir M.N., 2020. A Harris Hawks Optimization Based Single-and Multi-Objective Optimal Power Flow Considering Environmental Emission. Sustainability, 12(13), 5248.
• Jelovic, J., Samanipour, F., 2020. Adaptive repair method for constraint handling in multi-objective genetic algorithm based on relationship between constraints and variables. Applied Soft Computing Journal, 90, 106-143.
• Kohler, M., Tanscheit, R., Vellasco, M.M.B.R., 2019. PSO+: A new particle swarm optimization algorithm for constrained problems. Applied Soft Computing, 85, 105865.
• Lewis, A., Mirjalili, S., 2016. The whale optimization algorithm. Advances in Engineering Software, 95, 5167.
• Mert, A., Tezel, B.T., 2021. A cooperative system for metaheuristic algorithms. Expert Systems with Applications, 165, 113976.
• Miranda-Varela, M.,, Mezura-Montes, E. 2018. Constraint-handling techniques in surrogate-assisted evolutionary optimization. An empirical study. Applied Soft Computing Journal 73, 215–229.
• Sayah, S., Zehar, K. 2008. Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy conversion and Management, 49(11),3036-3042.
• Tuba, M. and Bacanin, N., 2014. Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems. Neuro computing, 143, 197-207.