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A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

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P. Dziwiński, L. Bartczuk, J. Paszkowski. A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm. Journal of Artificial Intelligence and Soft Computing Research, 10(2): 95-111, 2020. Search in Google Scholar

F. Jia, S. Luo, G. Yin, Y. Ye. A novel variant of the salp swarm algorithm for engineering optimization. Journal of Artificial Intelligence and Soft Computing Research, 13, 2023. Search in Google Scholar

J. Xia, H. Zhang, R. Li, Z. Wang, Z. Cai, Z. Gu, H. Chen, Z. Pan. Adaptive barebones salp swarm algorithm with quasi-oppositional learning for medical diagnosis systems: A comprehensive analysis. Journal of Bionic Engineering, 2022. Search in Google Scholar

Z. Wang, H. Ding, B. Li, L. Bao, and Z. Yang. An energy efficient routing protocol based on improved artificial bee colony algorithm for wireless sensor networks. IEEE Access, 8: 133577-133596, 2020. Search in Google Scholar

J. Xia, H. Zhang, R. Li, H. Chen, H. Turabieh, M. Mafarja, Z. Pan. Generalized oppositional moth flame optimization with crossover strategy: An approach for medical diagnosis. Journal of Bionic Engineering, 18: 991-1010, 2021. Search in Google Scholar

X. Wang, X. Dong, Y. Zhang, H. Chen. Crisscross harris hawks optimizer for global tasks and feature selection. Journal of Bionic Engineering, 2022. Search in Google Scholar

C. Lin, P. Wang, X. Zhao, and H Chen. Double mutational salp swarm algorithm: From optimal performance design to analysis. Journal of Bionic Engineering, 2022. Search in Google Scholar

J. H. Holland. Genetic algorithms. Scientific American, 267(1): 66-73, 1992. Search in Google Scholar

H. G. Beyer, H. P. Schwefel. Evolution strategies–a comprehensive introduction. Natural computing, 1: 3-52 2002. Search in Google Scholar

S. Das, P. N. Suganthan. Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1): 4-31, 2010. Search in Google Scholar

X. Yao, Y. Liu, and G. Lin. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2): 82-102, 1999. Search in Google Scholar

L. Hu, F. Lin, H. Li, C. Tong, Z. Pan, J. Liu, and H Chen. An intelligent prognostic system for analyzing patients with paraquat poisoning using arterial blood gas indexes. Journal of Pharmacological and Toxicological Methods, 84: 78-85, 2017. Search in Google Scholar

J. Kennedy, and R. Eberhart. Particle swarm optimization. in: Proceedings of ICNN’95-international conference on neural networks, 4: 1942–1948, IEEE, 1995. Search in Google Scholar

X.-S. Yang. Firefly algorithm: recent advances and applications. International Journal of Swarm Intelligence, 1(1): 36–50, 2013. Search in Google Scholar

Z. Wang, H. Ding, B. Li, L. Bao, Z. Yang, and Q. Liu. Energy efficient cluster based routing protocol for WSN using firefly algorithm and ant colony optimization. Wireless Personal Communications, 125(3): 2167-2200, 2022. Search in Google Scholar

D. Karaboga, and B. Basturk. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. Journal of Global Optimization, 39:459–471, 2007. Search in Google Scholar

M. Dorigo, M. Birattari, and T. Stutzle. Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4): 28–39, 2006. Search in Google Scholar

S. Mirjalili, S. M. Mirjalili, and A. Lewis. Grey wolf optimizer. Advances in Engineering Software, 69:(46–61), 2014. Search in Google Scholar

S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, and H. Faris. Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114: 163–191, 2017. Search in Google Scholar

G. Dhiman, and V. Kumar. Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114: 48–70, 2017. Search in Google Scholar

G. Dhiman, and V. Kumar. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165: 169-196, 2019. Search in Google Scholar

A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen. Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97: 849-872, 2019. Search in Google Scholar

S. Mirjalili. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89: 228-249, 2015. Search in Google Scholar

A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. Gandomi. Marine predators algorithm: A nature-inspired metaheuristic. Expert Systems with Applications, 152: 113377, 2020. Search in Google Scholar

R. Salgotra, and U. Singh. The naked mole-rat algorithm. Neural Computing and Applications, 31: 8837-8857, 2019. Search in Google Scholar

S. Li, H. Chen, M. Wang, A. A. Heidari, and S. Mirjalili. Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111: 300-323, 2020. Search in Google Scholar

J. Tu, H. Chen, M. Wang, and A. H. Gandomi. The colony predation algorithm. Journal of Bionic Engineering, 18: 674-710, 2021. Search in Google Scholar

G. G. Wang, S. Deb, and Z. Cui. Monarch butterfly optimization. Neural Computing and Applications, 31: 1995-2014, 2019. Search in Google Scholar

H. Zhang, T. Liu, X. Ye, A. A. Heidari, G. Liang, H. Chen, and Z. Pan. Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems. Engineering with Computers, 39(3): 1735-1769, 2023. Search in Google Scholar

R. Venkata Rao, V. J. Savsani, and D. P. Vakharia. Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3): 303-315, 2011. Search in Google Scholar

E. Atashpaz-Gargari, and C. Lucas. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation, 4661-4667, IEEE, 2007. Search in Google Scholar

M. Kumar, A. J. Kulkarni, and S. C. Satapathy. Socio evolution and learning optimization algorithm: A socio-inspired optimization methodology. Future Generation Computer Systems, 81: 252-272, 2018. Search in Google Scholar

Y. Yang, H. Chen, A. A. Heidari, and A. H. Gandomi. Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177: 114864, 2021. Search in Google Scholar

S. H. S. Moosavi, and V. K. Bardsiri. Poor and rich optimization algorithm: A new human-based and multi populations algorithm. Engineering Applications of Artificial Intelligence, 86: 165-181, 2019. Search in Google Scholar

L. Ren, A. A. Heidari, Z. Cai, Q. Shao, G. Liang, H. L. Chen, and Z. Pan. Gaussian kernel probability-driven slime mould algorithm with new movement mechanism for multi-level image segmentation. Measurement, 192: 110884, 2022. Search in Google Scholar

D. Bertsimas, and J. Tsitsiklis. Simulated annealing. Statistical Science, 8(1): 10-15, 1993. Search in Google Scholar

O. K. Erol, and I. Eksin. A new optimization method: big bang–big crunch. Advances in Engineering Software, 37(2): 106-111, 2006. Search in Google Scholar

E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi. GSA: a gravitational search algorithm. Information Sciences, 179(13): 2232-2248, 2009. Search in Google Scholar

S. Mirjalili, and S. M. Mirjalili. Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27: 495-513, 2016. Search in Google Scholar

H. Shah-Hosseini. Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimization. International Journal of Computational Science and Engineering, 6(1-2): 132-140, 2011. Search in Google Scholar

V. Goodarzimehr, S. Shojaee, S. Hamzehei-Javaran, and S. Talatahari. Special relativity search: A novel metaheuristic method based on special relativity physics. Knowledge-Based Systems, 257: 109484, 2022. Search in Google Scholar

A. Yadav. AEFA: Artificial electric field algorithm for global optimization. Swarm and Evolutionary Computation, 48: 93-108, 2019. Search in Google Scholar

H. Su, D. Zhao, A. A. Heidari, L. Liu, X. Zhang, M. Mafarja, and H. Chen. RIME: A physics-based optimization. Neurocomputing, 532: 183-214, 2023. Search in Google Scholar

A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. H. Gandomi. Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems, 191: 105190, 2020. Search in Google Scholar

Z. W. Geem, J. H. Kim, and G. V. Loganathan. A new heuristic optimization algorithm: harmony search. Simulation, 76(2): 60–68, 2001. Search in Google Scholar

A. Kaveh, and S. Talatahari. Stochastic paint optimizer: theory and application in civil engineering. Engineering with Computers, 37: 1–32, 2020. Search in Google Scholar

M. Zaeimi, and A. Ghoddosian. Color harmony algorithm: an art-inspired metaheuristic for mathematical function optimization. Soft Computing, 24: 12027–12066, 2020. Search in Google Scholar

A. H. Gandomi. Interior search algorithm (isa): a novel approach for global optimization. ISA Transactions, 53(4): 1168–1183, 2014. Search in Google Scholar

I. Ahmadianfar, A. A. Heidari, A. H. Gandomi, X. Chu, and H. Chen. INFO: An efficient optimization algorithm based on weighted mean of vectors. Expert Systems with Applications, 195: 116516, 2022. Search in Google Scholar

I. Ahmadianfar, A. A. Heidari, A. H. Gandomi, X. Chu, and H. Chen. RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181: 115079, 2021. Search in Google Scholar

Y. Liu, H. Ding, Z. Wang, G. Jin, B. Li, Z. Yang, and G. Dhiman. A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems. Mathematical Biosciences and Engineering, 20(9): 17242-17271, 2023. Search in Google Scholar

O. E. Turgut, and M. S. Turgut. Diversity enhanced equilibrium optimization algorithm for solving unconstrained and constrained optimization problems. Evolutionary Intelligence, 1-52, 2023. Search in Google Scholar

G. I. Sayed, G. Khoriba, and M. H. Haggag. A novel chaotic equilibrium optimizer algorithm with S-shaped and V-shaped transfer functions for feature selection. Journal of Ambient Intelligence and Humanized Computing, 1-26, 2023. Search in Google Scholar

R. Atha, A. Rajan, and S. Mallick. An enhanced equilibrium optimizer for solving complex optimization problems. Information Sciences, 120077, 2024. Search in Google Scholar

Y. Liu, H. Ding, Z. Wang, G. Dhiman, Z. Yang, and P. Hu. An enhanced equilibrium optimizer for solving optimization tasks. Computers, Materials and Continua, 77(2), 2023. Search in Google Scholar

J. Xia, H. Zhang, R. Li, H. Chen, H. Turabieh, M. Mafarja, and Z. Pan. Generalized oppositional moth flame optimization with crossover strategy: an approach for medical diagnosis. Journal of Bionic Engineering, 18(4): 991-1010, 2021. Search in Google Scholar

M. H. Nadimi-Shahraki, H. Zamani, A. Fatahi, and S. Mirjalili. MFO-SFR: an enhanced moth-flame optimization algorithm using an effective stagnation finding and replacing strategy. Mathematics, 11(4): 862, 2023. Search in Google Scholar

S. K. Sahoo, S. Sharma, and A. K. Saha. A novel variant of moth flame optimizer for higher dimensional optimization problems. Journal of Bionic Engineering, 1-27, 2023. Search in Google Scholar

Z. Wang, H. Ding, J. Yang, P. Hou, G. Dhiman, J. Wang, Z. Yang, and A. Li. Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization. Frontiers in Bioengineering and Biotechnology, 10, 2022. Search in Google Scholar

K. Cpałka, A. Słowik, and K. Łapa. A population-based algorithm with the selection of evaluation precision and size of the population. Applied Soft Computing, 115, 108154, 2022. Search in Google Scholar

R. Salgotra, S. Singh, U. Singh, S. Mirjalili, and A. H. Gandomi. Marine predator-inspired naked mole-rat algorithm for global optimization. Expert Systems with Applications, 212, 118822, 2023. Search in Google Scholar

M. S. Uzer, and O. Inan. Application of improved hybrid whale optimization algorithm to optimization problems. Neural Computing and Applications, 35(17): 12433-12451, 2023. Search in Google Scholar

V. Garg, K. Deep, K. A. Alnowibet, H. M. Zawbaa, and A. W. Mohamed. Biogeography-based optimization with salp swarm optimizer inspired operator for solving non-linear continuous optimization problems. Alexandria Engineering Journal, 73: 321-341, 2023. Search in Google Scholar

C. Zhong, G. Li, and Z. Meng. A hybrid teaching–learning slime mould algorithm for global optimization and reliability-based design optimization problems. Neural Computing and Applications, 34, 19: 16617-16642, 2022. Search in Google Scholar

A. A. Ewees, H. I. Fatma, and T. S. Ahmed. Gradient-based optimizer improved by slime mould algorithm for global optimization and feature selection for diverse computation problems. Expert Systems with Applications, 213: 118872, 2023. Search in Google Scholar

S. Nama, A. K. Saha, S. Chakraborty, A. H. Gandomi, and L. Abualigah. Boosting particle swarm optimization by backtracking search algorithm for optimization problems. Swarm and Evolutionary Computation, 79: 101304, 2023. Search in Google Scholar

B. S. Yıldız, S. Kumar, N. Panagant, P. Mehta, S. M. Sait, A. R. Yildiz, N. Pholdee, S. Bureerat, and S. Mirjalili. A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems. Knowledge-Based Systems, 271: 110554, 2023. Search in Google Scholar

A. Seyyedabbasi. WOASCALF: A new hybrid whale optimization algorithm based on sine cosine algorithm and levy flight to solve global optimization problems. Advances in Engineering Software, 173: 103272, 2022. Search in Google Scholar

L. Abualigah, A. A. Ewees, M. A. Al-Qaness, M. A. Elaziz, D. Yousri, R. A. Ibrahim, and M. Altalhi. Boosting arithmetic optimization algorithm by sine cosine algorithm and levy flight distribution for solving engineering optimization problems. Neural Computing and Applications, 34(11): 8823-8852, 2022. Search in Google Scholar

S. K. Sahoo, and A. K. Saha. A hybrid moth flame optimization algorithm for global optimization. Journal of Bionic Engineering, 19(5): 1522-1543, 2022. Search in Google Scholar

H. R. R. Zaman, and F. S. Gharehchopogh. An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems. Engineering with Computers, 38(4): 2797-2831, 2022. Search in Google Scholar

H. A. Shehadeh. A hybrid sperm swarm optimization and gravitational search algorithm (HSSOGSA) for global optimization. Neural Computing and Applications, 33(18): 11739-11752, 2021. Search in Google Scholar

W. H. Tan, and J. Mohamad-Saleh. A hybrid whale optimization algorithm based on equilibrium concept. Alexandria Engineering Journal 68: 763-786, 2023. Search in Google Scholar

S. Gupta, K. Deep. and S. Mirjalili. An efficient equilibrium optimizer with mutation strategy for numerical optimization, Applied Soft Computing 96, 106542, 2020. Search in Google Scholar

J. Liu, W. Li, Y. Li, Lwmeo: An efficient equilibrium optimizer for complex functions and engineering design problems, Expert Systems with Applications 198, 116828, 2022. Search in Google Scholar

X. Zhang, Q. Lin, Information-utilization strengthened equilibrium optimizer, Artificial Intelligence Review, 1–34, 2022. Search in Google Scholar

E. H. Houssein, B. E.-d. Helmy, D. Oliva, P. Jangir, M. Premkumar, A. A. Elngar, and H. Shaban. An efficient multi-thresholding based covid-19 CT images segmentation approach using an improved equilibrium optimizer, Biomedical Signal Processing and Control, 73, 103401, 2022. Search in Google Scholar

W. Shan, Z. Qiao, A. A. Heidari, H. Chen, H. Turabieh, and Y. Teng. Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis. Knowledge-Based Systems, 214, 106728, 2021. Search in Google Scholar

L. Ma, C. Wang, N. G. Xie, M. Shi, Y. Ye, and L. Wang. Moth-flame optimization algorithm based on diversity and mutation strategy. Applied Intelligence, 51, 5836-5872, 2021. Search in Google Scholar

Z. Wang, and H. Ding. Opposition-based learning equilibrium ptimizer with application in mobile robot path planning. International Journal of Robotics and Automation Technology, 10, 64-74, 2023. Search in Google Scholar

H. Ding, X. Cao, Z. Wang, G. Dhiman, P. Hou, J. Wang, A. Li, and X. Hu. Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies. Mathematical Biosciences and Engineering, 19(8): 7756-7804, 2022. Search in Google Scholar

Z. Wang, H. Ding, J. Wang, P. Hou, A. Li, Z. Yang, and X. Hu. Adaptive guided salp swarm algorithm with velocity clamping mechanism for solving optimization problems. Journal of Computational Design and Engineering, 9(6): 2022: 2196-2234. Search in Google Scholar

Z. Wang, H. Ding, Z. Yang, B. Li, Z. Guan, and L. Bao. Rank-driven salp swarm algorithm with orthogonal opposition-based learning for global optimization. Applied Intelligence, 5: 1-43, 2022. Search in Google Scholar

Z. Wang, H. Ding, J. Yang, J. Wang, B. Li, Z. Yang, and P. Hou. Advanced orthogonal opposition-based learning-driven dynamic salp swarm algorithm: framework and case studies. IET Control Theory and Applications, 16(10): 945-971, 2022. Search in Google Scholar

P. G. Luan, and N. T. Thinh. Hybrid genetic algorithm based smooth global-path planning for a mobile robot. Mechanics Based Design of Structures and Machines, 51(3): 1758-1774, 2023. Search in Google Scholar

P. B. Fernandes, R. C. L. Oliveira, and J. F. Neto. Trajectory planning of autonomous mobile robots applying a particle swarm optimization algorithm with peaks of diversity. Applied Soft Computing 116: 108108, 2022. Search in Google Scholar

D. Agarwal, and P. S. Bharti. Implementing modified swarm intelligence algorithm based on slime moulds for path planning and obstacle avoidance problem in mobile robots. Applied Soft Computing, 107, 107372, 2021. Search in Google Scholar

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