Algorithms

References:

• Yadav, Anupam.

"AEFA: Artificial electric field algorithm for global optimization." Swarm and Evolutionary Computation 48 (2019): 93-108.

AEO(npop, max_iter, lb::Real, ub::Real, dim, objfun)
AEO(npop, max_iter, lb::Vector{Float64}, ub::Vector{Float64}, objfun)
AEO(problem::OptimizationProblem, npop=30, max_iter=1000)

Run the Artificial Ecosystem-based Optimization (AEO) algorithm.

This function supports three ways to define the optimization problem:

1. By passing scalar bounds and problem dimensionality.
2. By passing vector bounds explicitly.
3. By using an OptimizationProblem struct that encapsulates the problem definition.

Arguments

Common

• npop::Integer: Number of individuals in the population.
• max_iter::Integer: Maximum number of iterations.

For scalar bounds:

• lb::Real: Lower bound (same for all dimensions).
• ub::Real: Upper bound (same for all dimensions).
• dim::Integer: Dimensionality of the problem.
• objfun::Function: Objective function to minimize.

For vector bounds:

• lb::Vector{Float64}: Lower bounds for each dimension.
• ub::Vector{Float64}: Upper bounds for each dimension.
• objfun::Function: Objective function to minimize.

For OptimizationProblem form:

• problem::OptimizationProblem: A struct containing objfun, lb, ub, and dim.

Returns

• OptimizationResult: A struct containing:
  • bestX::Vector{Float64}: The best solution found.
  • bestF::Float64: The best objective value.
  • his_best_fit::Vector{Float64}: History of the best fitness value at each iteration.

Examples

```julia

Using scalar bounds and dimension

result = AEO(30, 100, -5.12, 5.12, 10, Ackley)

Using vector bounds

lb = fill(-5.12, 10) ub = fill(5.12, 10) result = AEO(30, 100, lb, ub, 10, Ackley)

Using OptimizationProblem struct

problem = OptimizationProblem(Ackley, -5.12, 5.12, 10) result = AEO(problem, 30, 100)

AFT(noThieves, max_iter, lb, ub, objfun)

Ali Baba and the Forty Thieves (AFT) meta-heuristic optimization algorithm implementation in Julia.

Arguments:

• noThieves: Number of thieves in the algorithm.
• max_iter: Maximum number of iterations.
• lb: Lower bounds for the search space.
• ub: Upper bounds for the search space.
• objfun: Function to evaluate the fitness of solutions.

Returns:

• AFTResult: A struct containing:
  • fitness: The best fitness value found.
  • gbest: The position corresponding to the best fitness.
  • ccurve: A vector of best fitness values at each iteration.

References:

• Braik, Malik, Mohammad Hashem Ryalat, and Hussein Al-Zoubi. "A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves." Neural Computing and Applications 34, no. 1 (2022): 409-455.

AHA(npop, max_iter, lb, ub, objfun)

The Artificial Hummingbird Algorithm (AHA) is a meta-heuristic optimization algorithm inspired by the foraging behavior of hummingbirds. It is designed to solve numerical optimization problems by simulating the way hummingbirds search for food.

Arguments:

• npop: Number of hummingbirds (population size).
• max_iter: Maximum number of iterations.
• lb: Lower bounds for the search space.
• ub: Upper bounds for the search space.
• objfun: Objective function to evaluate the fitness of solutions.

Returns:

• AHAResult: A struct containing:
  • BestF: The best fitness value found.
  • BestX: The position corresponding to the best fitness.
  • HisBestFit: A vector of best fitness values at each iteration.

References:

• Zhao, Weiguo, Liying Wang, and Seyedali Mirjalili. "Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications." Computer Methods in Applied Mechanics and Engineering 388 (2022): 114194.

References:

• Xiao, Y., Cui, H., Khurma, R. A., & Castillo, P. A. (2025). Artificial lemming algorithm: a novel bionic meta-heuristic technique for solving real-world engineering optimization problems. Artificial Intelligence Review, 58(3), 84.

References:

• Mirjalili, Seyedali. "The ant lion optimizer." Advances in engineering software 83 (2015): 80-98.

References:

• Abualigah, Laith, Ali Diabat, Seyedali Mirjalili, Mohamed Abd Elaziz, and Amir H. Gandomi. "The arithmetic optimization algorithm." Computer methods in applied mechanics and engineering 376 (2021): 113609.

References:

• Wang, Xiaopeng, Václav Snášel, Seyedali Mirjalili, Jeng-Shyang Pan, Lingping Kong, and Hisham A. Shehadeh. "Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization." Knowledge-Based Systems 295 (2024): 111737.

References:

• Wang, Liying, Qingjiao Cao, Zhenxing Zhang, Seyedali Mirjalili, and Weiguo Zhao. "Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems." Engineering Applications of Artificial Intelligence 114 (2022): 105082.

References:

• Yuan, Chong, Dong Zhao, Ali Asghar Heidari, Lei Liu, Yi Chen, Zongda Wu, and Huiling Chen. "Artemisinin optimization based on malaria therapy: Algorithm and applications to medical image segmentation." Displays 84 (2024): 102740.

References:

• Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408.

References:

• Das, Amit Kumar, and Dilip Kumar Pratihar. "Bonobo optimizer (BO): an intelligent heuristic with self-adjusting parameters over continuous spaces and its applications to engineering problems." Applied Intelligence 52, no. 3 (2022): 2942-2974.

References:

• Arora, Sankalap, and Satvir Singh. "Butterfly optimization algorithm: a novel approach for global optimization." Soft computing 23 (2019): 715-734.

References:

• Braik, Malik, Alaa Sheta, and Heba Al-Hiary. "A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm." Neural computing and applications 33, no. 7 (2021): 2515-2547.

References:

• Shehadeh, Hisham A. "Chernobyl disaster optimizer (CDO): A novel meta-heuristic method for global optimization." Neural Computing and Applications 35, no. 15 (2023): 10733-10749.

References:

• Braik, Malik Shehadeh. "Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems." Expert Systems with Applications 174 (2021): 114685.

References:

• Khishe, Mohammad, and Mohammad Reza Mosavi. "Chimp optimization algorithm." Expert systems with applications 149 (2020): 113338.

References:

• Akbari, Mohammad Amin, Mohsen Zare, Rasoul Azizipanah-Abarghooee, Seyedali Mirjalili, and Mohamed Deriche. "The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems." Scientific reports 12, no. 1 (2022): 10953.

References:

• Braik, Malik, Alaa Sheta, and Heba Al-Hiary. "A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm." Neural computing and applications 33, no. 7 (2021): 2515-2547.

References:

• Naruei, Iraj, and Farshid Keynia. "A new optimization method based on COOT bird natural life model." Expert Systems with Applications 183 (2021): 115352.

References:

• Ghasemi, Mojtaba, Mohammad-Amin Akbari, Changhyun Jun, Sayed M. Bateni, Mohsen Zare, Amir Zahedi, Hao-Ting Pai, Shahab S. Band, Massoud Moslehpour, and Kwok-Wing Chau. "Circulatory System Based Optimization (CSBO): an expert multilevel biologically inspired meta-heuristic algorithm." Engineering Applications of Computational Fluid Mechanics 16, no. 1 (2022): 1483-1525.

References:

• Xue J, Shen B. Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. The Journal of Supercomputing. 2023 May;79(7):7305-36.

References:

• Ghafil, H. N., & Jármai, K. (2020). Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications. Applied Soft Computing, 93, 106392.

References:

• Agushaka, Jeffrey O., Absalom E. Ezugwu, and Laith Abualigah. "Dwarf mongoose optimization algorithm." Computer methods in applied mechanics and engineering 391 (2022): 114570.

References:

• Zhao, Shijie, Tianran Zhang, Shilin Ma, and Miao Chen. "Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications." Engineering Applications of Artificial Intelligence 114 (2022): 105075.

DSO(npop, max_iter, lb, ub, dim, objfun)

The Deep Sleep Optimiser (DSO) is a human-inspired metaheuristic optimization algorithm that mimics the sleep patterns of humans to find optimal solutions in various optimization problems.

Arguments:

• npop: Number of search agents (population size).
• max_iter: Number of iterations (generations).
• lb: Lower bounds of the search space.
• ub: Upper bounds of the search space.
• objfun: Objective function to be minimized.

Returns:

• ::DSOResult: A struct containing:
  • Best_Cost: The best cost found during the optimization.
  • Best_Position: The position corresponding to the best cost.
  • Best_iteration: A vector of best costs at each iteration.

References:

• Oladejo, Sunday O., Stephen O. Ekwe, Lateef A. Akinyemi, and Seyedali A. Mirjalili, The deep sleep optimiser: A human-based metaheuristic approach., IEEE Access (2023).

References:

• Lian, Junbo, Ting Zhu, Ling Ma, Xincan Wu, Ali Asghar Heidari, Yi Chen, Huiling Chen, and Guohua Hui.

"The educational competition optimizer." International Journal of Systems Science 55, no. 15 (2024): 3185-3222.

References:

• Abdel-Basset, Mohamed, Doaa El-Shahat, Mohammed Jameel, and Mohamed Abouhawwash.

"Exponential distribution optimizer (EDO): a novel math-inspired algorithm for global optimization and engineering problems." Artificial Intelligence Review 56, no. 9 (2023): 9329-9400.

References:

• Al-Betar, M.A., Awadallah, M.A., Braik, M.S. et al.

Elk herd optimizer: a novel nature-inspired metaheuristic algorithm. Artif Intell Rev 57, 48 (2024). https://doi.org/10.1007/s10462-023-10680-4

References:

• Faramarzi, Afshin, Mohammad Heidarinejad, Brent Stephens, and Seyedali Mirjalili.

"Equilibrium optimizer: A novel optimization algorithm." Knowledge-based systems 191 (2020): 105190.

References:

• Ouyang, K., Fu, S., Chen, Y., Cai, Q., Heidari, A. A., & Chen, H. (2024).

Escape: an optimization method based on crowd evacuation behaviors. Artificial Intelligence Review, 58(1), 19.

References:

• Luan, Tran Minh, Samir Khatir, Minh Thi Tran, Bernard De Baets, and Thanh Cuong-Le.

"Exponential-trigonometric optimization algorithm for solving complicated engineering problems." Computer Methods in Applied Mechanics and Engineering 432 (2024): 117411.

References:

• Qi, Ailiang, Dong Zhao, Ali Asghar Heidari, Lei Liu, Yi Chen, and Huiling Chen.

"FATA: an efficient optimization method based on geophysics." Neurocomputing 607 (2024): 128289.

References:

• Hashim, Fatma A., Reham R. Mostafa, Abdelazim G. Hussien, Seyedali Mirjalili, and Karam M. Sallam.

"Fick's Law Algorithm: A physical law-based algorithm for numerical optimization." Knowledge-Based Systems 260 (2023): 110146.

References:

• Ghasemi, M., Golalipour, K., Zare, M., Mirjalili, S., Trojovský, P., Abualigah, L. and Hemmati, R., 2024.

Flood algorithm (FLA): an efficient inspired meta-heuristic for engineering optimization. The Journal of Supercomputing, 80(15), pp.22913-23017.

References:

• Mohammed, Hardi, and Tarik Rashid.

"FOX: a FOX-inspired optimization algorithm." Applied Intelligence 53, no. 1 (2023): 1030-1050.

References:

• Agushaka, Jeffrey O., Absalom E. Ezugwu, and Laith Abualigah.

"Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer." Neural Computing and Applications 35, no. 5 (2023): 4099-4131.

References:

• Ahmadianfar, Iman, Omid Bozorg-Haddad, and Xuefeng Chu.

"Gradient-based optimizer: A new metaheuristic optimization algorithm." Information Sciences 540 (2020): 131-159.

References:

• Ghasemi, Mojtaba, Mohsen Zare, Amir Zahedi, Mohammad-Amin Akbari, Seyedali Mirjalili, and Laith Abualigah.

"Geyser inspired algorithm: a new geological-inspired meta-heuristic for real-parameter and constrained engineering optimization." Journal of Bionic Engineering 21, no. 1 (2024): 374-408.

References:

• El-Kenawy, El-Sayed M., Nima Khodadadi, Seyedali Mirjalili, Abdelaziz A. Abdelhamid, Marwa M. Eid, and Abdelhameed Ibrahim.

"Greylag goose optimization: nature-inspired optimization algorithm." Expert Systems with Applications 238 (2024): 122147.

References:

• Chopra, Nitish, and Muhammad Mohsin Ansari.

"Golden jackal optimization: A novel nature-inspired optimizer for engineering applications." Expert Systems with Applications 198 (2022): 116924.

References:

• Hu, Gang, Yuxuan Guo, Guo Wei, and Laith Abualigah.

"Genghis Khan shark optimizer: a novel nature-inspired algorithm for engineering optimization." Advanced Engineering Informatics 58 (2023): 102210.

References:

• Zhang, Yiying, Zhigang Jin, and Seyedali Mirjalili.

"Generalized normal distribution optimization and its applications in parameter extraction of photovoltaic models." Energy Conversion and Management 224 (2020): 113301.

References:

• Zhang, Qingke, Hao Gao, Zhi-Hui Zhan, Junqing Li, and Huaxiang Zhang.

"Growth Optimizer: A powerful metaheuristic algorithm for solving continuous and discrete global optimization problems." Knowledge-Based Systems 261 (2023): 110206.

References:

• Saremi, Shahrzad, Seyedali Mirjalili, and Andrew Lewis.

"Grasshopper optimisation algorithm: theory and application." Advances in engineering software 105 (2017): 30-47.

References:

• Abdollahzadeh, Benyamin, Farhad Soleimanian Gharehchopogh, and Seyedali Mirjalili.

"Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems." International Journal of Intelligent Systems 36, no. 10 (2021): 5887-5958.

References:

• Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis.

"Grey wolf optimizer." Advances in engineering software 69 (2014): 46-61.

References:

• Hashim, Fatma A., Essam H. Houssein, Kashif Hussain, Mai S. Mabrouk, and Walid Al-Atabany.

"Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems." Mathematics and Computers in Simulation 192 (2022): 84-110.

References:

• Askari, Qamar, Mehreen Saeed, and Irfan Younas.

"Heap-based optimizer inspired by corporate rank hierarchy for global optimization." Expert Systems with Applications 161 (2020): 113702.

References:

• Lian, Junbo, and Guohua Hui.

"Human evolutionary optimization algorithm." Expert Systems with Applications 241 (2024): 122638.

References:

• Yang, Yutao, Huiling Chen, Ali Asghar Heidari, and Amir H. Gandomi.

"Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts." Expert Systems with Applications 177 (2021): 114864.

References:

• Hashim, F.A., Houssein, E.H., Mabrouk, M.S., Al-Atabany, W. and Mirjalili, S., 2019.

Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, pp.646-667.

References:

• Heidari, Ali Asghar, Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, and Huiling Chen.

"Harris hawks optimization: Algorithm and applications." Future generation computer systems 97 (2019): 849-872.

References:

• Oladejo, Sunday O., Stephen O. Ekwe, and Seyedali Mirjalili.

"The Hiking Optimization Algorithm: A novel human-based metaheuristic approach." Knowledge-Based Systems 296 (2024): 111880.

References:

• Amiri, M.H., Mehrabi Hashjin, N., Montazeri, M., Mirjalili, S. and Khodadadi, N., 2024.

Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Scientific Reports, 14(1), p.5032.

References:

• MiarNaeimi, Farid, Gholamreza Azizyan, and Mohsen Rashki.

"Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems." Knowledge-Based Systems 213 (2021): 106711.

References:

• Ahmadianfar, Iman, Ali Asghar Heidari, Saeed Noshadian, Huiling Chen, and Amir H. Gandomi.

"INFO: An efficient optimization algorithm based on weighted mean of vectors." Expert Systems with Applications 195 (2022): 116516.

References:

• Ghasemi, Mojtaba, Mohsen Zare, Pavel Trojovský, Ravipudi Venkata Rao, Eva Trojovská, and Venkatachalam Kandasamy.

"Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm." Knowledge-Based Systems 295 (2024): 111850.

References:

• Rao, R.

"Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems." International Journal of Industrial Engineering Computations 7, no. 1 (2016): 19-34.

References:

• Chou, Jui-Sheng, and Dinh-Nhat Truong.

"A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean." Applied Mathematics and Computation 389 (2021): 125535.

References:

• Houssein, Essam H., Diego Oliva, Nagwan Abdel Samee, Noha F. Mahmoud, and Marwa M. Emam.

"Liver Cancer Algorithm: A novel bio-inspired optimizer." Computers in Biology and Medicine 165 (2023): 107389.

References:

• Houssein, Essam H., Mohammed R. Saad, Fatma A. Hashim, Hassan Shaban, and M. Hassaballah.

"Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems." Engineering Applications of Artificial Intelligence 94 (2020): 103731.

References:

• Ghasemi, Mojtaba, Mohsen Zare, Amir Zahedi, Pavel Trojovský, Laith Abualigah, and Eva Trojovská.

"Optimization based on performance of lungs in body: Lungs performance-based optimization (LPO)." Computer Methods in Applied Mechanics and Engineering 419 (2024): 116582.

References:

• Zheng, Boli, Yi Chen, Chaofan Wang, Ali Asghar Heidari, Lei Liu, and Huiling Chen.

"The moss growth optimization (MGO): concepts and performance." Journal of Computational Design and Engineering 11, no. 5 (2024): 184-221.

References:

• Abdollahzadeh, Benyamin, Farhad Soleimanian Gharehchopogh, Nima Khodadadi, and Seyedali Mirjalili.

"Mountain gazelle optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems." Advances in Engineering Software 174 (2022): 103282.

References:

• Faramarzi, Afshin, Mohammad Heidarinejad, Seyedali Mirjalili, and Amir H. Gandomi.

"Marine Predators Algorithm: A nature-inspired metaheuristic." Expert systems with applications 152 (2020): 113377.

References:

• Zhao, Weiguo, Zhenxing Zhang, and Liying Wang.

"Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications." Engineering Applications of Artificial Intelligence 87 (2020): 103300.

References:

• Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Abdolreza Hatamlou.

"Multi-verse optimizer: a nature-inspired algorithm for global optimization." Neural Computing and Applications 27 (2016): 495-513.

References:

• Dehghani, Mohammad, and Pavel Trojovský.

"Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems." Frontiers in Mechanical Engineering 8 (2023): 1126450.

References:

• Lian, Junbo, Guohua Hui, Ling Ma, Ting Zhu, Xincan Wu, Ali Asghar Heidari, Yi Chen, and Huiling Chen.

"Parrot optimizer: Algorithm and applications to medical problems." Computers in Biology and Medicine 172 (2024): 108064.

References:

• Ezugwu, Absalom E., Jeffrey O. Agushaka, Laith Abualigah, Seyedali Mirjalili, and Amir H. Gandomi.

"Prairie dog optimization algorithm." Neural Computing and Applications 34, no. 22 (2022): 20017-20065.

References:

• Bouaouda, Anas, Fatma A. Hashim, Yassine Sayouti, and Abdelazim G. Hussien.

"Pied kingfisher optimizer: a new bio-inspired algorithm for solving numerical optimization and industrial engineering problems." Neural Computing and Applications (2024): 1-59.

References:

• Yuan, Chong, Dong Zhao, Ali Asghar Heidari, Lei Liu, Yi Chen, and Huiling Chen.

"Polar lights optimizer: Algorithm and applications in image segmentation and feature selection." Neurocomputing 607 (2024): 128427.

References:

• Trojovský, Pavel, and Mohammad Dehghani.

"Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications." Sensors 22, no. 3 (2022): 855.

References:

• Askari, Qamar, Irfan Younas, and Mehreen Saeed.

"Political Optimizer: A novel socio-inspired meta-heuristic for global optimization." Knowledge-based systems 195 (2020): 105709.

References:

• Moosavi, Seyyed Hamid Samareh, and Vahid Khatibi Bardsiri.

"Poor and rich optimization algorithm: A new human-based and multi populations algorithm." Engineering applications of artificial intelligence 86 (2019): 165-181.

References:

• Abdollahzadeh, Benyamin, Nima Khodadadi, Saeid Barshandeh, Pavel Trojovský, Farhad Soleimanian Gharehchopogh, El-Sayed M. El-kenawy, Laith Abualigah, and Seyedali Mirjalili.

"Puma optimizer (PO): A novel metaheuristic optimization algorithm and its application in machine learning." Cluster Computing (2024): 1-49.

References:

• Zhao, Weiguo, Liying Wang, Zhenxing Zhang, Seyedali Mirjalili, Nima Khodadadi, and Qiang Ge.

"Quadratic Interpolation Optimization (QIO): A new optimization algorithm based on generalized quadratic interpolation and its applications to real-world engineering problems." Computer Methods in Applied Mechanics and Engineering 417 (2023): 116446.

References:

• Fu, Shengwei, et al.

"Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2D/3D UAV path planning and engineering design problems." Artificial Intelligence Review 57.6 (2024): 134.

References:

• Braik, M., & Al-Hiary, H. (2025).

Rüppell’s fox optimizer: A novel meta-heuristic approach for solving global optimization problems. Cluster Computing, 28(5), 1-77.

References:

• Su, Hang, Dong Zhao, Ali Asghar Heidari, Lei Liu, Xiaoqin Zhang, Majdi Mafarja, and Huiling Chen.

"RIME: A physics-based optimization." Neurocomputing 532 (2023): 183-214.

References:

• Jia, Heming, Xiaoxu Peng, and Chunbo Lang.

"Remora optimization algorithm." Expert Systems with Applications 185 (2021): 115665.

References:

• Abualigah, Laith, Mohamed Abd Elaziz, Putra Sumari, Zong Woo Geem, and Amir H. Gandomi.

"Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer." Expert Systems with Applications 191 (2022): 116158.

References:

• Dhiman, Gaurav, Meenakshi Garg, Atulya Nagar, Vijay Kumar, and Mohammad Dehghani.

"A novel algorithm for global optimization: rat swarm optimizer." Journal of Ambient Intelligence and Humanized Computing 12 (2021): 8457-8482.

References:

• Ahmadianfar, Iman, Ali Asghar Heidari, Amir H. Gandomi, Xuefeng Chu, and Huiling Chen.

"RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method." Expert Systems with Applications 181 (2021): 115079.

References:

• Moosavi, Seyyed Hamid Samareh, and Vahid Khatibi Bardsiri.

"Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation." Engineering Applications of Artificial Intelligence 60 (2017): 1-15.

References:

• Fu, Youfa, Dan Liu, Jiadui Chen, and Ling He.

"Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems." Artificial Intelligence Review 57, no. 5 (2024): 1-102.

References:

• Mirjalili, Seyedali.

"SCA: a sine cosine algorithm for solving optimization problems." Knowledge-based systems 96 (2016): 120-133.

References:

• Bai, Jianfu, Yifei Li, Mingpo Zheng, Samir Khatir, Brahim Benaissa, Laith Abualigah, and Magd Abdel Wahab.

"A sinh cosh optimizer." Knowledge-Based Systems 282 (2023): 111081.

References:

• Özbay, Feyza Altunbey.

"A modified seahorse optimization algorithm based on chaotic maps for solving global optimization and engineering problems." Engineering Science and Technology, an International Journal 41 (2023): 101408.

References:

• Zhong, C., Li, G., Meng, Z., Li, H., Yildiz, A. R., & Mirjalili, S. (2025).

Starfish optimization algorithm (SFOA): a bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers. Neural Computing and Applications, 37(5), 3641-3683.

References:

• Dhiman, Gaurav, and Vijay Kumar.

"Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications." Advances in Engineering Software 114 (2017): 48-70.

References:

• Li, Shimin, Huiling Chen, Mingjing Wang, Ali Asghar Heidari, and Seyedali Mirjalili.

"Slime mould algorithm: A new method for stochastic optimization." Future generation computer systems 111 (2020): 300-323.

References:

• Deng, Lingyun, and Sanyang Liu.

"Snow ablation optimizer: A novel metaheuristic technique for numerical optimization and engineering design." Expert Systems with Applications 225 (2023): 120069.

References:

• Hashim, Fatma A., and Abdelazim G. Hussien.

"Snake Optimizer: A novel meta-heuristic optimization algorithm." Knowledge-Based Systems 242 (2022): 108320.

References:

• Dhiman, Gaurav, and Vijay Kumar.

"Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems." Knowledge-based systems 165 (2019): 169-196.

References:

• Jiankai Xue & Bo Shen

A novel swarm intelligence optimization approach: sparrow search algorithm Systems Science & Control Engineering, 8:1 (2020), 22-34. DOI: 10.1080/21642583.2019.1708830

References:

• Mirjalili, Seyedali, Amir H. Gandomi, Seyedeh Zahra Mirjalili, Shahrzad Saremi, Hossam Faris, and Seyed Mohammad Mirjalili.

"Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems." Advances in engineering software 114 (2017): 163-191.

References:

• Dhiman, Gaurav, and Amandeep Kaur.

"STOA: a bio-inspired based optimization algorithm for industrial engineering problems." Engineering Applications of Artificial Intelligence 82 (2019): 148-174.

References:

• Jia, Heming, et al.

"Superb Fairy-wren Optimization Algorithm: a novel metaheuristic algorithm for solving feature selection problems." Cluster Computing 28.4 (2025): 246.

References:

• Zhao, Weiguo, Liying Wang, and Zhenxing Zhang.

"Supply-demand-based optimization: A novel economics-inspired algorithm for global optimization." Ieee Access 7 (2019): 73182-73206.

References:

• Rao, R. Venkata, Vimal J. Savsani, and Dipakkumar P. Vakharia.

"Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems." Computer-aided design 43, no. 3 (2011): 303-315.

References:

• Minh, Hoang-Le, Thanh Sang-To, Guy Theraulaz, Magd Abdel Wahab, and Thanh Cuong-Le.

"Termite life cycle optimizer." Expert Systems with Applications 213 (2023): 119211.

References:

• Braik, M., Al-Hiary, H., Alzoubi, H., Hammouri, A., Azmi Al-Betar, M., & Awadallah, M. A. (2025).

Tornado optimizer with Coriolis force: a novel bio-inspired meta-heuristic algorithm for solving engineering problems. Artificial Intelligence Review, 58(4), 1-99.

References:

• Kaur, Satnam, Lalit K. Awasthi, Amrit Lal Sangal, and Gaurav Dhiman.

"Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization." Engineering Applications of Artificial Intelligence 90 (2020): 103541.

References:

• Zhao, Shijie, Tianran Zhang, Liang Cai, and Ronghua Yang.

"Triangulation topology aggregation optimizer: A novel mathematics-based meta-heuristic algorithm for continuous optimization and engineering applications." Expert Systems with Applications 238 (2024): 121744.

References:

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