Bio-Inspired Optimization Algorithms: History
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The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. 

  • bio-inspired computation
  • multiparameter optimization
  • metaheuristic algorithms
  • genetic algorithms
  • artificial intelligence
  • deep learning

1. Introduction

Nowadays, we are witnessing an enormous popularity and a literal avalanche of bio-inspired algorithms [1] permeating practically all facets of life. Procedures using artificial intelligence (AI) [2] are being built into a vast number of different systems that include Internet search engines [3], cloud computing systems [4], Internet of Things [5], autonomous (self-driving) vehicles [6], AI chips in flagship smartphones [7], expert medical systems [8], robots [9], agriculture [10], architectural designs [11] and data mining [12], to quote just a tiny fragment. AI can chat with humans and even solve problems stated in the common human language [13], generate paintings and other artworks at a textual prompt [14], create music [15], translate between different languages [16], play very complex games and win them [17], etc. AI artworks have been winning art competitions (and creating controversies at that) [15]. Questions are even posed as to whether AI can show its own creativity comparable to that of humans [18]. Many AI functionalities are met in ordinary life, and we may not even recognize them. All of the mentioned applications and many more are exponentially multiplying, becoming more powerful and more spectacular. The possibilities, at least currently, appear endless. Concerns have been raised for possible dangers for humanity as a whole with using AI, and some legislations have already brought laws limiting the allowed performances and uses of artificial intelligence [19].
Not all results in the field of biomimetic computing are so spectacularly in the spotlight and followed by hype as those that mimic human behavior or even our creativity. However, maybe the most important achievements are hidden among the results that do not belong to this group. They include handling big data, performing time analysis or performing multi-criteria optimization. Such intelligent algorithms that are mostly “invisible” to the eyes of the general public are causing a silent revolution not only in engineering, physics, chemistry, medicine, healthcare and life sciences, but also in economics, finance, business, cybersecurity, language processing and many more fields.
Bio-inspired optimization algorithms are extremely versatile and convenient for complex optimization problems. The result of such wide applicability is their overwhelming presence in diverse fields—there are practically no areas of human interest where they do not appear. As an illustration of their ubiquity, this section mentions just some selected fields where their applications have been reported. They encompass various branches of engineering, including mechanical engineering (automotive [20][21], aerospace [22], fluid dynamics [23], thermal engineering [24], automation [25], robotics [26], mechatronics [27], MEMS [28][29], etc.), electrical engineering [30] (including power engineering [31], electronics [32], microelectronics [33] and nanoelectronics [33], control engineering [34], renewable energy [35], biomedical engineering [36], telecommunications [36], signal processing [37]), geometrical optics [38], photonics [39], nanophotonics and nanoplasmonics [40], image processing [41] including pattern recognition [42], computing [30], [43], networking (computer networks [44] including Internet and Intranet [45], social networks [46], networks on a chip [47], optical networks [48], cellular (mobile) networks [49], wireless sensor networks [50], Internet of things [51], etc.), data clustering and mining [52], civil engineering [53][54], architectural design [55], urban engineering [56], smart cities [57], traffic control and engineering [58], biomedicine and healthcare [59][60], pharmacy [61][62], bioinformatics [63], genomics [64], computational biology [60], environmental pollution control [65] and computational chemistry [66]. Other optimization fields where biomimetic algorithms find application include transportation and logistics [67], industrial production [68], manufacturing including production planning, supply chains, resource allocation and management [69], food production and processing [70], agriculture [71], financial markets [72] including stock market prediction [73], as well as cryptocurrencies and blockchain technology [74], and even such seemingly unlikely fields as language processing and sentiment analysis [75]. The cited applications are just a tip of an iceberg, and there is a vast number of other uses not even mentioned here.

2. A Possible Taxonomy of Bio-Inspired Algorithms

This section presents one possible hierarchical classification of bio-inspired algorithms. The consideration has been made without taking into account any specific targeted applications of the algorithms. Generally, taxonomies of bio-inspired algorithms are relatively rarely considered in the literature. The majority of papers simply skip the topic altogether or handle it casually, presenting only the methods that are of immediate interest to the subject of the paper or, even more often, giving only a partial and non-systematic picture and denoting it as a classification. This is not to say that exhaustive and systematic papers on the subject do not exist. However, it appears that no consensus has been reached about the taxonomy of at least some bio-inspired algorithms yet.
A problem when attempting to define a categorization in this field is that some approaches, although having different names, actually present algorithms very similar or even basically identical to those previously published. Often they offer only incremental advances, such as somewhat better results at benchmarks of precision or computing speed. This is a very slippery ground, however, since according to the previously mentioned No Free Lunch Theorem [76], no algorithm is convenient for all purposes, and while one of them may offer a fast and accurate solution to one class of optimization problems, there is no guarantee that it will not perform drastically worse with other problems, become stuck in a local optimum, never even reaching a global optimum, or even fail completely to give a meaningful solution. For this reason, it is very difficult to decide which procedures merit inclusion in the classification and which do not.
A number of benchmarks have been proposed to compare different optimization procedures, and the most recent publications in the field use them to prove the qualities and advantages of their proposals over the competing ones. A systematic review of methods to compare the performance of different algorithms has been published by Beiranvand, Hare and Lucet [77]. A more recent consideration of that kind dedicated to metaheuristics has been presented by Halim, Ismail and Das [78], who offered an exhaustive and systematic review of measures for determining the efficiency and the effectiveness of optimization algorithms. A benchmarking process for five global approaches for nanooptics optimization has been described by Scheider et al. [79].
One can find various taxonomy proposals in the literature, each with its own merits and disadvantages. Figure 1 represents the scheme of a possible classification of bio-inspired optimization methods.
Figure 1. Possible classification of bio-inspired optimization methods.

3. Heuristics

Heuristics can be briefly described as problem solving through approximate algorithms. The word stems from the Ancient Greek εὑρίσκω (meaning “to discover”). It includes approaches that do not mandatorily result in an optimum solution and are actually imperfect, yet are adequate for attaining a “workable” solution, i.e., a sufficiently good one that will probably be useful and accurate enough for a majority of cases. On the other hand, they may not work in certain cases, or may consistently introduce systematic errors in others. The methods used include pragmatic trade-offs, rules of thumb (use of approximations based on prior knowledge in similar situations), a trial and error approach, the process of elimination, guesswork (“educated guesses”) and acceptable/satisfactory approximations. The main benefit is that heuristic approaches usually have vastly lower computational cost, and their main deficiencies are that they are usually dependent on a particular problem (i.e., not generally applicable in all situations) and their accuracy may be quite low in certain cases, while inherently they do not offer a way to estimate that accuracy.
Heuristic approaches include common heuristic algorithms, metaheuristic algorithms and hyper-heuristic algorithms. All of these approaches are considered to represent the foundations of AI.

“Basic” Heuristic Algorithms

The heuristic algorithms represent the oldest approximate approach to optimization problems, from which metaheuristics and hyper-heuristics evolved. They include a number of approximate goal attainment methods. While there is no universally accepted taxonomy of common heuristic algorithms, a possible classification is presented in Table 1. Metaheuristic and hyper-heuristic algorithms are not included in this subsection, since these are covered separately in the next two sections. This is a short overview only, presented for the sake of generality, since the quoted algorithms are mostly unrelated to bio-inspired methods. The comprehensiveness of the table is not claimed, and some quoted methods may overlap more or less, thus appearing in multiple categories at the same time.
Table 1. Selected heuristic algorithms, excluding metaheuristics or hyper-heuristics.

This entry is adapted from the peer-reviewed paper 10.3390/biomimetics8030278


  1. Alanis, A.Y.; Arana-Daniel, N.; López-Franco, C. Bio-inspired Algorithms. In Bio-Inspired Algorithms for Engineering; Alanis, A.Y., Arana-Daniel, N., López-Franco, C., Eds.; Butterworth-Heinemann: Oxford, UK, 2018; pp. 1–14.
  2. Zhang, B.; Zhu, J.; Su, H. Toward the third generation artificial intelligence. Sci. China Inf. Sci. 2023, 66, 121101.
  3. Stokel-Walker, C. Can we trust AI search engines? New Sci. 2023, 258, 12.
  4. Gill, S.S.; Xu, M.; Ottaviani, C.; Patros, P.; Bahsoon, R.; Shaghaghi, A.; Golec, M.; Stankovski, V.; Wu, H.; Abraham, A. AI for next generation computing: Emerging trends and future directions. Internet Things 2022, 19, 100514.
  5. Stadnicka, D.; Sęp, J.; Amadio, R.; Mazzei, D.; Tyrovolas, M.; Stylios, C.; Carreras-Coch, A.; Merino, J.A.; Żabiński, T.; Navarro, J. Industrial Needs in the Fields of Artificial Intelligence, Internet of Things and Edge Computing. Sensors 2022, 22, 4501.
  6. Sujitha, S.; Pyari, S.; Jhansipriya, W.Y.; Reddy, Y.R.; Kumar, R.V.; Nandan, P.R. Artificial Intelligence based Self-Driving Car using Robotic Model. In Proceedings of the 2023 Third International Conference on Artificial Intelligence Smart Energy (ICAIS), Coimbatore, India, 2–4 February 2023; pp. 1634–1638.
  7. Park, H.; Kim, S. Overviewing AI-Dedicated Hardware for On-Device AI in Smartphones. In Artificial Intelligence and Hardware Accelerators; Mishra, A., Cha, J., Park, H., Kim, S., Eds.; Springer International Publishing: Cham, Switzerland, 2023; pp. 127–150.
  8. Apell, P.; Eriksson, H. Artificial intelligence (AI) healthcare technology innovations: The current state and challenges from a life science industry perspective. Technol. Anal. Strateg. Manag. 2023, 35, 179–193.
  9. Yan, L.; Grossman, G.M. Robots and AI: A New Economic Era; Taylor & Francis: Boca Raton, FL, USA, 2023.
  10. Wakchaure, M.; Patle, B.K.; Mahindrakar, A.K. Application of AI techniques and robotics in agriculture: A review. Artif. Intell. Life Sci. 2023, 3, 100057.
  11. Pan, Y.; Zhang, L. Integrating BIM and AI for Smart Construction Management: Current Status and Future Directions. Arch. Comput. Methods Eng. 2023, 30, 1081–1110.
  12. Baburaj, E. Comparative analysis of bio-inspired optimization algorithms in neural network-based data mining classification. Int. J. Swarm Intell. Res. (IJSIR) 2022, 13, 25.
  13. Taecharungroj, V. “What can ChatGPT do?” analyzing early reactions to the innovative AI chatbot on twitter. Big Data Cogn. Comput. 2023, 7, 35.
  14. Zhao, B.; Zhan, D.; Zhang, C.; Su, M. Computer-aided digital media art creation based on artificial intelligence. Neural Comput. Appl. 2023.
  15. Adam, D. The muse in the machine. Proc. Natl. Acad. Sci. USA 2023, 120, e2306000120.
  16. Kenny, D. Machine Translation for Everyone: Empowering Users in the Age of Artificial Intelligence; Language Science Press: Berlin, Germany, 2022.
  17. Hassabis, D. Artificial Intelligence: Chess match of the century. Nature 2017, 544, 413–414.
  18. Kirkpatrick, K. Can AI Demonstrate Creativity? Commun. ACM 2023, 66, 21–23.
  19. Chamberlain, J. The Risk-Based Approach of the European Union’s Proposed Artificial Intelligence Regulation: Some Comments from a Tort Law Perspective. Eur. J. Risk Regul. 2022, 14, 1–13.
  20. Rahul, M.; Jayaprakash, J. Mathematical model automotive part shape optimization using metaheuristic method-review. Mater. Today Proc. 2021, 47, 100–103.
  21. McLean, S.D.; Juul Hansen, E.A.; Pop, P.; Craciunas, S.S. Configuring ADAS Platforms for Automotive Applications Using Metaheuristics. Front. Robot. AI 2022, 8, 762227.
  22. Champasak, P.; Panagant, N.; Pholdee, N.; Vio, G.A.; Bureerat, S.; Yildiz, B.S.; Yıldız, A.R. Aircraft conceptual design using metaheuristic-based reliability optimisation. Aerosp. Sci. Technol. 2022, 129, 107803.
  23. Calicchia, M.A.; Atefi, E.; Leylegian, J.C. Creation of small kinetic models for CFD applications: A meta-heuristic approach. Eng. Comput. 2022, 38, 1923–1937.
  24. Menéndez-Pérez, A.; Fernández-Aballí Altamirano, C.; Sacasas Suárez, D.; Cuevas Barraza, C.; Borrajo-Pérez, R. Metaheuristics applied to the optimization of a compact heat exchanger with enhanced heat transfer surface. Appl. Therm. Eng. 2022, 214, 118887.
  25. Minzu, V.; Serbencu, A. Systematic Procedure for Optimal Controller Implementation Using Metaheuristic Algorithms. Intell. Autom. Soft Comput. 2020, 26, 663–677.
  26. Castillo, O.; Melin, P. A Review of Fuzzy Metaheuristics for Optimal Design of Fuzzy Controllers in Mobile Robotics. In Complex Systems: Spanning Control and Computational Cybernetics: Applications: Dedicated to Professor Georgi M. Dimirovski on His Anniversary; Shi, P., Stefanovski, J., Kacprzyk, J., Eds.; Springer International Publishing: Cham, Switzerland, 2022; pp. 59–72.
  27. Guo, K. Special Issue on Application of Artificial Intelligence in Mechatronics. Appl. Sci. 2023, 13, 158.
  28. Lu, S.; Li, S.; Habibi, M.; Safarpour, H. Improving the thermo-electro-mechanical responses of MEMS resonant accelerometers via a novel multi-layer perceptron neural network. Measurement 2023, 218, 113168.
  29. Pertin, O.; Guha, K.; Jakšić, O.; Jakšić, Z.; Iannacci, J. Investigation of Nonlinear Piezoelectric Energy Harvester for Low-Frequency and Wideband Applications. Micromachines 2022, 13, 1399.
  30. Razmjooy, N.; Ashourian, M.; Foroozandeh, Z. (Eds.) Metaheuristics and Optimization in Computer and Electrical Engineering; Springer Nature Switzerland AG: Cham, Switzerland, 2021.
  31. Pijarski, P.; Kacejko, P.; Miller, P. Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue. Energies 2023, 16, 2804.
  32. Joseph, S.B.; Dada, E.G.; Abidemi, A.; Oyewola, D.O.; Khammas, B.M. Metaheuristic algorithms for PID controller parameters tuning: Review, approaches and open problems. Heliyon 2022, 8, e09399.
  33. Valencia-Ponce, M.A.; González-Zapata, A.M.; de la Fraga, L.G.; Sanchez-Lopez, C.; Tlelo-Cuautle, E. Integrated Circuit Design of Fractional-Order Chaotic Systems Optimized by Metaheuristics. Electronics 2023, 12, 413.
  34. Roni, M.H.K.; Rana, M.S.; Pota, H.R.; Hasan, M.M.; Hussain, M.S. Recent trends in bio-inspired meta-heuristic optimization techniques in control applications for electrical systems: A review. Int. J. Dyn. Control 2022, 10, 999–1011.
  35. Amini, E.; Nasiri, M.; Pargoo, N.S.; Mozhgani, Z.; Golbaz, D.; Baniesmaeil, M.; Nezhad, M.M.; Neshat, M.; Astiaso Garcia, D.; Sylaios, G. Design optimization of ocean renewable energy converter using a combined Bi-level metaheuristic approach. Energy Convers. Manag. X 2023, 19, 100371.
  36. Qaisar, S.M.; Khan, S.I.; Dallet, D.; Tadeusiewicz, R.; Pławiak, P. Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare. Biocybern. Biomed. Eng. 2022, 42, 681–694.
  37. Rasheed, I.M.; Motlak, H.J. Performance parameters optimization of CMOS analog signal processing circuits based on smart algorithms. Bull. Electr. Eng. Inform. 2023, 12, 149–157.
  38. de Souza Batista, L.; de Carvalho, L.M. Optimization deployed to lens design. In Advances in Ophthalmic Optics Technology; Monteiro, D.W.d.L., Trindade, B.L.C., Eds.; IOP Publishing: Bristol, UK, 2022; pp. 9-1–9-29.
  39. Chen, X.; Lin, D.; Zhang, T.; Zhao, Y.; Liu, H.; Cui, Y.; Hou, C.; He, J.; Liang, S. Grating waveguides by machine learning for augmented reality. Appl. Opt. 2023, 62, 2924–2935.
  40. Edee, K. Augmented Harris Hawks Optimizer with Gradient-Based-Like Optimization: Inverse Design of All-Dielectric Meta-Gratings. Biomimetics 2023, 8, 179.
  41. Vineeth, P.; Suresh, S. Performance evaluation and analysis of population-based metaheuristics for denoising of biomedical images. Res. Biomed. Eng. 2021, 37, 111–133.
  42. Nssibi, M.; Manita, G.; Korbaa, O. Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey. Comput. Sci. Rev. 2023, 49, 100559.
  43. AlShathri, S.I.; Chelloug, S.A.; Hassan, D.S.M. Parallel Meta-Heuristics for Solving Dynamic Offloading in Fog Computing. Mathematics 2022, 10, 1258.
  44. Ghanbarzadeh, R.; Hosseinalipour, A.; Ghaffari, A. A novel network intrusion detection method based on metaheuristic optimisation algorithms. J. Ambient. Intell. Humaniz. Comput. 2023, 14, 7575–7592.
  45. Darwish, S.M.; Farhan, D.A.; Elzoghabi, A.A. Building an Effective Classifier for Phishing Web Pages Detection: A Quantum-Inspired Biomimetic Paradigm Suitable for Big Data Analytics of Cyber Attacks. Biomimetics 2023, 8, 197.
  46. Razaghi, B.; Roayaei, M.; Charkari, N.M. On the Group-Fairness-Aware Influence Maximization in Social Networks. IEEE Trans. Comput. Soc. Syst. 2022, 1–9.
  47. Gomes de Araujo Rocha, H.M.; Schneider Beck, A.C.; Eduardo Kreutz, M.; Diniz Monteiro Maia, S.M.; Magalhães Pereira, M. Using evolutionary metaheuristics to solve the mapping and routing problem in networks on chip. Des. Autom. Embed. Syst. 2023.
  48. Fan, Z.; Lin, J.; Dai, J.; Zhang, T.; Xu, K. Photonic Hopfield neural network for the Ising problem. Opt. Express 2023, 31, 21340–21350.
  49. Aldalbahi, A.; Siasi, N.; Mazin, A.; Jasim, M.A. Digital compass for multi-user beam access in mmWave cellular networks. Digit. Commun. Netw. 2022.
  50. Mohan, P.; Subramani, N.; Alotaibi, Y.; Alghamdi, S.; Khalaf, O.I.; Ulaganathan, S. Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks. Sensors 2022, 22, 1618.
  51. Bichara, R.M.; Asadallah, F.A.B.; Awad, M.; Costantine, J. Quantum Genetic Algorithm for the Design of Miniaturized and Reconfigurable IoT Antennas. IEEE Trans. Antenn. Propag. 2023, 71, 3894–3904.
  52. Gharehchopogh, F.S.; Abdollahzadeh, B.; Khodadadi, N.; Mirjalili, S. Metaheuristics for clustering problems. In Comprehensive Metaheuristics; Mirjalili, S., Gandomi, A.H., Eds.; Academic Press: Cambridge, MA, USA, 2023; pp. 379–392.
  53. Kashani, A.R.; Camp, C.V.; Rostamian, M.; Azizi, K.; Gandomi, A.H. Population-based optimization in structural engineering: A review. Artif. Intell. Rev. 2022, 55, 345–452.
  54. Sadrossadat, E.; Basarir, H.; Karrech, A.; Elchalakani, M. Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics. Eng. Comput. 2022, 38, 2569–2582.
  55. Aslay, S.E.; Dede, T. Reduce the construction cost of a 7-story RC public building with metaheuristic algorithms. Archit. Eng. Des. Manag. 2023, 1–16.
  56. Smetankina, N.; Semenets, O.; Merkulova, A.; Merkulov, D.; Misura, S. Two-Stage Optimization of Laminated Composite Elements with Minimal Mass. In Smart Technologies in Urban Engineering; Arsenyeva, O., Romanova, T., Sukhonos, M., Tsegelnyk, Y., Eds.; Springer International Publishing: Cham, Switzerland, 2023; pp. 456–465.
  57. Jiang, Y.; Li, H.; Feng, B.; Wu, Z.; Zhao, S.; Wang, Z. Street Patrol Routing Optimization in Smart City Management Based on Genetic Algorithm: A Case in Zhengzhou, China. ISPRS Int. J. Geo-Inf. 2022, 11, 171.
  58. Jovanović, A.; Stevanović, A.; Dobrota, N.; Teodorović, D. Ecology based network traffic control: A bee colony optimization approach. Eng. Appl. Artif. Intell. 2022, 115, 105262.
  59. Kaur, M.; Singh, D.; Kumar, V.; Lee, H.N. MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis. IEEE J. Biomed. Health Inform. 2022, 1–11.
  60. Aziz, R.M. Cuckoo Search-Based Optimization for Cancer Classification: A New Hybrid Approach. J. Comput. Biol. 2022, 29, 565–584.
  61. Kılıç, F.; Uncu, N. Modified swarm intelligence algorithms for the pharmacy duty scheduling problem. Expert Syst. Appl. 2022, 202, 117246.
  62. Luukkonen, S.; van den Maagdenberg, H.W.; Emmerich, M.T.M.; van Westen, G.J.P. Artificial intelligence in multi-objective drug design. Curr. Opin. Struct. Biol. 2023, 79, 102537.
  63. Amorim, A.R.; Zafalon, G.F.D.; Contessoto, A.d.G.; Valêncio, C.R.; Sato, L.M. Metaheuristics for multiple sequence alignment: A systematic review. Comput. Biol. Chem. 2021, 94, 107563.
  64. Jain, S.; Bharti, K.K. Genome sequence assembly using metaheuristics. In Comprehensive Metaheuristics; Mirjalili, S., Gandomi, A.H., Eds.; Academic Press: Cambridge, MA, USA, 2023; pp. 347–358.
  65. Neelakandan, S.; Prakash, M.; Geetha, B.T.; Nanda, A.K.; Metwally, A.M.; Santhamoorthy, M.; Gupta, M.S. Metaheuristics with Deep Transfer Learning Enabled Detection and classification model for industrial waste management. Chemosphere 2022, 308, 136046.
  66. Alshehri, A.S.; You, F. Deep learning to catalyze inverse molecular design. Chem. Eng. J. 2022, 444, 136669.
  67. Juan, A.A.; Keenan, P.; Martí, R.; McGarraghy, S.; Panadero, J.; Carroll, P.; Oliva, D. A review of the role of heuristics in stochastic optimisation: From metaheuristics to learnheuristics. Ann. Oper. Res. 2023, 320, 831–861.
  68. Dhouib, S.; Zouari, A. Adaptive iterated stochastic metaheuristic to optimize holes drilling path in manufacturing industry: The Adaptive-Dhouib-Matrix-3 (A-DM3). Eng. Appl. Artif. Intell. 2023, 120, 105898.
  69. Para, J.; Del Ser, J.; Nebro, A.J. Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives. Appl. Sci. 2022, 12, 1491.
  70. Sarkar, T.; Salauddin, M.; Mukherjee, A.; Shariati, M.A.; Rebezov, M.; Tretyak, L.; Pateiro, M.; Lorenzo, J.M. Application of bio-inspired optimization algorithms in food processing. Curr. Res. Food Sci. 2022, 5, 432–450.
  71. Khan, A.A.; Shaikh, Z.A.; Belinskaja, L.; Baitenova, L.; Vlasova, Y.; Gerzelieva, Z.; Laghari, A.A.; Abro, A.A.; Barykin, S. A Blockchain and Metaheuristic-Enabled Distributed Architecture for Smart Agricultural Analysis and Ledger Preservation Solution: A Collaborative Approach. Appl. Sci. 2022, 12, 1487.
  72. Mousapour Mamoudan, M.; Ostadi, A.; Pourkhodabakhsh, N.; Fathollahi-Fard, A.M.; Soleimani, F. Hybrid neural network-based metaheuristics for prediction of financial markets: A case study on global gold market. J. Comput. Des. Eng. 2023, 10, 1110–1125.
  73. Houssein, E.H.; Dirar, M.; Hussain, K.; Mohamed, W.M. Artificial Neural Networks for Stock Market Prediction: A Comprehensive Review. In Metaheuristics in Machine Learning: Theory and Applications; Oliva, D., Houssein, E.H., Hinojosa, S., Eds.; Springer International Publishing: Cham, Switzerland, 2021; pp. 409–444.
  74. Quek, S.G.; Selvachandran, G.; Tan, J.H.; Thiang, H.Y.A.; Tuan, N.T.; Son, L.H. A New Hybrid Model of Fuzzy Time Series and Genetic Algorithm Based Machine Learning Algorithm: A Case Study of Forecasting Prices of Nine Types of Major Cryptocurrencies. Big Data Res. 2022, 28, 100315.
  75. Hosseinalipour, A.; Ghanbarzadeh, R. A novel metaheuristic optimisation approach for text sentiment analysis. Int. J. Mach. Learn. Cybern. 2023, 14, 889–909.
  76. Wolpert, D.H.; Macready, W.G. No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1997, 1, 67–82.
  77. Beiranvand, V.; Hare, W.; Lucet, Y. Best practices for comparing optimization algorithms. Optim. Eng. 2017, 18, 815–848.
  78. Halim, A.H.; Ismail, I.; Das, S. Performance assessment of the metaheuristic optimization algorithms: An exhaustive review. Artif. Intell. Rev. 2021, 54, 2323–2409.
  79. Schneider, P.-I.; Garcia Santiago, X.; Soltwisch, V.; Hammerschmidt, M.; Burger, S.; Rockstuhl, C. Benchmarking Five Global Optimization Approaches for Nano-optical Shape Optimization and Parameter Reconstruction. ACS Photonics 2019, 6, 2726–2733.
  80. Smith, D.R. Top-down synthesis of divide-and-conquer algorithms. Artif. Intell. 1985, 27, 43–96.
  81. Jacobson, S.H.; Yücesan, E. Analyzing the Performance of Generalized Hill Climbing Algorithms. J. Heuristics 2004, 10, 387–405.
  82. Boettcher, S. Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems. Nat. Mach. Intell. 2023, 5, 24–25.
  83. Cheriyan, J.; Cummings, R.; Dippel, J.; Zhu, J. An improved approximation algorithm for the matching augmentation problem. SIAM J. Discret. Math. 2023, 37, 163–190.
  84. Gao, J.; Tao, X.; Cai, S. Towards more efficient local search algorithms for constrained clustering. Inf. Sci. 2023, 621, 287–307.
  85. Bahadori-Chinibelagh, S.; Fathollahi-Fard, A.M.; Hajiaghaei-Keshteli, M. Two Constructive Algorithms to Address a Multi-Depot Home Healthcare Routing Problem. IETE J. Res. 2022, 68, 1108–1114.
  86. Nadel, B.A. Constraint satisfaction algorithms. Comput. Intell. 1989, 5, 188–224.
  87. Narendra, P.M.; Fukunaga, K. A Branch and Bound Algorithm for Feature Subset Selection. IEEE Trans. Comput. 1977, 26, 917–922.
  88. Basu, A.; Conforti, M.; Di Summa, M.; Jiang, H. Complexity of branch-and-bound and cutting planes in mixed-integer optimization. Math. Program. 2023, 198, 787–810.
  89. Dutt, S.; Deng, W. Cluster-aware iterative improvement techniques for partitioning large VLSI circuits. ACM Trans. Des. Autom. Electron. Syst. 2002, 7, 91–121.
  90. Vasant, P.; Weber, G.-W.; Dieu, V.N. (Eds.) Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics; IGI Global: Hershey, PA, USA, 2016.
  91. Fávero, L.P.; Belfiore, P. Data Science for Business and Decision Making; Academic Press: Cambridge, MA, USA, 2018.
  92. Montoya, O.D.; Molina-Cabrera, A.; Gil-González, W. A Possible Classification for Metaheuristic Optimization Algorithms in Engineering and Science. Ingeniería 2022, 27, 1.
  93. Ma, Z.; Wu, G.; Suganthan, P.N.; Song, A.; Luo, Q. Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms. Swarm Evol. Comput. 2023, 77, 101248.
  94. Del Ser, J.; Osaba, E.; Molina, D.; Yang, X.-S.; Salcedo-Sanz, S.; Camacho, D.; Das, S.; Suganthan, P.N.; Coello Coello, C.A.; Herrera, F. Bio-inspired computation: Where we stand and what’s next. Swarm Evol. Comput. 2019, 48, 220–250.
  95. Molina, D.; Poyatos, J.; Ser, J.D.; García, S.; Hussain, A.; Herrera, F. Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cogn. Comput. 2020, 12, 897–939.
  96. Holland, J.H. Adaptation in Natural and Artificial Systems; University of Michigan Press: Ann Arbor, MI, USA, 1975.
  97. Wilson, A.J.; Pallavi, D.R.; Ramachandran, M.; Chinnasamy, S.; Sowmiya, S. A review on memetic algorithms and its developments. Electr. Autom. Eng. 2022, 1, 7–12.
  98. Bilal; Pant, M.; Zaheer, H.; Garcia-Hernandez, L.; Abraham, A. Differential Evolution: A review of more than two decades of research. Eng. Appl. Artif. Intell. 2020, 90, 103479.
  99. Sengupta, S.; Basak, S.; Peters, R.A. Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives. Mach. Learn. Knowl. Extr. 2019, 1, 157–191.
  100. Mirjalili, S.; Lewis, A. The Whale Optimization Algorithm. Adv. Eng. Softw. 2016, 95, 51–67.
  101. Mirjalili, S.; Mirjalili, S.M.; Lewis, A. Grey Wolf Optimizer. Adv. Eng. Softw. 2014, 69, 46–61.
  102. Karaboga, D.; Gorkemli, B.; Ozturk, C.; Karaboga, N. A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 2014, 42, 21–57.
  103. Dorigo, M.; Stützle, T. Ant Colony Optimization: Overview and Recent Advances. In Handbook of Metaheuristics; Gendreau, M., Potvin, J.-Y., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 311–351.
  104. Neshat, M.; Sepidnam, G.; Sargolzaei, M.; Toosi, A.N. Artificial fish swarm algorithm: A survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif. Intell. Rev. 2014, 42, 965–997.
  105. Fister, I.; Fister, I.; Yang, X.-S.; Brest, J. A comprehensive review of firefly algorithms. Swarm Evol. Comput. 2013, 13, 34–46.
  106. Ranjan, R.K.; Kumar, V. A systematic review on fruit fly optimization algorithm and its applications. Artif. Intell. Rev. 2023.
  107. Yang, X.-S.; Deb, S. Cuckoo search: Recent advances and applications. Neural Comput. Appl. 2014, 24, 169–174.
  108. Agarwal, T.; Kumar, V. A Systematic Review on Bat Algorithm: Theoretical Foundation, Variants, and Applications. Arch. Comput. Methods Eng. 2022, 29, 2707–2736.
  109. Selva Rani, B.; Aswani Kumar, C. A Comprehensive Review on Bacteria Foraging Optimization Technique. In Multi-objective Swarm Intelligence: Theoretical Advances and Applications; Dehuri, S., Jagadev, A.K., Panda, M., Eds.; Springer: Berlin/Heidelberg, Germany, 2015; pp. 1–25.
  110. Luque-Chang, A.; Cuevas, E.; Fausto, F.; Zaldívar, D.; Pérez, M. Social Spider Optimization Algorithm: Modifications, Applications, and Perspectives. Math. Probl. Eng. 2018, 2018, 6843923.
  111. Cuevas, E.; Fausto, F.; González, A. Locust Search Algorithm Applied to Multi-threshold Segmentation. In New Advancements in Swarm Algorithms: Operators and Applications; Cuevas, E., Fausto, F., González, A., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 211–240.
  112. Ezugwu, A.E.; Prayogo, D. Symbiotic organisms search algorithm: Theory, recent advances and applications. Expert Syst. Appl. 2019, 119, 184–209.
  113. Shehab, M.; Abualigah, L.; Al Hamad, H.; Alabool, H.; Alshinwan, M.; Khasawneh, A.M. Moth–flame optimization algorithm: Variants and applications. Neural Comput. Appl. 2020, 32, 9859–9884.
  114. Hashim, F.A.; Houssein, E.H.; Hussain, K.; Mabrouk, M.S.; Al-Atabany, W. Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Math. Comput. Simul. 2022, 192, 84–110.
  115. Li, J.; Lei, H.; Alavi, A.H.; Wang, G.-G. Elephant Herding Optimization: Variants, Hybrids, and Applications. Mathematics 2020, 8, 1415.
  116. Abualigah, L.; Diabat, A. A comprehensive survey of the Grasshopper optimization algorithm: Results, variants, and applications. Neural Comput. Appl. 2020, 32, 15533–15556.
  117. Alabool, H.M.; Alarabiat, D.; Abualigah, L.; Heidari, A.A. Harris hawks optimization: A comprehensive review of recent variants and applications. Neural Comput. Appl. 2021, 33, 8939–8980.
  118. Jiang, Y.; Wu, Q.; Zhu, S.; Zhang, L. Orca predation algorithm: A novel bio-inspired algorithm for global optimization problems. Expert Syst. Appl. 2022, 188, 116026.
  119. Zamani, H.; Nadimi-Shahraki, M.H.; Gandomi, A.H. Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization. Comput. Methods Appl. Mech. Eng. 2022, 392, 114616.
  120. Dehghani, M.; Trojovský, P. Serval Optimization Algorithm: A New Bio-Inspired Approach for Solving Optimization Problems. Biomimetics 2022, 7, 204.
  121. Salcedo-Sanz, S. A review on the coral reefs optimization algorithm: New development lines and current applications. Prog. Artif. Intell. 2017, 6, 1–15.
  122. Wang, G.-G.; Gandomi, A.H.; Alavi, A.H.; Gong, D. A comprehensive review of krill herd algorithm: Variants, hybrids and applications. Artif. Intell. Rev. 2019, 51, 119–148.
  123. Agushaka, J.O.; Ezugwu, A.E.; Abualigah, L. Gazelle optimization algorithm: A novel nature-inspired metaheuristic optimizer. Neural Comput. Appl. 2023, 35, 4099–4131.
  124. Dasgupta, D.; Yu, S.; Nino, F. Recent Advances in Artificial Immune Systems: Models and Applications. Appl. Soft Comput. 2011, 11, 1574–1587.
  125. Sadollah, A.; Sayyaadi, H.; Yadav, A. A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm. Appl. Soft Comput. 2018, 71, 747–782.
  126. Mousavirad, S.J.; Ebrahimpour-Komleh, H. Human mental search: A new population-based metaheuristic optimization algorithm. Appl. Intell. 2017, 47, 850–887.
  127. Xing, B.; Gao, W.-J. Imperialist Competitive Algorithm. In Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms; Xing, B., Gao, W.-J., Eds.; Springer International Publishing: Cham, Switzerland, 2014; pp. 203–209.
  128. Bozorgi, A.; Bozorg-Haddad, O.; Chu, X. Anarchic Society Optimization (ASO) Algorithm. In Advanced Optimization by Nature-Inspired Algorithms; Bozorg-Haddad, O., Ed.; Springer: Singapore, 2018; pp. 31–38.
  129. Abdel-Basset, M.; Mohamed, R.; Chakrabortty, R.K.; Sallam, K.; Ryan, M.J. An efficient teaching-learning-based optimization algorithm for parameters identification of photovoltaic models: Analysis and validations. Energy Convers. Manag. 2021, 227, 113614.
  130. Ray, T.; Liew, K.M. Society and civilization: An optimization algorithm based on the simulation of social behavior. IEEE Trans. Evol. Comput. 2003, 7, 386–396.
  131. Husseinzadeh Kashan, A. League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships. Appl. Soft Comput. 2014, 16, 171–200.
  132. Moghdani, R.; Salimifard, K. Volleyball Premier League Algorithm. Appl. Soft Comput. 2018, 64, 161–185.
  133. Biyanto, T.R.; Fibrianto, H.Y.; Nugroho, G.; Hatta, A.M.; Listijorini, E.; Budiati, T.; Huda, H. Duelist algorithm: An algorithm inspired by how duelist improve their capabilities in a duel. In Advances in Swarm Intelligence; Tan, Y., Shi, Y., Niu, B., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 39–47.
  134. Laguna, M. Tabu Search. In Handbook of Heuristics; Martí, R., Pardalos, P.M., Resende, M.G.C., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 741–758.
  135. Ghasemian, H.; Ghasemian, F.; Vahdat-Nejad, H. Human urbanization algorithm: A novel metaheuristic approach. Math. Comput. Simul. 2020, 178, 1–15.
  136. Askari, Q.; Younas, I.; Saeed, M. Political Optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowl. Based Syst. 2020, 195, 105709.
  137. Abdel-Basset, M.; Shawky, L.A. Flower pollination algorithm: A comprehensive review. Artif. Intell. Rev. 2019, 52, 2533–2557
  138. Ibrahim, A.; Anayi, F.; Packianather, M.; Alomari, O.A. New hybrid invasive weed optimization and machine learning approach for fault detection. Energies 2022, 15, 1488.
  139. Waqar, A.; Subramaniam, U.; Farzana, K.; Elavarasan, R.M.; Habib, H.U.R.; Zahid, M.; Hossain, E. Analysis of Optimal Deployment of Several DGs in Distribution Networks Using Plant Propagation Algorithm. IEEE Access 2020, 8, 175546–175562.
  140. Gupta, D.; Sharma, P.; Choudhary, K.; Gupta, K.; Chawla, R.; Khanna, A.; Albuquerque, V.H.C.D. Artificial plant optimization algorithm to detect infected leaves using machine learning. Expert Syst. 2021, 38, e12501.
  141. Cinar, A.C.; Korkmaz, S.; Kiran, M.S. A discrete tree-seed algorithm for solving symmetric traveling salesman problem. Eng. Sci. Technol. Int. J. 2020, 23, 879–890.
  142. Premaratne, U.; Samarabandu, J.; Sidhu, T. A new biologically inspired optimization algorithm. In Proceedings of the 2009 International Conference on Industrial and Information Systems (ICIIS), Peradeniya, Sri Lanka, 28–31 December 2009; pp. 279–284.
  143. Burke, E.K.; Gendreau, M.; Hyde, M.; Kendall, G.; Ochoa, G.; Özcan, E.; Qu, R. Hyper-heuristics: A survey of the state of the art. J. Oper. Res. Soc. 2013, 64, 1695–1724.
  144. Cowling, P.; Kendall, G.; Soubeiga, E. A hyperheuristic approach to scheduling a sales summit. In Practice and Theory of Automated Timetabling III, Proceedings of the Third International Conference, PATAT 2000, Konstanz, Germany, 16–18 August 2000; Selected Papers; Burke, E., Erben, W., Eds.; Springer: Berlin/Heidelberg, Germany, 2001; pp. 176–190.
  145. Moerland, T.M.; Broekens, J.; Plaat, A.; Jonker, C.M. Model-based Reinforcement Learning: A Survey. Found. Trends® Mach. Learn. 2023, 16, 1–118.
  146. Mazyavkina, N.; Sviridov, S.; Ivanov, S.; Burnaev, E. Reinforcement learning for combinatorial optimization: A survey. Comput. Oper. Res. 2021, 134, 105400.
  147. Raidl, G.R.; Puchinger, J.; Blum, C. Metaheuristic Hybrids. In Handbook of Metaheuristics; Gendreau, M., Potvin, J.-Y., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 385–417.
  148. Li, Y.; Jia, M.; Han, X.; Bai, X.-S. Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA). Energy 2021, 225, 120331.
  149. Bas, E.; Egrioglu, E.; Kolemen, E. Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization. Granul. Comput. 2022, 7, 411–420.
  150. Xue, Y.; Tong, Y.; Neri, F. An ensemble of differential evolution and Adam for training feed-forward neural networks. Inf. Sci. 2022, 608, 453–471.
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