Improved Tuna Swarm Optimization Algorithm for Engineering Design Problems

Abstract

Tuna Swarm Optimization (TSO) which is developed by being inspired by the hunting strategies of the tuna fish is a metaheuristic optimization algorithm (MHA). TSO is able to solve some optimization problems successfully. However, TSO has the handicap of having premature convergence and being caught by local minimum trap. This study proposes a mathematical model aiming to eliminate these disadvantages and to increase the performance of TSO. The basic philosophy of the proposed method is not to focus on the best solution but on the best ones. The Proposed algorithm has been compared to six current and popular MHAs in the literature. Using classical test functions to have a preliminary evaluation is a frequently preferred method in the field of optimization. Therefore, first, all the algorithms were applied to ten classical test functions and the results were interpreted through the Wilcoxon statistical test. The results indicate that the proposed algorithm is successful. Following that, all the algorithms were applied to three engineering design problems, which is the main purpose of this article. The original TSO has a weak performance on design problems. With optimal costs like 1.74 in welded beam design problem, 1581.47 in speed reducer design problem, and 38.455 in I-beam design problem, the proposed algorithm has been the most successful one. Such a case leads us to the idea that the proposed method of this article is successful for improving the performance of TSO.

Keywords

• Tuna Swarm Optimization
• Swarm-Based Metaheuristic Algorithm
• Engineering Design Problems

Mühendislik Tasarım Problemleri için Geliştirilmiş Tuna Sürü Optimizasyonu

Öz

Tuna swarm optimization (TSO) Tuna balıklarının avlanma stratejilerinden esinlenilerek geliştirilmiş bir meta-heuristic optimizasyon algoritmasıdır (MHA). TSO, bazı optimizasyon problemini başarıyla çözebilmektedir. Ancak TSO, premature convergence ve local minimum trap yakalanma gibi problemlere sahiptir. Bu çalışmada, TSO’nun bu dezavantajlarını gidermek ve performansını artırmak için geliştirilmiş bir matematiksel model önerilmektedir. Önerilen yöntemin temel felsefesi en iyi çözüme değil en iyi çözümlere odaklanmaktır. Proposed algorithm, literatürdeki güncel ve popüler 6 adet MHA ile karşılaştırılmıştır. Optimizasyon alanında klasik test fonksiyonlarının bir ön değerlendirme yapmak için kullanılması sıklıkla tercih edilen bir yöntemdir. Bu nedenle tüm algoritmalar ilk önce 10 adet klasik test fonksiyonlarına uygulanmış ve sonuçlar wilcoxen istatistik testi ile yorumlanmıştır. Elde edilen sonuçlar önerilen algoritmanın başarılı olduğunu göstermektedir. Daha sonra tüm algoritmalar, bu makalenin asıl amacı olan 3 adet mühendislik tasarım problemine uygulanmıştır. Orijinal TSO, tasarım problemlerinde kötü bir performansa sahiptir. Buna karşın proposed algorithm oldukça rekabetçi sonuçlar elde etmektedir. Bu durum, TSO’nun performansını artırmak için bu makalede önerilen yöntemin başarılı olduğunu ortaya koymaktadır.

Anahtar Kelimeler

• Tuna sürü optimizasyonu
• Sürü tabanlı meta sezgisel algoritmalar
• Mühendislik tasarım problemleri

References

1. Algorithm via Levy Flight for Optimization and Data Clustering Problems. IEEE Access 7, 142085-142096, 2019.
2. Abualigah L., Diabat A., Advances in Sine Cosine Algorithm: A comprehensive survey. Artificial Intelligence Review 54(4), 2567-2608, 2021.
3. Abualigah L., Diabat A., Geem Z. W., A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications. Applied Sciences 10(11), 3827, 2020.
4. Ahmadianfar I., Bozorg-Haddad O., Chu X., Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences 540, 131-159, 2020.
5. Amine K., Multiobjective Simulated Annealing: Principles and Algorithm Variants. Advances in Operations Research 2019, e8134674, 2019.
6. Ashraf H., Elkholy M. M., Abdellatif S. O., El‑Fergany A. A., Synergy of neuro-fuzzy controller and tuna swarm algorithm for maximizing the overall efficiency of PEM fuel cells stack including dynamic performance. Energy Conversion and Management:X 16, 100301, 2022.
7. Askari Q., Saeed M., Younas I., Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Systems with Applications 161, 113702, 2020.
8. Askari Q., Younas I., Saeed M., Political Optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowledge-Based Systems 195, 105709, 2020.

9. Deng W., Shang S., Cai X., Zhao H., Song Y., Xu J., An improved differential evolution algorithm and its application in optimization problem. Soft Computing 25(7), 5277-5298, 2021.
10. Feng Y., Deb S., Wang G.-G., Alavi A. H., Monarch butterfly optimization: A comprehensive review. Expert Systems with Applications 168, 114418, 2021.
11. Fu C., Zhang L., A novel method based on tuna swarm algorithm under complex partial shading conditions in PV system. Solar Energy 248, 28-40, 2022.
12. Gad A. G., Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Archives of Computational Methods in Engineering 29(5), 2531-2561, 2022.
13. Gandomi A. H., Yang X.-S., Alavi A. H., Talatahari S., Bat algorithm for constrained optimization tasks. Neural Computing and Applications 22(6), 1239-1255, 2013.
14. Gul F., Rahiman W., Alhady S. S. N., Ali A., Mir I., Jalil A., Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO–GWO optimization algorithm with evolutionary programming. Journal of Ambient Intelligence and Humanized Computing 12(7), 7873-7890, 2021.
15. Guo S.-M., Guo J.-K., Gao Y.-G., Guo P.-Y., Fu-Jun a H., Wang S.-C., Lou Z.-C., Zhang X., Research on Engine Speed Control Based on Tuna Swarm Optimization. Journal of Engineering Research and Reports 23(12), 272-280, 2022.
16. Hansen N., Müller S. D., Koumoutsakos P., Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES). Evolutionary Computation 11(1), 1-18, 2003.
17. Hashim F. A., Houssein E. H., Hussain K., Mabrouk M. S., Al-Atabany W., Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation 192, 84-110, 2022.
18. Jafari A., Khalili T., Babaei E., Bidram A., A Hybrid Optimization Technique Using Exchange Market and Genetic Algorithms. IEEE Access 8, 2417-2427, 2020.
19. Korashy A., Kamel S., Youssef A.-R., Jurado F., Modified water cycle algorithm for optimal direction overcurrent relays coordination. Applied Soft Computing 74, 10-25, 2019.
20. Kumar A., Pant S., Ram M., System Reliability Optimization Using Gray Wolf Optimizer Algorithm. Quality and Reliability Engineering International 33(7), 1327-1335, 2017.
21. Kumar C., Magdalin Mary D., A novel chaotic-driven Tuna Swarm Optimizer with Newton-Raphson method for parameter identification of three-diode equivalent circuit model of solar photovoltaic cells/modules. Optik 264, 169379, 2022.
22. Kumar M., Kulkarni A. J., Satapathy S. C., Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology. Future Generation Computer Systems 81, 252-272, 2018.
23. Kumar S., Yildiz B. S., Mehta P., Panagant N., Sait S. M., Mirjalili S., Yildiz A. R., Chaotic marine predators algorithm for global optimization of real-world engineering problems. Knowledge-Based Systems 261, 110192, 2023.
24. Kumar V., Kumar D., A Systematic Review on Firefly Algorithm: Past, Present, and Future. Archives of Computational Methods in Engineering 28(4), 3269-3291, 2021.
25. Li S., Gong W., Yan X., Hu C., Bai D., Wang L., Gao L., Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization. Energy Conversion and Management 186, 293-305, 2019.
26. Mareli M., Twala B., An adaptive Cuckoo search algorithm for optimisation. Applied Computing and Informatics 14(2), 107-115, 2018.
27. Mbuli N., Ngaha W. S., A survey of big bang big crunch optimisation in power systems. Renewable and Sustainable Energy Reviews 155, 111848, 2022.
28. Mirjalili S., SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based Systems 96, 120-133, 2016.
29. Mirjalili S., Evolutionary Algorithms and Neural Networks, Springer International Publishing, First Edition, United States, pp. 43-55, 2019.
30. Mirjalili S., Mirjalili S. M., Lewis A., Grey Wolf Optimizer. Advances in Engineering Software 69, 46-61, 2014.
31. Noureddine S., An optimization approach for the satisfiability problem. Applied Computing and Informatics 11(1), 47-59, 2015.
32. Öztürk Ş., Ahmad R., Akhtar N., Variants of Artificial Bee Colony algorithm and its applications in medical image processing. Applied Soft Computing 97, 106799, 2020.
33. Prajapati V. K., Jain M., Chouhan L., Tabu Search Algorithm (TSA): A Comprehensive Survey, 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), Jaipur/India, February 7-8, 2020, pp: 1-8.
34. Raja B. D., Patel V. K., Yildiz A. R., Kotecha P., Performance of scientific law-inspired optimization algorithms for constrained engineering applications. Engineering Optimization 55(10), 1798-1812, 2023.
35. Rajabioun R., Cuckoo Optimization Algorithm. Applied Soft Computing 11(8), 5508-5518, 2011.
36. Ramachandran M., Mirjalili S., Nazari-Heris M., Parvathysankar D. S., Sundaram A., Charles Gnanakkan C. A. R., A hybrid Grasshopper Optimization Algorithm and Harris Hawks Optimizer for Combined Heat and Power Economic Dispatch problem. Engineering Applications of Artificial Intelligence 111, 104753, 2022.
37. Rashedi E., Nezamabadi-pour, H., Saryazdi S., GSA: A Gravitational Search Algorithm. Information Sciences 179(13), 2232-2248, 2009.
38. Rosso M. M., Cucuzza R., Aloisio A., Marano G. C., Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator. Applied Sciences 12(5), 2285, 2022.
39. Tan M., Li Y., Ding D., Zhou R., Huang C., An Improved JADE Hybridizing with Tuna Swarm Optimization for Numerical Optimization Problems. Mathematical Problems in Engineering 2022, e7726548, 2022.
40. Tuerxun W., Xu C., Guo H., Guo L., Zeng N., Cheng Z., An ultra-short-term wind speed prediction model using LSTM based on modified tuna swarm optimization and successive variational mode decomposition. Energy Science & Engineering 10(8), 3001-3022, 2022.
41. Wang G.-G., Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing 10(2), 151-164, 2018.
42. Wang G.-G., Deb S., Coelho L. D. S., Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. International Journal of Bio-Inspired Computation 12(1), 1-22, 2018.
43. Wang G.-G., Deb S., Cui Z., Monarch butterfly optimization. Neural Computing and Applications 31(7), 1995-2014, 2019.
44. Wang J., Zhu L., Wu B., Ryspayev A., Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization. Forests 13(11), 1746, 2022.
45. Wang W., Tian J., An Improved Nonlinear Tuna Swarm Optimization Algorithm Based on Circle Chaos Map and Levy Flight Operator. Electronics 11(22), 3678, 2022.
46. Wang Y., Wang P., Zhang J., Cui Z., Cai X., Zhang W., Chen J., A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization. Mathematics 7(2), 135, 2019.
47. Wei Z., Huang C., Wang X., Han T., Li Y., Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization. IEEE Access 7, 66084-66109, 2019.
48. Wolpert D. H., Macready W. G., No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67-82, 1997.
49. Wu L., Huang X., Cui J., Liu C., Xiao W., Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot. Expert Systems with Applications 215, 119410, 2023.
50. Xie L., Han T., Zhou H., Zhang Z.-R., Han B., Tang A., Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization. Computational Intelligence and Neuroscience 2021, e9210050, 2021.
51. Xue Y., Zhang Q., Zhao Y., An improved brain storm optimization algorithm with new solution generation strategies for classification. Engineering Applications of Artificial Intelligence 110, 104677, 2022.
52. Yan Z., Yan J., Wu Y., Cai S., Wang H., A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning. Mathematics and Computers in Simulation 209, 55-86 2023.
53. Zhang F., Mei Y., Nguyen S., Zhang M., Tan K. C., Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling. IEEE Transactions on Evolutionary Computation 25(4), 651-665, 2021.
54. Zhang Y., Jin Z., Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems. Expert Systems with Applications 148, 113246, 2020.