PERFORMANCE EVALUATIONS OF THE MANTA RAY FORAGING OPTIMIZATION ALGORITHM IN REAL-WORLD CONSTRAINED OPTIMIZATION PROBLEMS

Abstract

Metaheuristic algorithms are often preferred for solving constrained engineering design optimization problems. The most important reason for choosing these algorithms is that they guarantee a satisfactory response within a reasonable time. The swarm intelligence-based manta ray foraging optimization algorithm (MRFO) is a metaheuristic algorithm proposed to solve engineering applications. In this study, the performance of MRFO is evaluated on 19 mechanical engineering optimization problems in the CEC2020 real-world constrained optimization problem suite. In order to increase the MRFO performance, three modifications are made to the algorithm; in this way, the enhanced manta ray foraging optimization (EMRFO) algorithm is proposed. The effects of the modifications made are analyzed and interpreted separately. Its performance has been compared with the algorithms in the literature, and it has been shown that EMRFO is a successful and preferable algorithm for this problem suite.

Keywords

• Constrained optimization problems
• Manta ray foraging optimization algorithm
• Real-world optimization problems

References

1. [1] Ezugwu AE, Shukla AK, Nath R, Akinyelu AA, Agushaka JO, Chiroma H, Muhuri PK. Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artificial Intelligence Review 2021; 54(6): p. 4237-4316.
2. [2] Pan JS, Zhang LG, Wang RB, Snášel V, Chu SC. Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems. Mathematics and Computers in Simulation 2022; 202: p. 343-373.
3. [3] Tang KS, Man KF, Kwong S, He Q. Genetic algorithms and their applications. IEEE signal processing magazine 1996; 13(6): p. 22-37.
4. [4] Price KV. Differential evolution in Handbook of optimization: From classical to modern approach. Springer, p. 187-214, 2013.
5. [5] Simon D. Biogeography-based optimization. IEEE transactions on evolutionary computation 2008; 12(6): p. 702-713.
6. [6] Rashedi E, Nezamabadi-Pour H,Saryazdi S. GSA: a gravitational search algorithm. Information sciences 2009; 179(13): p. 2232-2248.
7. [7] Alatas B. ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Systems with Applications 2011; 38(10): p. 13170-13180.
8. [8] Zhao W, Wang L, Zhang Z. Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems 2019; 163: p. 283-304.

9. [9] Rao RV,VSavsani VJ,Vakharia D. Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 2011; 43(3): p. 303-315.
10. [10] Moosavi SHS, Bardsiri VK. Poor and rich optimization algorithm: A new human-based and multi populations algorithm. Engineering Applications of Artificial Intelligence 2019; 86: p. 165-181.
11. [11] Ray T, Liew KM. Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation 2003; 7(4): p. 386-396.
12. [12] Zhao W, Wang L, Zhang Z. Supply-demand-based optimization: A novel economics-inspired algorithm for global optimization. IEEE Access 2019; 7: p. 73182-73206.
13. [13] Civicioglu P. Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and computation 2013; 219(15): p. 8121-8144.
14. [14] Liu J, Wu C, Wu G,Wang X. A novel differential search algorithm and applications for structure design. Applied Mathematics and Computation 2015; 268: p. 246-269.
15. [15] Wang L, Cao Q, Zhang Z, Mirjalili S, Zhao W. Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence 2022; 114: p. 105082.
16. [16] Kennedy J, Eberhart R. Particle swarm optimization. in Proceedings of ICNN'95-international conference on neural networks 1995: IEEE.
17. [17] Karaboga D, Akay B. A comparative study of artificial bee colony algorithm. Applied mathematics and computation 2009: 214(1): p. 108-132.
18. [18] Dorigo M, Birattari M, Stutzle T. Ant colony optimization. IEEE computational intelligence magazine 2006; 1(4): p. 28-39.
19. [19] Zhao W, Zhang Z, Wang L. Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence 2020; 87: p. 103300.
20. [20] Liu J, Chen Y, Liu X, Zuo F, Zhou H. An efficient manta ray foraging optimization algorithm with individual information interaction and fractional derivative mutation for solving complex function extremum and engineering design problems. Applied Soft Computing 2024; 150: p. 111042.
21. [21] Houssein EH, Ibrahim IE, Neggaz N, Hassaballah M,Wazery YM. An efficient ECG arrhythmia classification method based on Manta ray foraging optimization. Expert Systems with Applications 2021; 181: p. 115131.
22. [22] Houssein EH, Emam MM, Ali AA. Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images. Neural Computing and Applications 2021; 33(24): p. 16899-16919.
23. [23] Hemeida MG, Ibrahim AA, Mohamed AAA, Alkhalaf S, El-Dine AMB. Optimal allocation of distributed generators DG based Manta Ray Foraging Optimization algorithm (MRFO). Ain Shams Engineering Journal 2021; 12(1): p. 609-619.
24. [24] Tang A, Zhou H, Han T, Xie L. A Modified Manta Ray Foraging Optimization for Global Optimization Problems. IEEE Access 2021; 9: p. 128702-128721.
25. [25] Gokulkumari G. Classification of brain tumor using manta ray foraging optimization-based DeepCNN classifier. Multimedia Research 2020; 3(4): p. 32-42.
26. [26] Hassan MH, Houssein EH, Mahdy MA, Kamel S. An improved manta ray foraging optimizer for cost-effective emission dispatch problems. Engineering Applications of Artificial Intelligence 2021; 100: p. 104155. [27] Micev M, Ćalasan M, Ali ZM, Hasanien HM, Abdel Aleem SHE. Optimal design of automatic voltage regulation controller using hybrid simulated annealing – Manta ray foraging optimization algorithm. Ain Shams Engineering Journal 2021; 12(1): p. 641-657.
27. [28] Kahraman HT, Akbel M, Duman S. Optimization of Optimal Power Flow Problem Using Multi-Objective Manta Ray Foraging Optimizer. Applied Soft Computing 2022; 116: p. 108334.
28. [29] Got A, Zouache D, Moussaoui A. MOMRFO: Multi-objective Manta ray foraging optimizer for handling engineering design problems. Knowledge-Based Systems 2022; 237: p. 107880.
29. [30] Elaziz MA, Abualigah L, Ewees AA, Al-qaness MAA, Mostafa RR, Yousri D, Ibrahim RA. Triangular mutation-based manta-ray foraging optimization and orthogonal learning for global optimization and engineering problems. Applied Intelligence 2023; 53(7): p. 7788-7817.
30. [31] Zouache D, Abdelaziz FB. Guided Manta Ray foraging optimization using epsilon dominance for multi-objective optimization in engineering design. Expert Systems with Applications 2022; 189: p. 116126.
31. [32] Ekinci S, Izci D, Kayri M. An Effective Controller Design Approach for Magnetic Levitation System Using Novel Improved Manta Ray Foraging Optimization. Arabian Journal for Science and Engineering 2022; 47(8): p. 9673-9694.
32. [33] Yousri D, AbdelAty AM, Al-qaness MAA, Ewees AA, Radwan AG, Abd Elaziz M. Discrete fractional-order Caputo method to overcome trapping in local optima: Manta Ray Foraging Optimizer as a case study. Expert Systems with Applications 2022; 192: p. 116355.
33. [34] Daqaq F, Kamel S, Ouassaid M, Ellaia R, Agwa AM. Non-Dominated Sorting Manta Ray Foraging Optimization for Multi-Objective Optimal Power Flow with Wind/Solar/Small- Hydro Energy Sources. Fractal and Fractional 2022; 6(4):194.
34. [35] Zhu D, Wang S, Zhou C,Yan S. Manta ray foraging optimization based on mechanics game and progressive learning for multiple optimization problems. Applied Soft Computing 2023; 145: p. 110561.
35. [36] Yang J, Liu Z, Zhang X, Hu G. Elite Chaotic Manta Ray Algorithm Integrated with Chaotic Initialization and Opposition-Based Learning. Mathematics 2022; 10(16):2960.
36. [37] Ghosh KK, Guha R, Bera SK, Kumar N, Sarkar R. S-shaped versus V-shaped transfer functions for binary Manta ray foraging optimization in feature selection problem. Neural Computing and Applications 2021; 33(17): p. 11027-11041.
37. [38] Yildizdan G. Bin_MRFOA: A Novel manta ray foraging optimization algorithm for binary optimization. Konya Journal Of Engineering Sciences 2023; 11(2): p. 449-467.
38. [39] Wang D, Liu X, Li Z, Lu B, Guo A, Chai G. Discrete Manta Ray Foraging Optimization Algorithm And Its Application In Spectrum Allocation. Journal Of Computer Applications 2022; 42(1): p. 215.
39. [40] Ozsoydan FB, Baykasoglu A. Analysing the effects of various switching probability characteristics in flower pollination algorithm for solving unconstrained function minimization problems. Neural Computing and Applications 2019; 31(11): p. 7805-7819.
40. [41] Bansal JC, Singh PK, Saraswat M, Verma A, Jadon SS, Abraham A. Inertia Weight strategies in Particle Swarm Optimization. in 2011 Third World Congress on Nature and Biologically Inspired Computing 2011.
41. [42] Jusof MFM, Nasir ANK, Razak AAA, Rizal NAM, Ahmad MA, Muhamad IH. Adaptive-Somersault MRFO for Global Optimization with an Application to Optimize PD Control. in Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020: NUSYS’20 2022:Springer.
42. [43] Hakli H. BinEHO: a new binary variant based on elephant herding optimization algorithm. Neural Computing and Applications 2020; 32: p. 16971-16991.
43. [44] Ma J, Xia D, Guo H, Wang Y, Niu X, Liu Z, Jiang S. Metaheuristic-based support vector regression for landslide displacement prediction: A comparative study. Landslides 2022; 19(10): p. 2489-2511.
44. [45] Korkmaz S, Şahman MA, Cinar AC, Kaya E. Boosting the oversampling methods based on differential evolution strategies for imbalanced learning. Applied Soft Computing 2021; 112: p.107787.
45. [46] Kumar A, Wu G, Ali MZ, Mallipeddi R, Suganthan PN, Das S. A test-suite of non-convex constrained optimization problems from the real-world and some baseline results. Swarm and Evolutionary Computation 2020; 56: p. 100693.
46. [47] Friedman M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the american statistical association 1937; 32(2012): p. 675-701.
47. [48] Uymaz SA. Evaluation of the Most Valuable Player Algorithm for Solving Real-World Constrained Optimization Problems. Bilişim Teknolojileri Dergisi 2021.
48. [49] Wansasueb K, Panmanee S, Panagant N, Pholdee N, Bureerat S, Yildiz AR. Hybridised differential evolution and equilibrium optimiser with learning parameters for mechanical and aircraft wing design. Knowledge-Based Systems 2022; 239: p. 107955.