Artificial coronary circulation system: A new bio-inspired metaheuristic algorithm

Document Type : Article

Authors

1 School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran-16, Iran

2 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

A new swarm intelligence optimization technique is proposed, called Artificial Coronary Circulation System (ACCS). This optimization method simulates the coronary arteries (veins) growth on human heart. In this algorithm, each capillary is considered as a candidate solution. This algorithm starts with a random initial population of candidate solutions, and by using Coronary Growth Factor (CGF) evaluates the solutions. In each run the best candidate solution is selected as the main coronary vessel (artery or vein) and the other capillaries are considered as searchers of the search space. Then the heart decides other candidates to move toward/away from the main coronary vessels and searches for the optimal solution by using the heart memory. Finally, application of the proposed algorithm is demonstrated using some benchmark functions and some mechanical problems, confirming the potential and capability of the new algorithm.

Keywords

References

References:
1. Pardalos, P.M. and Floudas, C.A., Deterministic Global Optimization: Theory, Algorithms and Applications, Kluwer Academic Publishers, USA (2000).
2. Mirjalili, S., Mirjalili, S.M., and Lewis, A. "Grey wolf optimizer", Adv. Eng. Softw., 69, pp. 46-61 (2014).
3. Spall, J.C., Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, 65, John Wiley & Sons (2005).
4. Kaveh, A., Advances in Metaheuristic Algorithms for Optimal Design of Structures, 2nd Edn., Springer, Switzerland (2017).
5. Boussa, D.I., Lepagnot, J., and Siarry, P. "A survey on optimization metaheuristics", Inform. Sci., 237, pp. 82-117 (2013).
6. Gogna, A. and Tayal, A. "Metaheuristics: review and application", J. Experim. Theor. Artific. Intell., 25(4), pp. 503-526 (2013).
7. Miettinen, K., Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, John Wiley & Sons, Inc, New York (1999).
8. Knowles, J. and Corne, D. "The Pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimization", CEC 99. Proc. Congress. Evol. Comput. (1999).
9. Koza, J.R., Genetic Programming II, Automatic Discovery of Reusable Subprograms, MIT Press, Cambridge, MA (1992).
10. Kaveh, A. and Talatahari, S. "A novel heuristic optimization method: charged system search", Acta Mech., 213(3-4), pp. 267-289 (2010).
11. Kaveh, A., and Mahdavi, V.R. "Colliding bodies optimization: a novel meta-heuristic method", Comput. Struct., 139, pp. 18-27 (2014).
12. Hatamlou, A. "Black hole: A new heuristic optimization approach for data clustering", Inform. Sci., 222, pp. 175-184 (2013).
13. Kaveh, A. and Bakhshpoori, T. "Water evaporation optimization: A novel physically inspired optimization algorithm", Comput. Struct., 167, pp. 69-85 (2016).
14. Kennedy, J., Particle Swarm Optimization Encyclopedia of Machine Learning, Springer, pp. 760-766 (2010).
15. Naka, S., Genji, T., Yura, T., et al. "Hybrid particle swarm optimization based distribution state estimation using constriction factor approach", Proc. Int. Conf. SCIS and ISIS (2002).
16. Gandomi, A.H., Yang, X.-S., and Alavi, A.H. "Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems", Eng. Comput., 29(1), pp. 17-35 (2013).
17. Yang, X.-S. "Fire y algorithms for multimodal optimization", Int. Symp. Stochastic Algorithms (2009).
18. Mucherino, A. and Seref, O. "Monkey search: A novel metaheuristic search for global optimization", AIP Conf. Proc. (2007).
19. Karaboga, D. and Basturk, B. "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", J. Global Optimiz., 39(3), pp. 459-471 (2007).
20. Mirjalili, S. and Lewis, A. "The whale optimization algorithm", Adv. Eng. Softw., 95, pp. 51-67 (2016).
21. Gandomi, A.H. and Alavi, A.H. "Krill herd: A new bio-inspired optimization algorithm", Commun. Nonlinear Sci. Numer. Simul., 17(12), pp. 4831-4845 (2012).
22. Rao, R.V., Savsani, V.J., and Vakharia, D.  Teachinglearning- based optimization: A novel method for constrained mechanical design optimization problems", Comput.-Aided Des., 43(3), pp. 303-315 (2011).
23. Talatahari, S., Azar, B.F., Sheikholeslami, R., et al. "Imperialist competitive algorithm combined with chaos for global optimization", Commun. Nonlinear Sci. Numer. Simul., 17(3), pp. 1312-1319. (2012).
24. Omran, M.G., Alsharhan, S., and Clerc, M. "A modified intellects-masses optimizer for solving realworld optimization problems", Swarm Evol. Comput., 41, pp. 159-166 (2018).
25. Alatas, B. "A novel chemistry based metaheuristic optimization method for mining of classification rules", Expert Syst. Appl., 39(12), pp. 11080-11088 (2012).
26. Javidy, B., Hatamlou, A., and Mirjalili, S. "Ions motion algorithm for solving optimization problems", Appl. Soft Comput., 32, pp. 72-79 (2015).
27. Schalkoff, R.J., Artificial Neural Networks, 1, McGraw-Hill, New York (1997).
28. Timmis, J., Artificial Immune Systems: A Novel Data Analysis Technique Inspired by the Immune Network Theory, Department of Computer Science, University of Wales, Aberystwyth (2000).
29. Liang, Y.-C. and Cuevas Juarez, J.R. "A novel metaheuristic for continuous optimization problems: Virus optimization algorithm", Eng. Optimiz., 48(1), pp. 73- 93 (2016).
30. Wolpert, D.H. and Macready, W.G. "No free lunch theorems for optimization", IEEE Trans. Evol. Comput., 1(1), pp. 67-82 (1997).
31. Kaveh, A. and Shokohi, F. "A hybrid optimization algorithm for the optimal design of laterally-supported castellated beams", Scientia Iranica, Trans Civil Eng., 23(2), pp. 508-519 (2016).
32. Kaveh, A. and Talatahari, S. "Hybrid charged system search and particle swarm optimization for engineering design problems", Eng Comput, 28(4), pp. 423-440 (2011).
33. Kaveh, A. and Ilchi Ghazaan, M. "A new metaheuristic algorithm: vibrating particles system", Scientia Iranica, Trans Civil Eng., 24(2), pp. 551-566 (2017).
34. Hatamlou, A. "Heart: A novel optimization algorithm for cluster analysis", Prog. Artific. Intell., 2(2-3), pp. 167-173 (2014).
35. Keelan, J., Chung, E.M., and Hague, J.P. "Simulated annealing approach to vascular structure with application to the coronary arteries", Royal Soc. Open Sci., 3(2), p. 150431 (2016).
36. Chen, X., Niu, P., Niu, X., et al. "Growth, ageing and scaling laws of coronary arterial trees", J. Royal Soc. Interface, 12(113), p. 20150830 (2015).
37. Gandomi, A.H., Yang, X.-S., Alavi, A.H. et al. "Bat algorithm for constrained optimization tasks", Neural Comput. Appl., 22(6), pp. 1239-1255 (2013).
38. L ukasik, S. and Zak, S. "Fire y algorithm for continuous constrained optimization tasks", Lecture Notes in Computer Science, 5796, pp. 97-106 (2009).
39. Shareef, H., Ibrahim, A.A., and Mutlag, A.H. "Lightning search algorithm", Appl. Soft Comput., 36, pp. 315-333 (2015).
40. Coello, C.A. and Mezura Montes, E. "Constrainthandling in genetic algorithms through the use of dominance-based tournament selection", Adv. Eng. Inform., 16(3), pp. 193-203 (2002).
41. He, Q. andWang, L. "An e ective co-evolutionary particle swarm optimization for constrained engineering design problems", Eng. Appl. Artific. Intell, 20(1), pp. 89-99 (2007).
42. Mezura-Montes, E. and Coello, C.A.C. "An empirical study about the usefulness of evolution strategies to solve constrained optimization problems", Int. J. General Syst., 37(4), pp. 443-473 (2008).
Scientia Iranica
Volume 26, Issue 5
Transactions on Civil Engineering (A)
September and October 2019
Pages 2731-2747

Files

Share

How to cite

Statistics