Jump of Circles: A New Way to Solve the Engineering Optimization Problems

Document Type : Research Article

Authors

1 civil engineering department, faculty of engineering, university of Sistan and Balouchestan, Zahedan, Iran

2 Civil Engineering Department, University of Sistan and Baluchestan, Zahedan, Iran

Abstract

In this paper, a new meta-heuristic optimization method called the Jump of Circles Optimization Method is introduced. In any optimization problem, an answer zone is defined in which the optimization algorithms search the space to find the optimal answer. The method presented in this paper uses two important pillars in searching the answer zone. The first pillar is to use geometric principles. The Jump of circles uses the circle with decreasing radius. The second pillar is to use the meta-heuristic application. In meta-heuristic algorithms, the search points distribute randomly and jump in the answer zone. In the proposed method, the center of the searching circle jumps and sits on the optimal point of each step. The proposed algorithm solves the optimization problem in two phases. The first phase is optimal area exploration and the second phase is exploiting the exploration. Finally, the most optimal point that will be obtained from the two phases, is the optimal answer to the problem.  

Keywords

Main Subjects

References

  1. Holland JH. Genetic algorithms. Scientific American, 267 (1992) 66-72.
  2. Rao RV, Savsani VJ, Vakharia DP. Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information Science, 183(1) (2012) 1-15.
  3. Geem ZW, Kim JH, Loganathan G. A new heuristic optimization algorithm: harmony search. Simulation, 76(2) (2001) 60-68.
  4. Fogel D. Artificial intelligence through simulated evolution. Wiley-IEEE Press, 2009.
  5. He S, Wu Q, Saunders J. A novel group search optimizer inspired by animal behavioral ecology. Proceedings of the 2006 IEEE congress on evolutionary computation, (2006) 1272–1278.
  6. Rechenberg I. Evolutionsstrategien. Springer Berlin Heidelberg, (1978) 83-114.
  7. R. Koza. “Genetic programming”. 1992.
  8. Simon D. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6) (2008) 702-713.
  9. Dasgupta D, Zbigniew M, editors. Evolutionary algorithms in engineering applications. Springer Science & Business Media, 2013.
  10. Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of the 1995 IEEE international conference on neural networks, (1995) 1942–1948.
  11. Dorigo M, Birattari M, Stutzle T. Ant colony optimization. IEEE Computational Intelligence, 1(4) (2006) 28-39.
  12. Askarzadeh A, Rezazadeh A. A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer. International Journal of Energy Research, 37(10) (2012) 1196-1204.
  13. Pan W-T. A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26 (2012) 69-74.
  14. Gandomi AH, Alavi AH. Krill Herd: a new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12) (2012) 4831-4845.
  15. Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science, 220(4598) (1983) 671-680.
  16. Webster B, Bernhard PJ. A local search optimization algorithm based on natural principles of gravitation. Proceedings of the 2003 international conference on information and knowledge engineering (IKE’03), (2003) 255–261.
  17. Erol OK, Eksin I. A new optimization method: big bang–big crunch. Advances in Engineering Software, 37(2) (2006) 106-111.
  18. Formato RA. Central force optimization: A new metaheuristic with applications in applied electromagnetics. Progress in Electromagnetics Research, 77 (2007) 425-491.
  19. Rashedi E, Nezamabadi Pour H, Saryazdi S. GSA: a gravitational search algorithm. Information Science, 179(13) (2009) 2232-2248.
  20. Naderi, A., Sohrabi, M.R., Ghasemi, M.R., Dizangian B., Total and Partial Updating Technique: A Swift Approach for Cross-Section and Geometry Optimization of Truss Structures. KSCE J Civ. Eng., 24 (2020) 1219–1227.
  21. Schmit Jr. L.A., Miura H., Approximation concepts for efficient structural synthesis. US National Aeronautics and Space Administration, 1976.
  22. Lee K.S., Geem Z.W., A new structural optimization method based on the harmony search algorithm. Computers & Structures,82(9-10) (2004) 781-798.
  23. Kaveh A., Rahami H., Analysis, design and optimization of structures using force method and genetic algorithm. International Journal for Numerical Methods in Engineering, 65(10) (2006) 1570-1584.
  24. Li L.J., Huang Z.B., Liu F., A heuristic particle swarm optimization method for truss structures with discrete variables. Computers & Structures, 87(7-8) (2009) 435-443.
  25. Varaee H., Ghasemi M.R., Engineering optimization based on ideal gas molecular movement Algorithm. Engineering with Computers, 33(1) (2016) 71-93.
  26. Sonmez M. Artificial Bee Colony algorithm for optimization of truss structures. Applied Soft Computing, 11(2) (2011) 2406-2418.
  27. Kaveh A., Sheikholeslami R., Talatahari S., Keshvari-Ilkhichi M., Chaotic swarming of particles: A new method for size optimization of truss structures. Advances in Engineering Software, 67(2014) 136-147.
  28. Schmit Jr. L.A., Farshi B., Some Approximation Concepts for Structural Synthesis. AIAA Journal, 12 (5) (1974) 692-699.
  29. Khan M.R., Willmert K.D., Thornton W.A., An Optimality Criterion Method for Large-Scale Structures. AIAA Journal, 17(7) (1979) 753-761.
  30. Erbatur F., Hasancebi O., Tutuncu I., Kilic H., Optimal design of planar and space structures with genetic algorithms. Computers & Structures, 75(2) (2000) 209-224.
  31. Degertekin S.O., Improved harmony search algorithms for sizing optimization of truss structures. Computers & Structures, 92-93 (2012) 229-241.
  32. Ong Y.S., Keane A.J., Meta-Lamarckian Learning in Memetic Algorithms. IEEE Transactions on Evolutionary Computation, 8(2) (2004) 99-110.
  33. Salar M, Ghasemi M R, Dizangian B. A FAST GA-BASED METHOD FOR SOLVING TRUSS OPTIMIZATION PROBLEMS. International Journal of Optimization in Civil Engineering, 6(1) (2016) 101-114.
  34. Coello C.A.C., Montes E.M., “Use of dominance-based tournament selection to handle constraints in genetic algorithms”, Intelligent Engineering Systems through Artificial Neural Network, 11 (2001) 177-182.
  35. He Q., Wang L., “An effective co-evolutionary particle swarm optimization for constrained engineering design problems”, Engineering Applications of Artificial Intelligence, 20(1) (2007) 89-99.
  36. Gao L., Hailu A., “Comprehensive learning particle swarm optimizer for constrained mixed-variable optimization problems”, International Journal of Computational Intelligence Systems, 3(6) (2010) 832-842.
  37. GHOHANI ARAB H., MAHALLATI RAYENI A., GHASEMI M., An Effective Improved Multi-objective Evolutionary Algorithm (IMOEA) for Solving Constraint Civil Engineering Optimization Problems, Teknik Dergi, 32(2) (2021).
Amirkabir Journal of Civil Engineering
Volume 53, Issue 5
August 2021
Pages 1917-1936

Files

Share

How to cite

Statistics