PSOLVER: A new hybrid particle swarm optimization algorithm for solving continuous optimization problems

Expert Systems with Applications

Volumn 37, Issue 10, 2010, Pages 6798-6808

  Kayhan, Ali Haydar ^a   Ceylan, Huseyin ^a   Tamer Ayvaz, M ^a   Gurarslan, Gurhan ^a  

^a PAMUKKALE UNIVERSITY   (Turkey)

Author keywords

Hybridization; Optimization; Particle swarm optimization; Solver; Spreadsheets

Indexed keywords

HEURISTIC ALGORITHMS; PARTICLE SWARM OPTIMIZATION (PSO); PROBLEM SOLVING; SPREADSHEETS;

COMPARATIVE STUDIES; CONTINUOUS OPTIMIZATION PROBLEMS; ENGINEERING DESIGN PROBLEMS; HEURISTIC SOLUTIONS; HYBRID PARTICLE SWARM OPTIMIZATION ALGORITHM; HYBRIDIZATION; LOCAL OPTIMIZERS; SOLVER;

OPTIMIZATION;

EID: 81355132073     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.03.046     Document Type: Article

References (64)

1. Abido, M. A. (2002). Optimal power flow using particle swarm optimization. International Journal of Electrical Power and Energy Systems, 24(7), 563-571. (Pubitemid 34532350)
2. Arora, J. S. (1989). Introduction to optimum design. New York: McGraw-Hill.
3. Ayvaz, M. T., Kayhan, A. H., Ceylan, H., & Gurarslan, G. (2009). Hybridizing harmony search algorithm with a spreadsheet solver for solving continuous engineering optimization problems. Engineering Optimization, 41(12), 1119-1144.
4. Baumgartner, U., Magele, Ch., & Renhart, W. (2004). Pareto optimality and particle swarm optimization. IEEE Transactions on Magnetics, 40(2), 1172-1175.
5. Becerra, R. L., & Coello, C. A. C. (2006). Cultured differential evolution for constrained optimization. Computer Methods in Applied Mechanics and Engineering, 195(33-36), 4303-4322. (Pubitemid 44287665)
6. Brandstätter, B., & Baumgartner, U. (2002). Particle swarm optimization-mass spring system analogon. IEEE Transactions on Magnetics, 38(2), 997-1000. (Pubitemid 34531330)
7. Chootinan, P., & Chen, A. (2006). Constraint handling in genetic algorithms using a gradient-based repair method. Computers and Operations Research, 33(8), 2263-2281. (Pubitemid 41802289)
8. Ciuprina, G., Loan, D., & Munteanu, I. (2002). Use of intelligent-particle swarm optimization in electromagnetics. IEEE Transactions on Magnetics, 38(2), 1037-1040. (Pubitemid 34531340)
9. Cockshott, A. R., & Hartman, B. E. (2001). Improving the fermentation medium for Echinocandin B production part II: Particle swarm optimization. Process Biochemistry, 36, 661-669. (Pubitemid 32192838)
10. Coello, C. A. C. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41(2000), 113-127.
11. Coello, C. A. C., & Becerra, R. L. (2004). Efficient evolutionary optimization through the use of a cultural algorithm. Engineering Optimization, 36(2), 219-236.
12. Coello, C. A. C., & Montes, E. M. (2002). Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Advanced Engineering Informatics, 16(2002), 193-203.
13. Dorigo, M., & Di Caro, G. (1999). Ant colony optimisation: A new meta-heuristic. In Proceedings of the congress on evolutionary computation (Vol. 2, pp. 1470-1477).
14. Eberhart, R. C., & Hu, X. (1999). Human tremoe analysis using particle swarm optimization. In Proceedings of the congress on evolutionary computation, Washington, DC, USA (pp. 1927-1930).
15. Eberhart, R. C., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proc. of the sixth int. symp. micro machine and human science, Nagoya, Japan (pp. 39-43).
16. Eberhart, R. C., & Shi, Y. (2001). Tracking and optimizing dynamic systems with particle swarms. In Proceedings of congress on evolutionary computation, Seoul, Korea (pp. 27-30).
17. Elegbede, C. (2005). Structural reliability assessment based on particle swarm optimization. Structural Safety, 27(2), 171-186. (Pubitemid 40019890)
18. Fan, S. S., Liang, Y. C., & Zahara, E. (2004). Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions. Engineering Optimization, 36(4), 401-418.
19. Fan, S. S., & Zahara, E. (2007). A hybrid simplex search and particle swarm optimization for unconstrained optimization. European Journal of Operational Research, 181, 527-548. (Pubitemid 46413581)
20. Ferreira, E. N. C., & Salcedo, R. (2001). Can spreadsheet solvers solve demanding optimization problems. Computer Applications in Engineering Education, 9(1), 49-56. (Pubitemid 33692542)
21. Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., & Alizadeh, Y. (2008). Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Computer Methods in Applied Mechanics and Engineering, 197, 3080-3091.
22. Fourie, P. C., & Groenwold, A. A. (2002). The particle swarm optimization algorithm in size and shape optimization. Structural and Multidisciplinary Optimization, 23(4), 259-267. (Pubitemid 35354676)
23. Frontline System Inc. (1999). A tutorial on spreadsheet optimization.
24. Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68. (Pubitemid 32543074)
25. Ghaffari-Miab, M., Farmahini-Farahani, A., Faraji-Dana, R., & Lucas, C. (2007). An efficient hybrid Swarm intelligence-gradient optimization method for complex time Green's functions of multilayer media. Progress in Electromagnetics Research, 77, 181-192. (Pubitemid 47409921)
26. Glover, F. (1977). Heuristic for integer programming using surrogate constraints. Decision Sciences, 8(1), 156-166.
27. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley.
28. Hedar, A. D., & Fukushima, M. (2006). Derivative-free filter simulated annealing method for constrained continuous global optimization. Journal of Global Optimization, 35(4), 521-549. (Pubitemid 44078244)
29. He, Q., & Wang, L. (2006). An effective co-evolutionary particle swarm optimization for engineering optimization problems. Engineering Application of Artificial Intelligence, 20, 89-99.
30. He, Q., & Wang, L. (2007). A hybrid particle swarm optimization with a feasibility based rule for constrained optimization. Applied Mathematics and Computation, 186, 1407-1422. (Pubitemid 46497348)
31. Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Ann Arbor: University of Michigan Press.
32. Houck, C. R., Joines, J. A., & Kay, M. G. (1996). Comparison of genetic algorithms, random start, and two-opt switching for solving large location-allocation problems. Computers and Operations Research, 23(6), 587-596.
33. Houck, C. R., Joines, J. A., & Wilson, J. R. (1997). Empirical investigation of the benefits of partial Lamarckianism. Evolutionary Computation, 5(1), 31-60. (Pubitemid 127717837)
34. Hu, X., & Eberhart, R. C. (2001). Tracking dynamic systems with PSO: Where's the cheese? In Proceedings of workshop on particle swarm optimization, Indianapolis, USA.
35. Kannan, B. K., & Kramer, S. N. (1994). An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. Journal of Mechanical Design, 116, 318-320.
36. Kazuhiro, I., Shinji, N., & Masataka, Y. (2006). Hybrid swarm optimization techniques incorporating design sensitivities. Transactions of the Japan Society of Mechanical Engineers, 72(719), 2264-2271.
37. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks, Piscataway, USA (pp. 1942-1948).
38. Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. In Proc. IEEE int. conf. systems, man. and cybernetics (Vol. 5, pp. 4104-4108).
39. Kirkpatrick, S., Gelatt, C., & Vecchi, M. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
40. Koziel, S., & Michalewicz, Z. (1999). Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evolutionary Computation, 7, 19-44.
41. Lasdon, L. S., Waren, A. D., Jain, A., & Ratner, M. (1978). Design and testing of a generalized reduced gradient code for nonlinear programming. ACM Transactions on Mathematical Software, 4(1), 34-49.
42. Lee, K. S., & Geem, Z. W. (2005). A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering, 194, 3902-3933. (Pubitemid 40815237)
43. Michalewicz, Z. (1992). Genetic algorithm + data structure = evolution programs. New York: Springer-Verlag.
44. Microsoft. (1995). Microsoft Excel - Visual Basic for applications. Washington: Microsoft Press.
45. OTC. (2009). Optimization Technology Center's Web site (online). (accessed 31.03.2009).
46. Perez, R. E., & Behdinan, K. (2007). Particle swarm approach for structural design optimization. Computers and Structures, 85, 1579-1588. (Pubitemid 47362226)
47. Rosen, E. M. (1997). Visual Basic for applications, Add-Ins and Excel 7.0. CACHE News (Vol. 45, pp. 1-3).
48. Runarsson, T. P., & Yao, X. (2000). Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 4(3), 284-292.
49. Runarsson, T. P., & Yao, X. (2005). Search biases in constrained evolutionary optimization. IEEE Transactions on Systems, Man and Cybernetics, 35(2), 233-243. (Pubitemid 40654435)
50. Salerno, J. (1997). Using particle swarm optimization technique to train a recurrent neural model. In Proc. of the ninth IEEE int. conf. tools and artificial intelligence, USA (pp. 45-49).
51. Salman, A., Ahmad, I., & Al-Madani, S. (2002). Particle swarm optimization for task assignment problem. Microprocessors and Microsystems, 26, 363-371.
52. Shannon, M. W. (1998). Evolutionary algorithms with local search for combinatorial optimization. PhD thesis, University of California, San Diego.
53. Shi, Y., & Eberhart, R. C. (1998). Parameter selection in particle swarm optimization. Evolutionary Programming VII. Lecture Notes in Computer Science (Vol. 1447). Berlin: Springer.
54. Slade, W. H., Ressom, H. W., Musavi, M. T., & Miller, R. L. (2004). Inversion of ocean color observations using particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing, 42(9), 1915-1923.
55. Stokes, L., & Plummer, J. (2004). Using spreadsheet solvers in sample design. Computational Statistics and Data Analysis, 44(3), 527-546. (Pubitemid 37358754)
56. Tandon, V. (2000). Closing the gap between CAD/CAM and optimized CNC end milling. MSc thesis, Purdue School of Engineering and Technology, Indianapolis, USA.
57. Tandon, V., El-Mounayri, H., & Kishawy, H. (2002). NC end milling optimization using evolutionary computation. International Journal of Machine Tools and Manufacture, 42(5), 595-605. (Pubitemid 34116088)
58. Van Den Bergh, F., & Engelbrecht, A. P. (2000). Cooperative learning in neural networks using particle swarm optimizers. South African Computer Journal, 26, 84-90.
59. Venter, G., & Sobieszczanski-Sobieski, J. (2004). Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization. Structural and Multidisciplinary Optimization, 26(1-2), 121-131.
60. Victoire, T. A., & Jeyakumar, A. E. (2004). Hybrid PSO-SQP for economic dispatch with valve-point effect. Electric Power Systems Research, 71(1), 51-59.
61. Wikipedia. (2009). The free Encyclopedia web site (online). (accessed 31.03.2009).
62. Yoshida, H., Fukuyama, Y., Takayama, S., & Nakanishi, Y. (1999). A particle swarm optimization for reactive power and voltage control in electric power systems considering voltage security assessment. In Proc. IEEE int. conf. systems, man. and cybernetics, Tokyo, Japan (pp. 497-502).
63. Zahara, E., & Hu, C. H. (2008). Solving constrained optimization problems with hybrid particle swarm optimization. Engineering Optimization, 40(11), 1031-1049.
64. Zahara, E., & Kao, Y. T. (2009). Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems. Expert Systems with Applications, 36, 3880-3886.