Dynamic selective pressure using hybrid evolutionary and ant system strategies for structural optimization

International Journal for Numerical Methods in Engineering

Volumn 73, Issue 4, 2008, Pages 544-563

  Kaveh, Ali ^a   Shahrouzi, M ^b  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

^b INTERNATIONAL INSTITUTE OF EARTHQUAKE ENGINEERING AND SEISMOLOGY IIEES   (Iran)

Author keywords

Ant colony metaphor; Direct index coding; Evolutionary strategies; Genetic algorithm; Structural topology and sizing optimization; Variable mutation band

Indexed keywords

GENETIC ALGORITHMS; STRATEGIC PLANNING; STRUCTURAL OPTIMIZATION; TOPOLOGY; TRAVELING SALESMAN PROBLEM;

ANT COLONY METAPHOR; EVOLUTIONARY STRATEGY;

STRUCTURAL DESIGN;

GENETIC ALGORITHMS; STRATEGIC PLANNING; STRUCTURAL DESIGN; STRUCTURAL OPTIMIZATION; TOPOLOGY; TRAVELING SALESMAN PROBLEM;

EID: 38349195073     PISSN: 00295981     EISSN: 10970207     Source Type: Journal    
DOI: 10.1002/nme.2088     Document Type: Article

References (50)

1. Rechenberg I. Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Frommann-Holzboog: Stuttgart, 1973.
2. Hu J, Goodman ED. Hierarchical fair competition model for parallel evolutionary algorithms. Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, IEEE World Congress on Computational Intelligence, Honolulu, HI, 2002.
3. Fraser AS. Simulation of genetic systems by automatic digital computers. I. Introduction. Australian Journal of Biological Sciences 1857; 10:484-491.
4. Holland HJ. Adaptation in Natural and Artificial Systems, an Introductory Analysis with Application to Biology, Control and Artificial Intelligence. The University of Michigan Press: Ann Arbor, MI, 1975.
5. Goldberg DE, Samtani MP. Engineering optimization via genetic algorithm. Proceedings of the 9th Conference on Electronic Computation, University of Alabama, Birmingham. ASCE: New York, NY, 1986; 471-484.
6. Whitley D, Rana S, Heckendorn RB. Representation issues in neighborhood search and evolutionary algorithms. In Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, Quagliarella D, Periaux J, Poloni C, Winter G (eds). Wiley: New York, 1998; 39-57.
7. Coello Coello CA. Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering 2002; 191:1245-1287.
8. Kauffman SA. The Origins of Order: Self-organization and Selection in Evolution. Oxford University Press: New York, 1993.
9. Hiroyasu T, Miki M, Shimosaka H, Tanimura Y. Structural optimization by real-coded probabilistic model-building GA. Proceedings of the 2002 IEEE International Conference on Systems, Man and Cybernetics (SMC 2002). Academic Journal 2002.
10. Kaveh A, Shahrouzi M. A hybrid approach for genetic search in structural optimization with minimal initial population. In press.
11. Rothlauf F. Representations for Genetic and Evolutionary Algorithms. Studies in Fuzziness and Soft Computing, vol. 104. Springer: Heidelberg, 1st edition 2002, 2nd printing, 2003.
12. Camp CV, Pezeshk S, Cao G. Design of framed structures using a genetic algorithm. In Advances in Structural Optimization, Frangopol DM, Cheng FY (eds). ASCE: New York, 1997; 19-30.
13. Schmitt LM. Fundamental study: theory of genetic algorithms. Theoretical Computer Science 2001; 259:1-61.
14. Yuan B, Gallagher M. A hybrid approach to parameter tuning in genetic algorithms. Proceedings of the IEEE Congress in Evolutionary Computation (CEC'05), vol. 2, Napier University, Edinburgh, U.K., 2-5 September 2005; 1096-1103.
15. Petrovski A, Brownlee A, McCall J. Statistical optimisation and tuning of GA factors. Proceedings of the IEEE Congress in Evolutionary Computation (CEC'05), vol. 1, Napier University, Edinburgh, U.K., 2-5 September 2005; 758-764.
16. Lis J, Lis M. Self-adapting parallel genetic algorithm with the dynamic mutation probability, crossover rate and population size. In Proceedings of the 1st Polish National Conference on Evolutionary Computation, Oficina Wydawnica Politechniki, Warszawskiej, Arabas J (ed.), 1996; 324-329.
17. Thierens D. Adaptive mutation rate control schemes in genetic algorithms. Proceedings of the 2002 IEEE World Congress on Computational Intelligence: Congress on Evolutionary Computation, Honolulu, HI. IEEE Press: New York, 2002; 980-985.
18. Thierens D. Scalability problems of simple genetic algorithms. Evolutionary Computation 2000; 7(3):331-352.
19. Kaveh A, Shahrouzi M. Direct index coding for discrete size optimization of structures by genetic algorithms. International Journal of Civil Engineering 2006; 3(3-4):166-181.
20. Kaveh A, Shahrouzi M. Simultaneous topology and size optimization of structures by genetic algorithm using minimal length chromosome. Engineering Computations 2006; 6(5):664-674.
21. Kaveh A, Shahrouzi M. Simulated annealing and adaptive dynamic variable band mutation for structural optimization by genetic algorithms. Asian Journal of Civil Engineering 2006; 7(6):655-674.
22. Baeck T. Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. Proceedings of the First IEEE Conference on Evolutionary Computation, Orlando, Florida, U.S.A., 27-29 June 1994; 57-62.
23. Affenzeller M. Transferring the concept of selective pressure from evolutionary strategies to genetic algorithms. Proceedings of the 14th International Conference on Systems Science, vol. 2, Wrocaw, Poland, 11-14 September 2001; 346-353.
24. Affenzeller M. A generic evolutionary computation approach based up on genetic algorithms and evolution strategies. Journal of Systems Science 2002; 28(4):59-71.
25. Affenzeller M, Wagner S. SASEGASA: an evolutionary algorithm for retarding premature convergence by self-adaptive selection pressure steering. IWANN 2003, Lecture Notes in Computer Science. Springer: Berlin/Heidelberg, 2003.
26. Beyer H-G. Toward a theory of evolution strategies: the (μ, λ)-theory. Evolutionary Computation 1995; 2(4): 381-407.
27. Beker T, Hadany L. Noise and elitism in evolutionary computation. In Soft Computing Systems- Design, Management and Applications, Abraham A et al. (eds). IOS Press: Amsterdam, 2002.
28. Affenzeller M, Wagner S. SASEGASA: a new generic parallel evolutionary algorithm for achieving highest quality results. Journal of Heuristics, Special Issue on New Advances on Parallel Meta-Heuristics for Complex Problems 2004; 10(3):239-263.
29. Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies. In Proceedings of the 1st European Conference on Artificial Life, Paris, France, Varela F, Bourgine P (eds). Elsevier Publishing: Amsterdam, The Netherlands, 1991; 134-142.
30. Dorigo M, Gambardella LM. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1997; 1:53-66.
31. Maniezzo V, Carbonaro A. Ant colony optimization: an overview. In Essays and Surveys in Metaheuristics, Ribeiro C (ed.). Kluwer Academic Publishers: Dordrecht, 2001; 21-44.
32. De Jong KA. Genetic algorithms are not function optimizers. In Foundations of Genetic Algorithms 2, Whitley LD (ed.). Morgan Kaufmann: Los Altos, CA, 1993.
33. Kaveh A, Shahrouzi M. A hybrid ant strategy and genetic algorithm to tune the population size for efficient structural optimization. Engineering Computations 2007; 24(3):237-254.
34. Kaveh A, Shahrouzi M. Extracting optimal structural design family in genetic search incorporating ant colony approach, submitted.
35. Goldberg DE, Richardson J. Genetic algorithms with sharing for multimodal function optimization. Proceedings of the 2nd International Conference on Genetic Algorithms and their Application, Lawrence Erlbaum Associates, 1987; 41-49.
36. Rajeev S, Krishnamoorthy CS. Discrete optimization of trusses using genetic structures. Journal of Structural Engineering (ASCE) 1992; 118:1233-1350.
37. Cai J, Thierauf G. Discrete structural optimization using evolution strategies. Technical Report, Department of Civil Engineering, University of Essen, Essen, Germany, 1993.
38. Deb K, Gulati S. Design of truss-strucWres for minimum weight using genetic algorithms. Finite Elements in Analysis and Design 2001; 37:447-465.
39. Coello Coello CA. Discrete optimization of trusses using genetic algorithms. EXPERSYS-94: Expert Systems Applications and Artificial Intelligence, Gournaysur-Marne, France, IITT International, 1994; 331-336.
40. Galante M. Genetic algorithms as an approach to optimize real-world trusses. International Journal for Numerical Methods in Engineering 1996; 39:361-382.
41. Ghasemi MR, Hinton E, Wood RD. Optimization of trusses using genetic algorithms for discrete and continuous variables. Engineering Computations 1999; 3:272-301.
42. Turkkan N. Discrete optimization of structures using a floating-point genetic algorithm. Annual Conference of the Canadian Society for Civil Engineering, Moncton, NB, Canada, 2003.
43. Kaveh A, Kalatjari V. Topology optimization of trusses using genetic algorithm force method and graph theory. International Journal for Numerical Methods in Engineering 2003; 58:771-791.
44. AISC. Allowable Stress Design and Plastic Design Specifications for Structural Steel Buildings (9th edn). American Institute of Steel Construction: Chicago, IL, 1989.
45. Kaveh A. Structural Mechanics: Graph and Matrix Methods (3rd edn). Research Studies Press (Wiley): Somerset, U.K., 2004.
46. Chai S, Shi LS, Sun HC. An application of relative difference quotient algorithm to topology optimization of truss structures with discrete variables. Structural Optimization 1999; 18:48-55.
47. Huang M-W, Arora JS. Optimal design of steel structures using standard sections. Structural Optimization 1997; 14:24-35.
48. Furlong RW. Column slenderness and charts for design. AISC Journal, Proceedings 1971; 68(1):9-18.
49. Reinlet G. TSPLIB - A traveling salesman problem library. ORSA Journal on Computing 1991; 3:376-384.
50. Reinlet G. TSPLIB. http://www.iwr.uni-heidelberg.de/iwr/comopt/soft/ TSPLIB95/TSPLIB.html