2017 | OriginalPaper | Chapter
Hint
Swipe to navigate through the chapters of this book
Although different metaheuristic algorithms have some differences in approaches to determine the optimum solution, however, their general performance is approximately the same. They start the optimization with random solutions, and the subsequent solutions are based on randomization and some other rules. With progressing the optimization process, the power of rules increases, and the power of randomization decreases. It seems that these rules can be modeled by a familiar concept of physics as well known as the fields of forces (FOF). FOF is a concept which is utilized in physics to explain the reason of the operation of the universe. The virtual FOF model is approximately simulated by using the concepts of real-world fields such as gravitational, magnetic, or electric fields (Kaveh and Talatahari [1]).
Please log in to get access to this content
To get access to this content you need the following product:
Advertisement
1.
go back to reference Kaveh A, Talatahari S (2011) An enhanced charged system search for configuration optimization using the concept of fields of forces. Struct Multidiscip Optim 43(3):339–351 CrossRef Kaveh A, Talatahari S (2011) An enhanced charged system search for configuration optimization using the concept of fields of forces. Struct Multidiscip Optim 43(3):339–351
CrossRef
2.
go back to reference Kaveh A, Farahmand Azar B, Talatahari S (2008) Ant colony optimization for design of space trusses. Int J Space Struct 23(3):167–181 CrossRef Kaveh A, Farahmand Azar B, Talatahari S (2008) Ant colony optimization for design of space trusses. Int J Space Struct 23(3):167–181
CrossRef
3.
go back to reference Gribbin J (1998) Particle physics from A to Z. Weidenfeld & Nicolson, London Gribbin J (1998) Particle physics from A to Z. Weidenfeld & Nicolson, London
4.
go back to reference Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–286 CrossRefMATH Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–286
CrossRefMATH
5.
go back to reference Kaveh A, Talatahari S (2010) Optimal design of skeletal structures via the charged system search algorithm. Struct Multidiscip Optim 41(6):893–911 CrossRef Kaveh A, Talatahari S (2010) Optimal design of skeletal structures via the charged system search algorithm. Struct Multidiscip Optim 41(6):893–911
CrossRef
6.
go back to reference Kaveh A, Talatahari S (2010) Charged system search for optimum grillage systems design using the LRFD-AISC code. J Constr Steel Res 66(6):767–771 CrossRef Kaveh A, Talatahari S (2010) Charged system search for optimum grillage systems design using the LRFD-AISC code. J Constr Steel Res 66(6):767–771
CrossRef
7.
go back to reference Imai K, Schmit LA (1981) Configuration optimisation of trusses. J Struct Div ASCE 107:745–756 Imai K, Schmit LA (1981) Configuration optimisation of trusses. J Struct Div ASCE 107:745–756
8.
go back to reference Felix JE (1981) Shape optimization of trusses subjected to strength, displacement, and frequency constraints. M.Sc. thesis, Naval Postgraduate School Felix JE (1981) Shape optimization of trusses subjected to strength, displacement, and frequency constraints. M.Sc. thesis, Naval Postgraduate School
9.
go back to reference Rahami H, Kaveh A, Gholipoura Y (2008) Sizing, geometry and topology optimization of trusses via force method and genetic algorithm. Eng Struct 30:2360–2369 CrossRef Rahami H, Kaveh A, Gholipoura Y (2008) Sizing, geometry and topology optimization of trusses via force method and genetic algorithm. Eng Struct 30:2360–2369
CrossRef
10.
go back to reference Zheng QZ, Querin OM, Barton DC (2006) Geometry and sizing optimization of discrete structure using the genetic programming method. Struct Multidiscip Optim 231:452–461 CrossRef Zheng QZ, Querin OM, Barton DC (2006) Geometry and sizing optimization of discrete structure using the genetic programming method. Struct Multidiscip Optim 231:452–461
CrossRef
11.
go back to reference Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798 CrossRef Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798
CrossRef
12.
go back to reference Vanderplaats GN, Moses F (1972) Automated design of trusses for optimum geometry. J Struct Div ASCE 98:671–690 Vanderplaats GN, Moses F (1972) Automated design of trusses for optimum geometry. J Struct Div ASCE 98:671–690
13.
go back to reference Yang JP (1996) Development of genetic algorithm-based approach for structural optimization. Ph.D. thesis, Nanyang Technology University, Singapore Yang JP (1996) Development of genetic algorithm-based approach for structural optimization. Ph.D. thesis, Nanyang Technology University, Singapore
14.
go back to reference Soh CK, Yang JP (1996) Fuzzy controlled genetic algorithm for shape optimization. J Comput Civil Eng ASCE 10(2):143–150 CrossRef Soh CK, Yang JP (1996) Fuzzy controlled genetic algorithm for shape optimization. J Comput Civil Eng ASCE 10(2):143–150
CrossRef
15.
go back to reference Yang JP, Soh CK (1997) Structural optimization by genetic algorithms with tournament selection. J Comput Civil Eng ASCE 11(3):195–200 CrossRef Yang JP, Soh CK (1997) Structural optimization by genetic algorithms with tournament selection. J Comput Civil Eng ASCE 11(3):195–200
CrossRef
16.
go back to reference Wu SJ, Chow PT (1995) Integrated discrete and configuration optimization of trusses using genetic algorithms. Comput Struct 55(4):695–702 CrossRefMATH Wu SJ, Chow PT (1995) Integrated discrete and configuration optimization of trusses using genetic algorithms. Comput Struct 55(4):695–702
CrossRefMATH
17.
go back to reference Kaveh A, Kalatjari V (2004) Size/geometry optimization of trusses by the force method and genetic algorithm. Z Angew Math Mech 84(5):347–357 MathSciNetCrossRefMATH Kaveh A, Kalatjari V (2004) Size/geometry optimization of trusses by the force method and genetic algorithm. Z Angew Math Mech 84(5):347–357
MathSciNetCrossRefMATH
18.
go back to reference Rajeev S, Krishnamoorthy CS (1997) Genetic algorithms based methodologies for design optimisation of trusses. J Struct Eng ASCE 123:350–358 CrossRef Rajeev S, Krishnamoorthy CS (1997) Genetic algorithms based methodologies for design optimisation of trusses. J Struct Eng ASCE 123:350–358
CrossRef
19.
go back to reference Schutte JF, Groenwold AA (2003) Sizing design of truss structures using particle swarms. Struct Multidiscip Optim 25:261–269 CrossRef Schutte JF, Groenwold AA (2003) Sizing design of truss structures using particle swarms. Struct Multidiscip Optim 25:261–269
CrossRef
20.
go back to reference Kaveh A, Talatahari S (2009) Hybrid algorithm of harmony search, particle swarm and ant colony for structural design optimization, Chapter 5 of a book entitled: Harmony search algorithms for structural design optimization, edit. Z.W. Geem. Springer, Berlin, Heidelberg Kaveh A, Talatahari S (2009) Hybrid algorithm of harmony search, particle swarm and ant colony for structural design optimization, Chapter 5 of a book entitled: Harmony search algorithms for structural design optimization, edit. Z.W. Geem. Springer, Berlin, Heidelberg
21.
go back to reference American Institute of Steel Construction (AISC) (1989) Manual of steel construction—allowable stress design, 9th edn. AISC, Chicago, IL American Institute of Steel Construction (AISC) (1989) Manual of steel construction—allowable stress design, 9th edn. AISC, Chicago, IL
- Title
- Field of Forces Optimization
- DOI
- https://doi.org/10.1007/978-3-319-46173-1_5
- Author:
-
A. Kaveh
- Publisher
- Springer International Publishing
- Sequence number
- 5
- Chapter number
- Chapter 5