Abstract
This chapter consists of two parts. In the first part, the recently developed optimization algorithm based on Newton’s law of cooling is presented, which is called Thermal Exchange Optimization (TEO) algorithm [1]. In the second part, the improved version of TEO is named as Improved TEO and abbreviated as ITEO [2] is presented.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–68
Kaveh A, Dadras A, Bakhshpoori T (2018) Improved thermal exchange optimization algorithm for discrete optimization of skeletal structures. Smart Struct Syst 21(3):263–278
Newton I (1809) A scale of the degrees of heat. Philosoph Trans Royal Soc Lond Abridged 4:572–575
Kirkpatrick S, Gelatt C, Vecchi M (1983) Optimization by simulated annealing. Sci 220(4598):671–680
Rosenbrock HH (1960) An automatic method for finding the greatest or least value of a function. Comput J 3:175–184
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102
Mirjalili SA, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948
Storn R, Price K (1997) Differential evolutionary simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248
Deb K (1991) Optimal design of a welded beam via genetic algorithms. AIAA J 29:2013–2015
Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Indust 41:113–127
Lee KS, Geem Z (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798
He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problem. Eng Appl Artific Intell 20:89–99
Montes EM, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37:443–473
Coello CAC (2000) Constraint-handling using an evolutionary multiobjective optimization technique. Civ Eng Environ Syst 17:319–346
Arora JS (1989) Introduction to optimum design. McGraw-Hill, New York, USA
Gandomi A et al (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255
Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69–85
Huang FZ, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356
Wang Y, Cai Z, Zhou Y (2009) Accelerating adaptive trade-off model using shrinking space technique for constrained evolutionary optimization. Int J Numer Methods Eng 77(11):1501–1534
Montemurro M, Vincenti A, Vannucci P (2013) The automatic dynamic penalisation method (ADP) for handling constraints with genetic algorithms. Comput Methods Appl Mech Eng 256:70–87
Thanedar PB, Vanderplaats GN (1995) Survey of discrete variable optimization for structural design. J Struct Eng 121:301–305
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2–4):311–338
Yang X-S, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483
Blum C, Li X (2008) Swarm intelligence in optimization. In: Swarm intelligence. Springer, pp 43–85
Davison JH, Adams PF (1974) Stability of braced and unbraced frames. J Struct Div 100(2):319–334
Kaveh A, Bakhshpoori T (2013) Optimum design of space trusses using cuckoo search algorithm with lévy flights. Iranian J Sci Technol Trans Civil Eng 37(1):1–15
Kaveh A, Bakhshpoori T (2016) An accelerated water evaporation optimization formulation for discrete optimization of skeletal structures. Comput Struct 177:218–228
Kaveh A, Ilchi Ghazaan M (2015) A comparative study of CBO and ECBO for optimal design of skeletal structures. Comput Struct 153:137–147
Kaveh A, Talatahari S (2010) Optimum design of skeletal structures using imperialist competitive algorithm. Comput Struct 88(21–22):1220–1229
Kaveh A, Talatahari S (2012) Charged system search for optimal design of frame structures. Appl Soft Comput 12(1):382–393
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput-Aided Des 43(3):303–315
Rueda Torres J, Erlich I (2016) Solving the CEC2016 Real-Parameter Single Objective Optimization Problems through MVMO-PHM: Technical Report
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2012) Mine blast algorithm for optimization of truss structures with discrete variables. Comput Struct 102:49–63
Yeniay Ö (2005) Penalty function methods for constrained optimization with genetic algorithms. Math Comput Appl 10(1):45–56
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kaveh, A. (2021). Thermal Exchange Metaheuristic Optimization Algorithm. In: Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer, Cham. https://doi.org/10.1007/978-3-030-59392-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-59392-6_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59391-9
Online ISBN: 978-3-030-59392-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)