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Multi-tracker Optimization Algorithm: A General Algorithm for Solving Engineering Optimization Problems

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Iranian Journal of Science and Technology, Transactions of Mechanical Engineering Aims and scope Submit manuscript

Abstract

In this paper, a new computational population-based optimization algorithm, which is designed based on the advantages and disadvantages of other evolutionary optimization algorithms introduced so far, is proposed. This new algorithm, which is named as “multi-tracker optimization algorithm,” due to a multi-level structure of trackers within it, has some unique features, such as increasing the accuracy of the optimal point and continuous local search after convergence in order to escape from local minima simultaneously. Another important advantage of this algorithm is optimizing time-varying dynamical problems and tracking the optimal point. These characteristics make the algorithm very efficient for optimization problems, especially in the field of engineering. For a thorough investigation and comparison of this algorithm with other efficient optimization algorithms, different optimization problems such as static, dynamic, unconstrained and constrained, each of which has different challenges, are considered. The results of applying this algorithm on the abovementioned basic problems show the superiority of this algorithm over other efficient evolutionary algorithms.

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References

  • Akhtar S, Tai K, Ray T (2002) A socio-behavioural simulation model for engineering design optimization. Eng Optim 34(4):341–354

    Article  Google Scholar 

  • Ali M (2012). Swarm directions embedded differential evolution for faster convergence of global optimization problems. Int J Artif Intell Tools 21(3)

  • Aragon VS, Esquivel SC, Coello CAC (2010) A modified version of a T-cell algorithm for constrained optimization problems. Int J Numer Methods Eng 84:351–378

    MATH  Google Scholar 

  • Arora JS (1989) Introduction to optimum design. McGrow-Hill, New York

    Google Scholar 

  • Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Evolutionary computation, 2007, CEC 2007, IEEE Congress, pp 4661–4667

  • Atiqullah MM, Rao SS (2000) Simulated annealing and parallel processing: an implementation for constrained global design optimization. Eng Optim 32(5):659–685

    Article  Google Scholar 

  • Azad SK, Hasancebi O (2014) An elitist self-adaptive step-size search for structural design optimization. Appl Soft Comput J 19:226–235. doi:10.1016/j.asoc.2014.02.017

    Article  Google Scholar 

  • Back T, Hoffmeister F, Schwefel H (1991) A survey of evolution strategies. In: Proceedings of the fourth international conference on genetic algorithms and their applications, pp 2–9

  • Boussaid I, Chatterjee A, Siarry P, Ahmed-Nacer M (2012) Biogeography-based optimization for constrained optimization problems. Comput Oper Res 39:3293–3304

    Article  MATH  MathSciNet  Google Scholar 

  • Cagnina LC, Esquivel SC, Coello CAC (2008) Solving engineering optimization problems with the simple constrained particle optimizer. Informatica 32:319–326

    MATH  Google Scholar 

  • Coelho LDS (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37:1676–1683

    Article  Google Scholar 

  • Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127

    Article  Google Scholar 

  • Coello CAC, Becerra RL (2004) Efficient evolutionary optimization through the use of a cultural algorithm. Eng Optim 36:219–236

    Article  Google Scholar 

  • Coello CAC, Montes EF (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inf 16:193–203

    Article  Google Scholar 

  • Deb K (1991) Optimal design of a welded beam via genetic algorithms. AIAA J 29(11):2013–2015

    Article  Google Scholar 

  • Deb K (1997) GeneAS: a robust optimal design technique for mechanical component design. In: Dasgupta D, Michalewicz Z (eds) Evolutionary algorithms in engineering applications. Springer, Berlin, pp 497–514

    Chapter  Google Scholar 

  • Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186:311–338

    Article  MATH  Google Scholar 

  • Dimopoulos GG (2007) Mixed-variable engineering optimization based on evolutionary and social metaphors. Comput Methods Appl Mech Eng 196:803–817

    Article  MATH  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B 26(1):29–41

    Article  Google Scholar 

  • Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan

  • Fogel L (1994) Evolutionary programming in perspective: the top-down view. In: Zurada JM, Marks R Jr, Robinson C (eds) Computational intelligence: imitating life. IEEE Press, Piscataway, NJ

    Google Scholar 

  • Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simulat 17:4831–4845

    Article  MATH  MathSciNet  Google Scholar 

  • Gandomi AH, Yang XS, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89:2325–2336

    Article  Google Scholar 

  • Gandomi AH, Yang XS, Alavi AH, Talatahari S (2012) Bat algorithm for constrained optimization tasks. Neural Comput Appl

  • Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony Search. Simulation 76(2):60–68

    Article  Google Scholar 

  • Hasancebi O, Azad SK (2015) Adaptive dimensional search: a new metaheuristic algorithm for discrete truss sizing optimization. Comput Struct 154:1–16. doi:10.1016/j.compstruc.2015.03.014

    Article  Google Scholar 

  • He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99

    Article  Google Scholar 

  • He S, Prempain E, Wu QH (2004) An improved particle swarm optimizer for mechanical design optimization problems. Eng Optim 36(5):585–605

    Article  MathSciNet  Google Scholar 

  • Hedar AR, Fukushima M (2006) Derivative-free filter simulated annealing method for constrained continuous global optimization. J Glob Optim 35(4):521–649

    Article  MATH  MathSciNet  Google Scholar 

  • Huang FZ, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186:340–356

    MATH  MathSciNet  Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report, Erciyes University

  • Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    MATH  MathSciNet  Google Scholar 

  • Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294

    Article  Google Scholar 

  • Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213:267–289

    Article  MATH  Google Scholar 

  • Kazemzadeh Azad S, Hasancebi O, Kazemzadeh Azad S (2013) Upper bound strategy for metaheuristic based design optimization of steel frames. Adv Eng Softw 57:19–32. doi:10.1016/j.advengsoft.2012.11.016

    Article  Google Scholar 

  • Kazemzadeh-Parsi MJ (2014a) A modified firefly algorithm for engineering design optimization problems. IJST Trans Mech Eng 38:403–421

    Google Scholar 

  • Kazemzadeh-Parsi MJ (2014b) Optimal shape design for heat conduction using smoothed fixed grid finite element method. IJST Trans Mech Eng 39(M2):467–487

    MATH  Google Scholar 

  • Kazemzadeh-Parsi MJ, Daneshmand F, Ahmadfard MA, Adamowski J, Martel R (2015a) Optimal groundwater remediation design of pump and treat systems via a simulation-optimization approach and firefly algorithm. Eng Optim 47(1):1–17

    Article  Google Scholar 

  • Kazemzadeh-Parsi MJ, Daneshmand F, Ahmadfard MA, Adamowski J (2015b) Optimal remediation design of unconfined contaminated aquifers based on the finite element method and a modified firefly algorithm. Water Resour Manag 29:2895–2912

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization, neural networks. In: Proceedings of IEEE international conference, vol. 4, pp 1942–1948

  • Kirtrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MATH  MathSciNet  Google Scholar 

  • Lee KS, Geem ZW (2005) A new metaheuristic algorithm for continues engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3933

    Article  MATH  Google Scholar 

  • Leite JPB, Topping BHV (1998) Improved genetic operators for structural optimization. Adv Eng Softw 29(7–9):529–562

    Article  Google Scholar 

  • Lemonge ACC, Barbosa HJC (2004) An adaptive penalty scheme for genetic algorithms in structural optimization. Int J Numer Methods Eng 59:703–736

    Article  MATH  Google Scholar 

  • Liu JL (2005) Novel orthogonal simulated annealing with fractional factorial analysis to solve global optimization problems. Eng Optim 37(5):499–519

    Article  MathSciNet  Google Scholar 

  • Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188:1567–1579

    MATH  MathSciNet  Google Scholar 

  • McCrea WH, Whipple FJW (1940) Random paths in two and three dimensions. Proc R Soc Edinburgh 60:281–298

    Article  MATH  MathSciNet  Google Scholar 

  • Moezi SA, Ghahramani A, Zakeri E, Bazargan-Lari Y, Assadsangabi B (2012) Shape optimization of BELTs via genetic algorithm. In: International conference on mechanical engineering and advanced technology

  • Moezi SA, Zakeri E, Zare A, Nedaei M (2015a) On the application of modified cuckoo optimization algorithm to the crack detection problem of cantilever Euler–Bernoulli beam. Comput Struct 157:42–50

    Article  Google Scholar 

  • Moezi SA, Zakeri E, Bazargan-lari Y, Zare A (2015b) 2 & 3-Dimensional optimization of connecting rod with genetic and modified cuckoo optimization algorithms. IJST Trans Mech Eng 39:39–49

    Google Scholar 

  • Moezi SA, Rafeeyan M, Ebrahimi S (2015c) Sliding mode control of 3-RPR parallel robot on the optimal path using cuckoo optimization algorithm. Mod J Mech Eng 15(2):147–158

    Google Scholar 

  • Moezi SA, Rafeeyan M, Zakeri E, Zare A (2016) Simulation and experimental control of a 3-RPR parallel robot using optimal fuzzy controller and fast on/off solenoid valves based on the PWM wave. ISA Trans 61(2016):265–286

    Article  Google Scholar 

  • Moradi S, Razi P, Fatahi L (2011) On the application of bees algorithm to the problem of crack detection of beam-type structures. Comput Struct 89(23–24):2169–2175

    Article  Google Scholar 

  • Nakrani S, Tovey C (2004) On honey bees and dynamic server allocation in Internet hosting centers. Adapt Behav 12:223–240

    Article  Google Scholar 

  • Oftadeh R, Mahjoob MJ, Shariatpanahi M (2010) A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput Math Appl 60:2087–2098

    Article  MATH  Google Scholar 

  • Oldenhuis R (2012) Many test functions for global optimizers. Mathworks. Retrieved 1 Nov 2012

  • Osman K, Erol IE (2006) A new optimization method: big bang-big crunch. Adv Eng Softw 37(2):106–111

    Article  Google Scholar 

  • Parsopoulos KE, Vrahatis MN (2005) Unified particle swarm optimization for solving constrained engineering optimization problems. Lecture notes in computer science, 3612 (LNFA), pp 582–591

  • Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2005) The bees algorithm. Technical Note, Manufacturing Engineering Center, Cardiff University

  • Ragsdell KM, Phillips DT (1976) Optimal design of a class of welded structures using geometric programming. ASME J Eng Ind 98(3):1021–1025

    Article  Google Scholar 

  • Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput J 11(8):5508–5518

    Article  Google Scholar 

  • Ranganathan A (2004) The Levenberg–Marquardt algorithm 3 LM as a blend of gradient descent and Gauss–Newton itera. Internet httpexcelsior cs ucsb educoursescs290ipdfL MA pdf, vol. 142, pp. 1–5

  • Rao RV, Savsani VJ, Balic J (2012) Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Eng Optim. doi:10.1080/0305215X.2011.652103

    Google Scholar 

  • Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput 7(4):386–396

    Article  Google Scholar 

  • Ray T, Saini P (2001) Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Optim 33(3):735–748

    Article  Google Scholar 

  • Rothlauf F (2006) Representations for genetic and evolutionary algorithms

  • Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. ASME J Mech Des 112(2):223–229

    Article  Google Scholar 

  • Shen H, Zhu Y, Niu B, Wu QH (2009) An improved group search optimizer for mechanical design optimization problems. Prog Nat Sci 19:91–97

    Article  Google Scholar 

  • Siddall JN (1972) Analytical decision making in engineering design. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Snyman JA (2005) practical mathematical optimization: an introduction to basic optimization theory and classical and new gradient-based algorithms. Springer, NewYork. ISBN 0-387-24348-8

    MATH  Google Scholar 

  • Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, Berkeley, CA, USA

  • Talbi E (2009) Metaheuristics: from design to implementation. Wiley, Hoboken, NJ

    Book  MATH  Google Scholar 

  • Tamura K, Yasuda K (2011) Spiral dynamics inspired optimization. J Adv Comput Intell Intell Inf 15(8):1116–1122

    Article  Google Scholar 

  • Tsoulos IG (2008) Modifications of real code genetic algorithm for global optimization. Appl Math Comput 203:598–607

    MATH  MathSciNet  Google Scholar 

  • Watson LT, Haftka RT (1989) Modern homotopy methods in optimization. Comput Methods Appl Mech Eng 74(3):289–305

    Article  MATH  MathSciNet  Google Scholar 

  • Yang XS (2009) Firefly algorithms for multimodal optimization. Lecture notes compututer science (including subseries of lecture notes artificial intellegence. lecture notes bioinformatics), vol. 5792 LNCS, pp 169–178

  • Yang XS (2010b) A new metaheuristic bat-inspired algorithm. In: Gonzalez JR et al. (ed) Nature inspired cooperative strategies for optimization NISCO. Studies in computational intelligence, Vol. 284. Springer, Berlin, pp 65–74

  • Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of the world congress on nature and biologically inspired computing (NaBIC 2009), IEEE Publications, pp 210–214

  • Zakeri E, Farahat S (2015) Safe path planning and control of an unmanned underwater vehicle (UUV) using particle swarm optimization and fuzzy logic control method. Mod Mech Eng 14(14):199–210

    Google Scholar 

  • Zakeri E, Farahat S, Moezi SA, Zare A (2016) Path planning for unmanned underwater vehicle in 3d space with obstacles using spline-imperialist competitive algorithm and optimal interval type-2 fuzzy logic controller. Latin Am J Solids Struct 13:6

    Article  Google Scholar 

  • Zhang C, Wang HP (1993) Mixed-discrete nonlinear optimization with simulated annealing. Eng Optim 17(3):263–280

    Google Scholar 

  • Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inform Sci 178(15):3043–3074

    Article  Google Scholar 

  • Zhang J, Liang C, Huang Y, Wu J, Yang S (2009) An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization. Appl Math Comput 211:392–416

    MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Young Researchers and Elite Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran

    Ehsan Zakeri, Seyed Alireza Moezi & Amin Zare

  2. Department of Mechanical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

    Yousef Bazargan-Lari

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  1. Ehsan Zakeri
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  2. Seyed Alireza Moezi
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  3. Yousef Bazargan-Lari
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  4. Amin Zare
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Correspondence to Ehsan Zakeri.

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Zakeri, E., Moezi, S.A., Bazargan-Lari, Y. et al. Multi-tracker Optimization Algorithm: A General Algorithm for Solving Engineering Optimization Problems. Iran J Sci Technol Trans Mech Eng 41, 315–341 (2017). https://doi.org/10.1007/s40997-016-0066-9

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