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Evolutionary Intelligence
Published in: Evolutionary Intelligence 1/2019

17-11-2018 | Research Paper

A movable damped wave algorithm for solving global optimization problems

Authors: Rizk M. Rizk-Allah, Aboul Ella Hassanien

Published in: Evolutionary Intelligence | Issue 1/2019

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Abstract

This paper presents a new optimization methodology called movable damped wave algorithm for solving global optimization problems. The proposed methodology mimics mathematically the behavior of waveform induced by oscillating phenomena. It starts by creating multiple initial random solutions which are updated through introducing a mathematical model based on a damped wave function. In the proposed methodology, the updating mechanisms of solutions are based on designing a mathematical relation for the movable wave with the aim to effectively achieve robust solutions. Therefore, this methodology can be more robust, statistically sound, and convergent quickly to the optimal global solution. The performance of the proposed is validated by carrying out on 23 benchmark problems and three engineering design problems. The results show vividly that the proposed is a reliable algorithm and outperforms the comparative algorithms in most cases.
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Appendix
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Literature
1.
Gaoji S, Ruiqing Z, Yanfei L (2016) Joint operations algorithm for large-scale global optimization. Appl Soft Comput 38:1025–1039CrossRef
2.
Rao SS (2009) Engineering optimization: theory and practice. Wiley, New YorkCrossRef
3.
X. S. Yang. Engineering optimisation: an introduction with metaheuristic applications. Wiley, New York, 2010
4.
5.
Das S, Suganthan P (2011) Differential evolution: a survey of the state of-the-art. IEEE Trans Evol Comput 15:4–31CrossRef
6.
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, pp. 39–43
7.
Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. In: Proceedings of the first European conference on artificial life, pp 134–42
8.
Rizk-Allah RM, Zaki EM, El-Sawy AA (2013) Hybridizing ant colony optimization with firefly algorithm for unconstrained optimization problems. Appl Math Comput 224:473–483MathSciNetMATH
9.
Rizk M. Rizk-Allah RA, El-Sehiemy S, Deb G-G, Wang (2017) A novel fruit fly framework for multi-objective shape design of tubular linear synchronous motor. J Supercomput 73(3):1235–1256CrossRef
10.
11.
Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: NaBIC2009, World congress on Nature and biologically inspired computing, pp 210–214,
12.
13.
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRef
14.
Seyedali Mirjalili SCA (2016) A sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133CrossRef
15.
Seyedali Mirjalili A, Lewis (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRef
16.
Seyedali, Mirjalili (2015) The ant lion optimizer. Adv Eng Softw 83:80–98CrossRef
17.
Mousa AA, Waiel F, Abd El-Wahed RM, Rizk-Allah (2011) A hybrid ant colony optimization approach based local search scheme for multiobjective design optimizations. Electr Power Syst Res 81:1014–1023CrossRef
18.
El-Sawy AA, Zaki EM, Rizk-Allah RM, (2013) A novel hybrid ant colony optimization and firefly algorithm for solving constrained engineering design problems. J Nat Sci Math 6(1):1–22
19.
El-Sawy AA, Zaki EM, Rizk-Allah RM (2013) novel hybrid ant colony optimization and firefly algorithm for multi-objective optimization problems. Int J Math Arch 4(1):152–161MATH
20.
Rizk-Allah RM (2014) A novel multi-ant colony optimization for multi-objective resource allocation problems. Int J Math Arch 5:183–192
21.
Rizk-Allah RM (2016) Hybridization of fruit fly optimization algorithm and firefly algorithm for solving nonlinear programming problems. Int J Swarm Intel Evol Comput 5:1–10CrossRef
22.
Rizk M. Rizk-Allah RagabAEl-Sehiemy, Wang G-G (2018) A novel parallel hurricane optimization algorithm for secure emission/economic load dispatch solution. Appl Soft Comput 63:206–222CrossRef
23.
24.
25.
Kaveh A, Mohammad A, Motie Share M, Moslehi (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech 224(1):85–107MATHCrossRef
26.
Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112:283–294CrossRef
27.
Kaveh A, Farhoudi N (2013) A new optimization method: dolphin echolocation. Adv Eng Softw 59:53–70CrossRef
28.
Kaveh A, Zolghadr A (2014) Democratic PSO for truss layout and size optimization with frequency constraints. Comput Struct 130:10–21CrossRef
29.
Kaveh A, Bakhshpoori T (2016) A new metaheuristic for continuous structural optimization: water evaporation optimization. Struct Multidispl Optim Issue 1:54 23–43.
30.
Kaveh A, Ilchi M, Ghazaan (2014) Enhanced colliding bodies optimization for design problems with continuous and discrete variables. Adv Eng Softw 77:66–75MATHCrossRef
31.
Kaveh A, Ghazaan MI (2017) Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints. Acta Mech 1:307–322 228(MathSciNetCrossRef
32.
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans on Evol Comput Vol 1:67–82CrossRef
33.
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102CrossRef
34.
Joines JA, Houck CR (1994) On the use of non-stationary penalty functions to solve nonlinearly constrained optimization problems with GA’s. In Proceedings of the first IEEE conference on evolutionary computation, IEEE world congress on computational intelligence, pp 579–584
35.
Garcia S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization. J Heuristics 15:617–644MATHCrossRef
36.
Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112CrossRef
37.
Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solving structural optimization problems. Eng Comput 29(1):17–35CrossRef
38.
39.
Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36):3902–3933MATHCrossRef
40.
Deb K (1991) Optimal design of a welded beam via genetic algorithms. AIAA J 29(11):2013–2015CrossRef
41.
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2):311–338MATHCrossRef
42.
Garg H (2014) Solving structural engineering design optimization problems using an artificial bee colony algorithm. J Ind Manag Optim 10(3):777–794MathSciNetMATHCrossRef
43.
Hwang SF, He RS (2006) A hybrid real-parameter genetic algorithm for function optimization. Adv Eng Inf 20(1):7–21CrossRef
44.
Mehta VK, Dasgupta B (2012) A constrained optimization algorithm based on the simplex search method. Eng Optim 44(5):537–550MathSciNetCrossRef
45.
Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput 7(4):386–396CrossRef
46.
47.
Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127CrossRef
48.
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 497–514CrossRef
49.
He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for con-strained engineering design problems. Eng Appl Artif Intell 20:89–99CrossRef
50.
Cagnina LC, Esquivel SC, Coello CAC (2008) Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica 32:319–326MATH
51.
Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27:155–182MATHCrossRef
52.
Kaveh A, Talatahari S (2009) Engineering optimization with hybrid particle swarm and ant colony optimization. Asian J Civ Eng (Build Hous.) 10:611–628
53.
Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform vol 16:193–203CrossRef
54.
Coelho LS (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37:1676–1683CrossRef
55.
Montes EM, Coello CAC, Reyes JV, Davila LM (2007) Multiple trial vectors in differential evolution for engineering design. Eng Optim 39:567–589MathSciNetCrossRef
56.
Montes EM, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37:443–473MathSciNetMATHCrossRef
57.
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report, TR06, Erciyes University, Engineering Faculty, Computer Engineering Department
Metadata
Title
A movable damped wave algorithm for solving global optimization problems
Authors
Rizk M. Rizk-Allah
Aboul Ella Hassanien
Publication date
17-11-2018
Publisher
Springer Berlin Heidelberg
Published in
Evolutionary Intelligence / Issue 1/2019
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-018-0187-8

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