Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms
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Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(12):108–132
Civicioglu P (2013) Artificial cooperative search algorithm for numerical optimization problems. Inform Syst 229:58–76
Yang XS, Deb S (2009) Cuckoo search via levy flights. World congress on nature and biologically inspired computing-Nabic’2009. Coimbatore, India, vol 4, pp 210–214
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417
Yong W, Han-Xion L, Tingwen H, Long L (2014) Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Appl Soft Comput 218:232–247
Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219:8121–8144
Civicioglu P, Beşdok E (2013) A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346
Civicioglu P (2012) Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46:229–247
Bratton D, Kennedy J (2007) Defining a standard for particle swarm optimization. In: IEEE swarm intelligence symposium, Honolulu 1-4244-0708-7
Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10:281–295
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73
Omran MGH, Clerc M (2015) http://www.particleswarm.info/Programs.html . Accessed 20 Feb 2018
Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195
Price KV, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer, Berlin
Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18
Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 13:2232–2248
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13:2592–2612
Wang D, Wua Z, Fei Y, Zhang W (2014) Structural design employing a sequential approximation optimization approach. Comput Struct 134:75–87
Maheri MR, Narimani MM (2014) An enhanced harmony search algorithm for optimum design of side sway steel frames. Comput Struct 136:78–89
Civicioglu P, Alcı M (2004) Edge detection of highly distorted images suffering from impulsive noise. AEU Int J Electron C 58(6):413–419
Wu X, Yang Z (2013) Nonlinear speech coding model based on genetic programming. Appl Soft Comput 13(7):3314–3323
Yoon Y, Kim YH (2013) An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Trans Cybern 43(5):1473–1483
Civicioglu P, Alcı M, Besdok E (2004) Using an exact radial basis function artificial neural network for impulsive noise suppression from highly distorted image databases. LNCS 3261:383–391
Chauhan RS, Arya SK (2013) An optimal design of IIR digital filter using particle swarm optimization. Appl Artif Intell 27(6):429–440
Yan Y, He Y, Hu Y, et al (2014) Video superresolution via parameter-optimized particle swarm optimization. Math Probl Eng 373425
Moezi SA, Zakeri E, Zare A, Nedaei M (2015) On the application of modified cuckoo optimization algorithm to the crack detection problem of cantilever Euler–Bernoulli beam. Comput Struct 157:42–50
Wang GG, Gandomi AH, Alavi AH, Deb S (2016) A hybrid method based on krill herd and quantum-behaved particle swarm optimization. Neural Comput Appl 4(27):989–1006
Heidari AA, Abbaspour RA, Jordehi AR (2017) An efficient chaotic water cycle algorithm for optimization tasks. Neural Comput Appl 1(28):57–85
Faris H, Aljarah I, Azmi Al-Betar M, Mirjalili S (2017) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30:413–435
Aljarah I, Faris H, Mirjalili S, Al-Madi N (2018) Training radial basis function networks using biogeography-based optimizer. Neural Comput Appl 7(29):529–553
Wang GG, Gandomi AH, Alavi AH, Hao GS (2014) Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput Appl 2(25):297–308
Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 4(27):1053–1073
Alweshah M (2018) Construction biogeography-based optimization algorithm for solving classification problems. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3402-8
Liang JJ, Qu BY, Suganthan PN, Hernandez-Diaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Technical report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, January 2013
Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958
Wang Y, Liu ZZ, Li J et al (2016) Utilizing cumulative population distribution information in differential evolution. Appl Soft Comput 48:329–346
Civicioglu P, Besdok E (2018) A+ Evolutionary search algorithm and QR decomposition based rotation invariant crossover operator. Expert Syst Appl 103:49–62
https://www.mathworks.com/matlabcentral/fileexchange/68370-weighted-differential-evolution-algorithm-wde . Accessed 20 Feb 2018
Ghilani CD, Wolf PR (2006) Adjustment computations, spatial data analysis, Forth edn. Wiley, New Jersey
Yetkin M, Berber M (2014) Implementation of robust estimation in GPS networks using the Artificial Bee Colony algorithm. Earth Sci Inform 7:39–46. https://doi.org/10.1007/s12145-013-0131-5
Yetkin M (2018) Application of robust estimation in geodesy using the harmony search algorithm. J Spat Sci 63(1):63–73. https://doi.org/10.1080/14498596.2017.1341856
Derrac J, Garca S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18
Matsumoto M, Nishimura T (1998) Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans Model Comput Sci 8(1):3–30
Mezura-Montesa E, Coellob CAC (2011) Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm Evol Comput 1(4):173–194