Moleküler Potansiyel Enerji Fonksiyonu İçin Geliştirilmiş Sosyal Örümcek Algoritması

Year 2020, Volume: 8 Issue: 3, 618 - 642, 03.09.2020

Emine Baş Erkan Ülker

https://doi.org/10.36306/konjes.788082

Cited By: 1

Abstract

Sosyal örümcek algoritması (SÖA), sürekli optimizasyon problemlerini çözmek için örümcek davranışları üzerine oluşturulan yeni bir sezgisel algoritmadır. Bu çalışmada, SÖA molekülün enerji fonksiyonunun basitleştirilmiş bir modelini en aza indirmek için kullanılmıştır. Moleküler potansiyel enerji fonksiyonu problemi, en önemli gerçek hayat problemlerinden biridir. Moleküler potansiyel enerji fonksiyonu problemi, bir proteinin 3D yapısını tahmin etmeye çalışır. Sosyal örümcek algoritması çeşitli teknikler (Çaprazlama-mutasyon ve Gbest yakınsaması-sessiz örümcek teknikleri) eklenerek geliştirilmiştir ve çeşitli tekniklerle geliştirilen SÖA 'ya Geliştirilmiş SSA (GSÖA) denilmiştir. Bu teknikler sayesinde, SÖA 'nın sürekli arama uzayında keşif ve sömürü yetenekleri geliştirilmiştir. SÖA ve GSÖA 'nın genel performansları, düşük ölçekli ve yüksek ölçekli on üç kıyaslama fonksiyonunda test edilmiştir ve elde edilen sonuçlar birbiriyle karşılaştırılmıştır. Wilcoxon işaretli testi, elde edilen SÖA ve GSÖA sonuçlarına uygulanmıştır. Daha sonra, SÖA ve GSÖA'nın genel performansı, farklı boyutlarda tanımlanan molekülün basitleştirilmiş bir modeli üzerinde test edilmiştir. Ayrıca, GSÖA'nın performansı, literatürdeki çeşitli sanatsal algoritmalarla da karşılaştırılmıştır. Sonuçlar, GSÖA 'nın performansının üstünlüğünü göstermiştir.

Keywords

Sınırlı Optimizasyon, Sosyal örümcek algoritması, Moleküler enerji fonksiyonu

IMPROVED SOCIAL SPIDER ALGORITHM FOR MINIMIZING MOLECULAR POTENTIAL ENERGY FUNCTION

Abstract

The social spider algorithm (SSA) is a new heuristic algorithm created on spider behaviors to solve continuous optimization problems. In this study, SSA is used in order to minimize a simplified model of the energy function of the molecule. The Molecular potential energy function problem is one of the most important real-life problems. The Molecular potential energy function problem attempts to predict the 3D structure of a protein. SSA is developed by various techniques (Crossover-mutation and Gbest convergence-silent spider techniques) and SSA is called Improved SSA (ISSA). By these techniques, the exploration and exploitation capabilities of SSA in the continuous search space are improved. The general performances of SSA and ISSA are tested on low-scaled and large-scaled thirteen benchmark functions and obtained results are compared with each other. Wilcoxon signed-rank test is applied to SSA and ISSA results. Then, the general performance of the SSA and ISSA is tested on a simplified model of the molecule for different dimensions. Also, the performance of the ISSA is compared to various state-of-art algorithms in the literature. The results showed the superiority of the performance of ISSA.

Keywords

Constrained Optimization, Social spider algorithm, Molecular energy function

References

• Acılar A.M., (2013), Yapay Bağışıklık Algoritmaları Kullanılarak Bulanık Sistem Tasarımı, Konya,Turkey, (Ph.D. thesis).
• Bansal J.C., Deep Shashi K., Katiyar V.K., (2010), Minimization of molecular potential energy function using particle swarm optimization. Int J Appl Math Mech 6(9):1–9.
• Barbosa H.J.C., Lavor C., Raupp F.M., (2005), A GA-simplex hybrid algorithm for global minimization of a molecular potential energy function. Ann Oper Res 138:189–202.
• Cuevas E., Cienfuegos M., Zaldívar D., Pérez-Cisneros M., (2013), A swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications 40, 6374–6384.
• Deep K., Barak S., Katiyar V.K., Nagar A.K., (2012), Minimization of molecular potential energy function using newly developed real coded genetic algorithms. Int J Optim Control Theor Appl (IJOCTA) 2(1):51–58.
• Deep K., Thakur M., (2007a), A new mutation operator for real coded genetic algorithms. Appl Math Comput 193(1):211–230.
• Deep K., Thakur M., (2007b), A new crossover operator for real coded genetic algorithms. Appl Math Comput 188(1):895–912.
• Dra˘zi´c M., Lavor C., Maculan N., Mladenovi´c N., (2008), A continuous variable neighborhood search heuristic for finding the three-dimensional structure of a molecule. Eur JOper Res 185:1265–1273.
• El-bages M.S., Elsayed W.T., (2017), Social spider algorithm for solving the transmission expansion planning problem, Electric Power Systems Research 143, 235–243.
• Elsayed W.T., Hegazy Y.G., Bendary F.M., El-bages M.S., (2016), Modified social spider algorithm for solving the economic dispatch Problem, Engineering Science and Technology, an International Journal 19, 1672–1681.
• Grimaccia Gandelli F., Mussetta M., Pirinoli P., Zich R.E., (2007), Development and validation of different hybridization strategies between GA and PSO. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp 2782–2787.
• Grimaldi E.A., Grimacia F., Mussetta M., Pirinoli P., Zich R.E., (2004), A new hybrid genetical swarm algorithm for electromagnetic optimization, In Proceedings of the international conference on computational electromagnetics and its applications, Beijing, China, pp 157–160.
• Hedar A., Ali A.F., Hassan T., (2011), Genetic algorithm and tabu search-based methods for molecular 3D-structure prediction. Numer Algebra Control Optim 1(1):191–209. Holland J.H., (1975), Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
• Jian M., Chen Y., (2006), Introducing recombination with dynamic linkage discovery to particle swarm optimization. In: Proceedings of the genetic and evolutionary computation conference, pp 85–86.
• Juang C.F., (2004), A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern Part B Cybern 34:997–1006. Kennedy J., Eberhart R., (1995), Particle swarm optimization, in Proc. IEEE Int. Conf.Neural Networks, Perth, WA, pp. 1942–1948.
• Krink T., Lvbjerg M., (2002), The lifecycle model: combining particle swarm optimization, genetic algorithms, and hill climbers. In: Proceedings of the parallel problem solving from nature, pp 621-630.
• Lavor C., Maculan N., (2004), A function to test methods applied to global minimization of the potential energy of molecules. Numer Algorithms 35:287–300.
• Mallipeddi R., Mallipeddi S., Suganthan P.N., Tasgetiren M.F., (2011), Differential evolution algorithm with an ensemble of parameters and mutation strategies, Appl.Soft Comput. 11 1679–1696.
• Mousa A., Bentahar J., ( 2016 ), An Efficient QoS-aware Web Services Selection using Social Spider Algorithm, The 13th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2016), Procedia Computer Science 94, 176 – 182.
• Pardalos P.M., Shalloway D., Xue G.L., (1994), Optimization methods for computing global minima of the nonconvex potential energy function. J Glob Optim 4:117–133. Parpinelli R.S., Lopes H.S., (2011), New inspirations in swarm intelligence: a survey, Int.J. Bio-Inspired Comput. 3 (1) 1–16.
• Robinson J., Sinton S., Samii Y.R., (2002), Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna. In: Proceedings of the IEEE international symposium in Antennas and Propagation Society, pp 314–317.
• Settles M., Soule T., (2005), Breeding swarms: a GA/PSO hybrid. In: Proceedings of Genetic and Evolutionary Computation Conference, pp 161–168.
• Surjanovic S., Bingham D., (2017), Virtual library of simulation experiments: test functions and datasets, http://www.sfu.ca/ ∼ssurjano.
• Talbi E.G., (2009), Metaheuristics: From Design to Implementation, Wiley.
• Tawhid M.A., Ali A.F., (2016), A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems, Ain Shams Engineering Journal., xx, xxx-xxx.
• Tawhid M.A., Ali A.F., (2017a), A hybrid social spider optimization and genetic algorithm for minimizing molecular potential energy function, Soft Computing, 21:6499–6514 DOI 10.1007/s00500-016-2208-9.
• Tawhid M.A., Ali A.F., (2017b), A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function, Memetic Comp., 9:347–359.
• Tawhid M.A., Ali A.F., (2017c), A Hybrid Flower Pollination and Genetic Algorithm for Minimizing the Non-Convex Potential Energy of Molecular Structure, Trends Artif. Intell., 1(1):12-21.
• Wales D.J., Scheraga H.A., (1999), Global optimization of clusters, crystals, and biomolecules. Science 285:1368–1372.
• Yu J.Q.J., Li O.K.V., (2015), A social spider algorithm for global optimization, Applied Soft Computing 30 614–627.
• Yu J. J. Q., Li V. O. K., (2016), A social spider algorithm for solving the non-convex economic load dispatch problem, Neurocomputing, 171(C), 955–965.