Analysis, Test and Management of the Meta-Heuristic Searching Process: An Experimental Study on SOS

Year 2020, Volume: 23 Issue: 2, 445 - 455, 01.06.2020

Hamdi Tolga Kahraman , Sefa Aras Yusuf Sönmez , Uğur Güvenç , Eyüp Gedikli

https://doi.org/10.2339/politeknik.548717

Abstract

In a search
process, getting trapped in a local minimum or jumping the global minimum
problems are also one of the biggest problems of meta-heuristic algorithms as
in artificial intelligence methods. In this paper, causes of these problems are
investigated and novel solution methods are developed. For this purpose, a
novel framework has been developed to test and analyze the meta-heuristic
algorithms. Additionally, analysis and test studies have been carried out for
Symbiotic Organisms Search (SOS) Algorithm. The aim of the study is to measure
the mimicking a natural ecosystem success of symbiotic operators. Thus,
problems in the search process have been discovered and operators' design
mistakes have been revealed as a case study of the developed testing and
analyzing method. Moreover, ways of realizing a precise neighborhood search
(intensification) and getting rid of the local minimum (increasing
diversification) have been explored. Important information that enhances the
performance of operators in the search process has been achieved through
experimental studies. Additionally, it is expected that the new experimental
test methods developed and presented in this paper contributes to
meta-heuristic algorithms studies for designing and testing.

Keywords

Symbiotic organisms search, benchmark problem, algorithm analyze, intensification, diversification

References

• [1] Blum C. and Roli A., “Metaheuristics in combinatorial optimization: Overview and conceptual comparison”, ACM Computing Surveys, 35(3), 268-308, (2003).[2] Yang X.S., Scholarpedia, 6(8):1147, (2011).[3] Holland J.H., “Adaption in Natural and Artificial Systems”, University of Michigan Pres, Ann Arbor, MI, USA, (1975).[4] Goldberg D. E., “Genetic Algorithms in Search, Optimization, and Machine Learning”, Reading, MA: Addison-Wesley, ISBN 0201157675, (1989).[5] Dorigo M., “Optimization, Learning and Natural Algorithms”, PhD thesis, Politecnico di Milano, (1992).[6] Dorigo M., Maniezzo V., and Colorni A., “Ant System: Optimization by a colony of cooperating agents”, IEEE Trans Syst Man Cybernet Part B, 26(1):29–41, (1996).[7] Dorigo M. and Stützle T., “Ant Colony optimization”, MA: MIT Press, Cambridge, (2004).[8] Eberhart R. C. and Kennedy J., “A new optimizer using particle swarm theory”, Proceedings of the sixth international symposium on micro machine and human science, Piscataway, NJ, Nagoya, Japan, 39-43, (1995).[9] Eberhart R. C. and Shi Y., “Particle swarm optimization: developments, applications and resources”, Proc. congress on evolutionary computation, Piscataway, NJ., Seoul, Korea., 81-86, (2001).[10] Rajabioun R., “Cuckoo Optimization Algorithm”, Applied Soft Computing, 11:5508-5518, (2011).[11] Karaboga D., and Basturk B., “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm” Journal of Global Optimization, 39:459–471, (2007).[12] Karaboga D., and Ozturk C., “A novel clustering approach: Artificial Bee Colony (ABC) algorithm”, Applied Soft Computing, 11(1): 652-657, (2011).[13] Rashedi E., Nezamabadi-pour H., and Saryazdi S., “GSA: A Gravitational Search Algorithm”, Information Sciences, 179: 2232–2248, (2009).[14] Kahraman H. T., Sagiroglu S., and Colak I., “The development of intuitive knowledge classifier and the modeling of domain dependent data”, Knowledge Based Systems, 37: 283-295, (2013).[15] Rao R.V., Savsani V.J, and Vakharia D.P., “Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems”, Computer-Aided Design, 43: 303–315, (2011).[16] Cheng M.Y., and Prayogo D., “Symbiotic Organisms Search: A new metaheuristic optimization algorithm”, Computers and Structures, 139: 98–112, (2014).[17] Trana D.H., Cheng M.Y., and Prayogo D., “A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time–cost–labor utilization tradeoff problem”, Knowledge-Based Systems, 94: 132–145, (2016).[18] Meng A., Li, Z., Yin, H., Chen, S., and Guo, Z., “Accelerating particle swarm optimization using crisscross search”, Information Sciences, 329: 52–72, (2016).[19] Wang Z., Xing H., Li T., Yang Y., Qu R., and Pan, Y., “A Modified Ant Colony Optimization Algorithm for Network Coding Resource Minimization”, IEEE Transactions on Evolutionary Computation, 20(3): 325-342, (2016).[20] Lin Q., Chen J., Zhan Z.H., Chen W.N., Coello C.A.C., Yin Y., Lin C.M., and Zhang J., “A Hybrid Evolutionary Immune Algorithm for Multiobjective Optimization Problems”, IEEE Transactions on Evolutionary Computation, 20(5): 711-729, (2016).[21] Seçkiner S.U., Eroğlu Y., Emrullah M., and Dereli T., “Ant colony optimization for continuous functions by using novel pheromone updating”, Applied Mathematics and Computation, 219(9): 4163-4175, (2013).[22] Civicioglu P., “Backtracking search optimization algorithm for numerical optimization problems”, Applied Mathematics and Computation, 219(15): 8121-8144, (2013).[23] Topal A.O., and Altun O., “A novel meta-heuristic algorithm: Dynamic Virtual Bats Algorithm”, Information Sciences, 354: 222-235, (2016).[24] Baykasoğlu A., and Akpinar Ş., “Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems–Part 1: Unconstrained optimization”, Applied Soft Computing, 56: 520-540, (2017).[25] Özkış A., and Babalık A., “A novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithm”, Information Sciences, 402: 124-148, (2017).[26] Babalik A., Ozkis A., Uymaz S.A., and Kiran, M.S., “A multi-objective artificial algae algorithm”, Applied Soft Computing, 68: 377-395, (2018).[27] Aydilek İ.B., “A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems”, Applied Soft Computing, 66: 232-249, (2018).[28] Melki G., Kecman V., Ventura S., and Cano A., “OLLAWV: OnLine Learning Algorithm using Worst-Violators”, Applied Soft Computing, 66: 384-393, (2018).[29] Zhang J., Xiao M., Gao L., and Pan Q., “Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems”, Applied Mathematical Modelling, 63: 464-490, (2018).[30] Trunfio G.A., Topa P., Was J., “A new algorithm for adapting the configuration of subcomponents in large-scale optimization with cooperative coevolution”, Information Sciences, 372: 773–795, (2016).[31] Karafotias G., Hoogendoorn M., and Eiben A.E., “Parameter Control in Evolutionary Algorithms: Trends and Challenges”, IEEE Transactions on Evolutionary Computation, 19(2): 167-187, (2015).[32] Sun G., Zhang A., Yao Y., and Wang Z., “A novel hybrid algorithm of gravitational search algorithm with genetic algorithm for multi-level thresholding”, Applied Soft Computing, 46: 703–730, (2016).