Abstract
To date, there are much increasing trends on adopting parameter free meta-heuristic algorithms for solving general optimization problems. With parameter free algorithms, there are no parameter controls for tuning. As such, the adoption of parameter free meta-heuristic algorithms is often straightforward. On the negative note, exploration (i.e. roaming the search space thoroughly) and exploitation (i.e. manipulating the current known best neighbor) are pre-set. As the search spaces are problem dependent, any pre-set exploration and exploitation can lead to entrapment in local optima. In this paper, we investigate the use of Lévy flight to enhance the exploration of a parameter free meta-heuristic algorithm, called Modified Symbiotic Organism Search Algorithm (MSOS), via its population initialization. Our experimentations involving the software module clustering problems have been encouraging, as MSOS gives competitive results against existing selected parameter free meta-heuristic algorithms. For all the given module clustering problems, MSOS generates overall best mean results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lucca GAD, Fasolino AR, Pace F, Tramontana P, Carlini UD (2002) Comprehending web applications by a clustering-based approach. In: 10th international workshop on program comprehension. IEEE, Paris, pp 261–270
Jahnke JH (2004) Reverse engineering software architecture using rough clusters. In: IEEE annual meeting of the fuzzy information processing. Alberta, pp 4–9
Sommerville I (2015) Software engineering, 10th edn. Pearson, Harlow
Mitchell BS, Mancoridis S (2006) On the automatic modularization of software systems using the bunch tool. IEEE Trans Soft Eng 32(3):193–208
Mahdavi K, Harman M, Hierons RM (2003) A multiple hill climbing approach to software module clustering. In: International conference on software maintenance. Amsterdam, pp 315–324
Kumari AC, Srinivas K (2016) Hyper-heuristic approach for multi-objective software module clustering. J Syst Softw 117:384–401
Praditwong K, Harman M, Yao X (2011) Software module clustering as a multi-objective search problem. IEEE Trans Soft Eng 37(2):264–282
Huang J, Liu J, Yao X (2017) A multi-agent evolutionary algorithm for software module clustering problems. Soft Comput 21(12):3415–3428
Zamli KZ (2018) Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization. In: International conference on information and communications technology. IEEE, Yogyakarta, pp 67–71
Zamli KZ, Din F, Ramli N, Ahmed BS (2019) Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm. arXiv:1902.11159
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82
Alsewari ARA, Zamli KZ (2012) A harmony search based pairwise sampling strategy for combinatorial testing. Int J Phys Sci 7(7):1062–1072
Din F, Alsewari ARA, Zamli KZ (2017) A parameter free choice function based hyper-heuristic strategy for pairwise test generation. In: IEEE international conference on software quality, reliability and security companion. IEEE, Prague, pp 85–91
Din F, Zamli KZ (2018) Fuzzy adaptive teaching learning-based optimization strategy for gui functional test cases generation. In: 7th international conference on software and computer applications. ACM, Kuantan, pp 92–96
Nasser AB, Zamli KZ, Alsewari ARA, Ahmed BS (2018) Hybrid flower pollination algorithm strategies for t-way test suite generation. PLoS ONE 13(5):e0195187
Younis MI, Zamli KZ, Isa NAM (2008) MIPOG-modification of the IPOG strategy for t-way software testing. In: Distributed frameworks and applications. IEEE, Penang, pp 1–6
Younis MI, Zamli KZ, Isa NAM (2008) Algebraic strategy to generate pairwise test set for prime number parameters and variables. In: International symposium on information technology. IEEE, Kuala Lumpur, pp 1–4
Zamli KZ, Alkazemi BY, Kendall G (2016) A tabu search hyper-heuristic strategy for t-way test suite generation. Appl Soft Comput 44:57–74
Zamli KZ, Din F, Ahmed BS, Bures M (2018) A hybrid q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem. PLoS ONE 13(5):e0195675
Zamli KZ, Din F, Kendall G, Ahmed BS (2017) An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation. Inf Sci 399:121–153
Ahmed BS, Gambardella LM, Afzal W, Zamli KZ (2017) Handling constraints in combinatorial interaction testing in the presence of multi objective particle swarm and multithreading. Inf Softw Technol 86:20–36
Ahmed BS, Sahib MA, Gambardella LM, Afzal W, Zamli KZ (2016) Optimum design of PIλDΜ controller for an automatic voltage regulator system using combinatorial test design. PLoS ONE 11(11):e0166150
Alsewari ARA, Zamli KZ (2011) Interaction test data generation using harmony search algorithm. In: IEEE symposium on industrial electronics and applications. IEEE, Langkawi, pp 559–564
Din F, Zamli KZ (2018) Hyper-heuristic-based strategy for pairwise test case generation. Adv Sci Lett 24(10):7333–7338
Nasser AB, Alsewari ARA, Tairan NM, Zamli KZ (2017) Pairwise test data generation based on flower pollination algorithm. Malay J Comput Sci 30(3):242–257
Nasser AB, Zamli KZ, Alsewari ARA, Ahmed BS (2018) An elitist-flower pollination-based strategy for constructing sequence and sequence-less t-way test suite. Int J Bio-Inspired Comput 12(2):115–127
Ahmed BS, Zamli KZ, Afzal W, Bures M (2017) Constrained interaction testing: a systematic literature study. IEEE Access 5
Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112
Pavlyukevich I (2007) Lévy flights, non-local search and simulated annealing. J Comput Phys 226(2):1830–1844
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-Learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–313
Rao RV (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–24
Acknowledgements
The work reported in this paper is funded by “Fundamental Research Grant from Ministry of Higher Education Malaysia titled: A Reinforcement Learning Sine Cosine based Strategy for Combinatorial Test Suite Generation (grant no: RDU170103)”. We thank MOHE for the support.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zainal, N.A., Zamli, K.Z., Din, F. (2020). A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem. In: Kasruddin Nasir, A.N., et al. InECCE2019. Lecture Notes in Electrical Engineering, vol 632. Springer, Singapore. https://doi.org/10.1007/978-981-15-2317-5_19
Download citation
DOI: https://doi.org/10.1007/978-981-15-2317-5_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2316-8
Online ISBN: 978-981-15-2317-5
eBook Packages: EngineeringEngineering (R0)