Enhanced Optimizer Algorithm and its Application to Software Testing

References

• Alzaqebah, A., Masadeh, R., & Hudaib, A. (2018). Whale optimization algorithm for requirements prioritization. 9th International Conference on Information and Communication Systems (ICICS). Jordan University of Science and Technology: IEEE.
   Google Scholar
• Arabloo, M., Bahadori, A., Ghiasi, M. M., Lee, M., Abbas, A., & Zendehboudi, S. (2015). A novel modeling approach to optimize oxygen–Steam ratios in coal gasification process. Fuel, 153, 1–5.
   Web of Science ®Google Scholar
• Benmessahel, I., Xie, K., & Chellal, M. (2018). A new evolutionary neural networks based on intrusion detection systems using multiverse optimization. Applied Intelligence, 48(8), 2315–2327.
   Web of Science ®Google Scholar
• Chekanin, V. A., & Chekanin, A. V. (2018). Design of library of metaheuristic algorithms for solving the problems of discrete optimization. In Chekanin, V. A., & Chekanin, A. V. (Eds.), Advances in mechanical engineering (pp. 25–32). Cham: Springer.
   Google Scholar
• Deng, W., Yao, R., Zhao, H., Yang, X., & Li, G. (2019). A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Computing, 23(7), 2445–2462.
   Web of Science ®Google Scholar
• Fellner, A., Krenn, W., Schlick, R., Tarrach, T., & Weissenbacher, G. (2019). Model-based, mutation-driven test-case generation via heuristic-guided branching search. ACM Transactions on Embedded Computing Systems (TECS), 18(1), 4.
   Web of Science ®Google Scholar
• Fu, W., Tan, J., Zhang, X., Chen, T., & Wang, K. (2019). Blind parameter identification of MAR model and mutation hybrid GWO-SCA optimized SVM for fault diagnosis of rotating machinery. Complexity, 2019, 1–17.
   PubMedGoogle Scholar
• Gholizadeh, S., Razavi, N., & Shojaei, E. (2018). Improved black hole and multiverse algorithms for discrete sizing optimization of planar structures. Engineering Optimization, 1–23.
   Web of Science ®Google Scholar
• Hudaib, A., & Fakhouri, H. N. (2018). Supernova optimizer: A novel natural inspired meta-heuristic. Modern Applied Science, 12(1), 32–50.
   Google Scholar
• Julian, M. (2018). A benchmark function for multi-objective optimization of competing concept alternatives. The Unsw Canberra at ADFA Journal of Undergraduate Engineering Research, 10, 2.
   Google Scholar
• Kun, W., & Yichen, W. (2016). Software test case generation based on the fault propagation path coverage. 2016 Annual Reliability and Maintainability Symposium (RAMS). IEEE.
   Google Scholar
• Liang, J. J., Suganthan, P. N., & Deb, K. (2005). Novel composition test functions for numerical global optimization. IEEE Swarm Intelligence Symposium (pp. 68–75). USA: Matlab codes of composition functions.
   Google Scholar
• Liu, Y., Yuzhu, H., & Cui, W. (2018). An improved SVM classifier based on multi-verse optimizer for fault diagnosis of autopilot. IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). China: IEEE.
   Google Scholar
• Mehne, S., Hashemi, H., & Mirjalili, S. (2020). Moth-flame optimization algorithm: Theory, literature review, and application in optimal nonlinear. Nature-inspired Optimizers: Theories, Literature Reviews and Applications, 811, 143.
   Google Scholar
• Mirjalili, S., Aljarah, I., Mafarja, M., Heidari, A. A., & Faris, H. (2020). Grey wolf optimizer: Theory, literature review, and application in computational fluid dynamics problems. In Nature-inspired optimizers (pp. 87–105). Cham: Springer.
   Google Scholar
• Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495–513.
   Web of Science ®Google Scholar
• Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61.
   Web of Science ®Google Scholar
• Pachauri, A., & Agarwal, K. (2008). Towards software test data generation using binary partical swarm optimization.
   Google Scholar
• Park, H., & Bychkov, V. (2018). “Pattern-based automated test data generation.” U.S. Patent Application 15/993,158, filed September 27.
   Google Scholar
• Qiao, S., Zhou, Y., Zhou, Y., & Wang, R. (2019). A simple water cycle algorithm with percolation operator for clustering analysis. Soft Computing, 23(12), 4081–4095.
   Web of Science ®Google Scholar
• Romli, R., & Yusoff, N. (2019). An analysis on the applicability of meta-heuristic searching techniques for automated test data generation in automatic programming assessment. Baghdad Science Journal, 16, 515–533.
   Web of Science ®Google Scholar
• Rostami, A., Arabloo, M., & Ebadi, H. (2017). Genetic programming (GP) approach for prediction of supercritical CO2 thermal conductivity. Chemical Engineering Research and Design, 122, 164–175.
   Web of Science ®Google Scholar
• Sulaiman, M. (2019). Optimal operation of the hybrid electricity generation system using multiverse optimization algorithm. Computational Intelligence and Neuroscience, 2019, 1–12.
   PubMed Web of Science ®Google Scholar
• Tangherloni, A., Spolaor, S., Cazzaniga, P., Besozzi, D., Rundo, L., Mauri, G., & Nobile, M. S. (2019). Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design. Applied Soft Computing, 81, 105494.
   Web of Science ®Google Scholar
• Varshney, S., & Mehrotra, M. (2019). A hybrid particle swarm optimization and differential evolution based test data generation algorithm for data-flow coverage using neighbourhood search strategy. Informatica, 42, 3.
   Web of Science ®Google Scholar
• Venkatakrishnan, G., Rengaraj, R., & Salivahanan, S. (2018). Grey wolf optimizer to real power dispatch with non-linear constraints. Computer Modeling in Engineering & Sciences, 115, 025–045.
   Web of Science ®Google Scholar
• Xi, J., Xue, Y., Xu, Y., & Shen, Y. (2013). Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols. Food Chemistry, 141(1), 320–326.
   PubMed Web of Science ®Google Scholar
• Yıldız, B. S., & Yıldız, A. R. (2018). Comparison of grey wolf, whale, water cycle, ant lion and sine-cosine algorithms for the optimization of a vehicle engine connecting rod. Materials Testing, 60(3), 311–315.
   Google Scholar
• Zaghoul, F. A. L., Rababah, O., & Fakhouri, H. (2014). Website search engine optimization: Geographical and cultural point of view. UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. Cambridge, UK: IEEE.
   Google Scholar
• Zhou, Y., Miao, F., & Luo, Q. (2019). Symbiotic organisms search algorithm for optimal evolutionary controller tuning of fractional fuzzy controllers. Applied Soft Computing, 77, 497–508.
   Web of Science ®Google Scholar
• Zhou, Y., Wang, R., Zhao, C., Luo, Q., & Metwally, M. A. (2017). Discrete greedy flower pollination algorithm for spherical traveling salesman problem. Neural Computing and Applications, 1–16.
   Web of Science ®Google Scholar