Jing Wang
- Ammann, Paul, and Jeff Offutt. Introduction to software testing. Cambridge University Press, 2016.Google Scholar
Cross Ref
- Williams, Nicky, "Pathcrawler: Automatic generation of path tests by combining static and dynamic analysis." European Dependable Computing Conference. Springer, Berlin, Heidelberg, 2005.Google Scholar
- Boyer, Robert S., Bernard Elspas, and Karl N. Levitt. "SELECT—a formal system for testing and debugging programs by symbolic execution." ACM SigPlan Notices 10.6 (1975): 234-245.Google ScholarDigital Library
- Miller, Webb, and David L. Spooner. "Automatic generation of floating-point test data." IEEE Transactions on Software Engineering 3 (1976): 223-226.Google ScholarDigital Library
- Yang, Xin-She. Nature-inspired metaheuristic algorithms. Luniver press, 2010.Google ScholarDigital Library
- Burkill, J. C. "The derivates of functions of intervals." Fundamenta Mathematicae 5.1 (1924): 321-327.Google ScholarCross Ref
- King, James C. "Symbolic execution and program testing." Communications of the ACM 19.7 (1976): 385-394.Google ScholarDigital Library
- Murty, Katta G. Linear programming. Chichester, 1983.Google Scholar
- Schwefel, Hans-Paul Paul. Evolution and optimum seeking: the sixth generation. John Wiley & Sons, Inc., 1993.Google ScholarDigital Library
- Gupta, Neelam, Aditya P. Mathur, and Mary Lou Soffa. "Automated test data generation using an iterative relaxation method." ACM SIGSOFT Software Engineering Notes 23.6 (1998): 231-244.Google ScholarDigital Library
- Wu, Weiwei. "Research of Automatic Test Case Generation Algorithm Based on Improved Particle Swarm Optimization." 2016 4th International Conference on Machinery, Materials and Computing Technology. Atlantis Press, 2016.Google Scholar
- Zhu, Xiao-mei, and Xian-feng Yang. "Software test data generation automatically based on improved adaptive particle swarm optimizer." 2010 International Conference on Computational and Information Sciences. IEEE, 2010.Google Scholar
- Bao, Xiaoan, "Path-oriented test cases generation based adaptive genetic algorithm." PloS one 12.11 (2017): e0187471.Google ScholarCross Ref
- Li, L. S., and Z. M. Guo. "Optimization Techniques Research on Testing Data Through Path Coverage with Chaotic Fruit Fly Algorithm." Journal of Chinese Computer Systems 39.2 (2018): 362-366.Google Scholar
- Singh, Mayank, "Automatic test data generation based on multi-objective ant lion optimization algorithm." 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech). IEEE, 2017.Google Scholar
- Houssein, Essam H., "Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems." Engineering Applications of Artificial Intelligence 94 (2020): 103731.Google ScholarCross Ref
- Hashim, Fatma A., "Henry gas solubility optimization: A novel physics-based algorithm." Future Generation Computer Systems 101 (2019): 646-667.Google ScholarDigital Library
- Whitley, Darrell. "A genetic algorithm tutorial." Statistics and computing 4.2 (1994): 65-85.Google ScholarCross Ref
- Gupta, Nirmal Kumar, and Mukesh Kumar Rohil. "Improving GA based automated test data generation technique for object oriented software." 2013 3rd IEEE International Advance Computing Conference (IACC). IEEE, 2013.Google Scholar
- Kennedy, James, and Russell Eberhart. "Particle swarm optimization." Proceedings of ICNN'95-International Conference on Neural Networks. Vol. 4. IEEE, 1995.Google Scholar
- Wang, Zhenzhen, and Qiaolian Liu. "A software test case automatic generation technology based on the modified particle swarm optimization algorithm." 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS). IEEE, 2018.Google Scholar
- Mirjalili, Seyedali, and Andrew Lewis. "The whale optimization algorithm." Advances in engineering software 95 (2016): 51-67.Google ScholarDigital Library
- Hussien, Abdelazim G., Essam H. Houssein, and Aboul Ella Hassanien. "A binary whale optimization algorithm with hyperbolic tangent fitness function for feature selection." 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2017.Google Scholar
- Hussien, Abdelazim G., "S-shaped binary whale optimization algorithm for feature selection." Recent trends in signal and image processing. Springer, Singapore, 2019. 79-87.Google Scholar
- Osama, Sarah, "An optimized support vector regression using whale optimization for long term wind speed forecasting." Series in machine perception and artificial intelligence, hybrid metaheuristics. World Scientific, 2018. 171-196.Google Scholar
- Houssein, Essam H., "Epileptic detection based on whale optimization enhanced support vector machine." Journal of Information and Optimization Sciences 40.3 (2019): 699-723.Google ScholarCross Ref
- Phatak, S. C., and S. Suresh Rao. "Logistic map: A possible random-number generator." Physical review E 51.4 (1995): 3670.Google ScholarCross Ref
- Bing, L. I., and J. I. A. N. G. Weisun. "Chaos optimization method and its application [J]." Control Theory & Applications 4 (1997).Google Scholar
- Mafarja, Majdi M., and Seyedali Mirjalili. "Hybrid whale optimization algorithm with simulated annealing for feature selection." Neurocomputing 260 (2017): 302-312.Google ScholarDigital Library
- Abd El Aziz, Mohamed, Ahmed A. Ewees, and Aboul Ella Hassanien. "Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation." Expert Systems with Applications 83 (2017): 242-256.Google ScholarDigital Library
- LI, Xin-De, "Optimal allocation of water resources based on Whale Optimization Algorithm." China Rural Water and Hydropower 4 (2018): 8.Google Scholar
- ben oualid Medani, Khaled, Samir Sayah, and Abdelghani Bekrar. "Whale optimization algorithm based optimal reactive power dispatch: A case study of the Algerian power system." Electric Power Systems Research 163 (2018): 696-705.Google ScholarCross Ref
- Trivedi, Indrajit N., "Novel adaptive whale optimization algorithm for global optimization." Indian Journal of Science and Technology 9.38 (2016): 319-26.Google ScholarCross Ref
- Zheng, Yuefeng, "A novel hybrid algorithm for feature selection based on whale optimization algorithm." IEEE Access 7 (2018): 14908-14923.Google ScholarCross Ref
- Sharawi, Marwa, Hossam M. Zawbaa, and Eid Emary. "Feature selection approach based on whale optimization algorithm." 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2017.Google Scholar
- Kaur, Gaganpreet, and Sankalap Arora. "Chaotic whale optimization algorithm." Journal of Computational Design and Engineering 5.3 (2018): 275-284.Google ScholarCross Ref
- Sahoo, Rashmi Rekha, and Mitrabinda Ray. "PSO based test case generation for critical path using improved combined fitness function." Journal of King Saud University-Computer and Information Sciences 32.4 (2020): 479-490.Google ScholarCross Ref
- Pachauri, Ankur, and Gursaran Srivastava. "Automated test data generation for branch testing using genetic algorithm: An improved approach using branch ordering, memory and elitism." Journal of Systems and Software 86.5 (2013): 1191-1208.Google ScholarDigital Library
- Korel, Bogdan. "Automated software test data generation." IEEE Transactions on software engineering 16.8 (1990): 870-879.Google ScholarDigital Library
- Y. Ling, Y. Zhou and Q. Luo, "Lévy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization," in IEEE Access, vol. 5, pp. 6168-6186, 2017, doi:10.1109/ACCESS.2017.2695Google ScholarCross Ref
Recommendations
An adaptive optimisation algorithm based on modified whale optimisation algorithm and Laplace crossover
Whale optimisation algorithm (WOA) is a new bio-inspired algorithm which mimics the hunting behaviour of humpback whale in nature. Standard WOA is easily trapped in local optima, provide slow convergence rate and lack of diversity, as the dimension of the ...
Bacterial foraging optimisation algorithm, particle swarm optimisation and genetic algorithm: a comparative study
Nature inspired meta-heuristic algorithms have been widely used in order to find efficient solutions for optimisation problems, and granted results have been achieved. Particle swarm optimisation PSO algorithm is one of the most utilised algorithms in ...
Hybrid whale optimisation and β-hill climbing algorithm for continuous optimisation problems
The whale optimisation algorithm (WOA) is an efficient optimisation algorithm inspired by the bubble-net hunting strategy of humpback whale. As any optimisation algorithm, WOA may prematurely converge to suboptimal solutions. This paper introduces a new ...
Comments