Software requirements optimization using multi-objective quantum-inspired hybrid differential evolution

Advances in Intelligent Systems and Computing

Volumn 175 ADVANCES, Issue , 2013, Pages 107-120

  Charan Kumari, A ^a   Srinivas, K ^a   Gupta, M P ^b  

^a DAYALBAGH EDUCATIONAL INSTITUTE   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

Author keywords

Multi objective next release problem; Multi objective optimization; Multi objective Quantum inspired Hybrid Differential Evolution; Search based software engineering

Indexed keywords

BENCHMARKING; CUSTOMER SATISFACTION; GENETIC ALGORITHMS; PARETO PRINCIPLE; QUANTUM COMPUTERS; REQUIREMENTS ENGINEERING; SALES; SOFTWARE ENGINEERING;

CONFLICTING OBJECTIVES; DIFFERENTIAL EVOLUTION; HYBRID DIFFERENTIAL EVOLUTION; MANUFACTURING COMPANIES; MULTI-OBJECTIVE NEXT RELEASE PROBLEMS; OPTIMIZATION TECHNIQUES; SEARCH-BASED SOFTWARE ENGINEERING; SOFTWARE REQUIREMENTS;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84872590379     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-31519-0_7     Document Type: Article

References (11)

1. Bagnall, A., Rayward-Smith, V., Whittley, I.: The next release problem. Information and software technology 43(14), 883-890 (2001 (Pubitemid 33050968)
2. Zhang, Y., Harman, M., Mansouri, A.S.: The Multi-Objective Next Release Problem. In: GECCO 2007: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1129-1136. ACM, New York (2007
3. Durillo, J.J., Zhang, Y., Alba, E., Nebro, A.J.: A study of the multi-objective next release problem. In: SSBSE 2009: Proceedings of the 2009 1st International Symposium on Search Based Software Engineering, pp. 49-58. IEEE Computer Society, Washington (2009
4. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2001
5. Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for solving Multi-Objective Problems. Springer (2007
6. Price, K., Storn, R.: Differential Evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report, International Computer Science Institute, Berkley (1995
7. Han, K.H., Kim, J.H.: Quantum-inspired Evolutionary Algorithm for a Class of Combinatorial Optimization. IEEE Transactions on Evolutionary Computation 6(6), 580-593 (2002
8. Defoin,M., Schliebs, S., Kasabov, N.: A Versatile Quantum-inspired Evolutionary Algorithm. In: The IEEE Congress on Evolutionary Computation (CEC 2007), pp. 423-430 (2007 (Pubitemid 351371430)
9. Su, H., Yang, Y.: Quantum-inspired differential evolution for binary optimization. In: The 4th International Conference on Natural Computation, pp. 341-346 (2008
10. Deb, K., Pratap, A., Agarwal, S.,Meyarivan, T.: A Fast and ElitistMultiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182-197 (2002 (Pubitemid 34555372)
11. Zitzler, E., Thiele, L.:Multiobjective Evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257-271 (1999 (Pubitemid 30544879)