GA-Based Hybrid Approach to Solve Fuzzy Multi-objective Optimization Model of Multi-application-Based COTS Selection Problem
Due to the quick growth of the modular software development, the commercial off-the-shelf (COTS) selection model of optimization technique becomes more popular in a component-based software system (CBSS). In order to realize the benefits of the COTS product, it is necessary to select the right products for various software systems. This paper proposed a genetic algorithm (GA)-based hybrid approach with fuzzy exponential membership function for best fit of COTS components. In this proposed approach, decision-maker (DM) is required to specify the different aspiration levels as per his/her preference to obtain an efficient allocation plan with different shape parameters in the exponential membership function. A real-world scenario of developing two financial applications for two small-scale industries is provided to represent the importance of the proposed algorithm with data set from a realistic situation.
KeywordsIntra-modular coupling density Multi-objective optimization Cohesion Coupling Genetic algorithm
- 1.Gupta, P., Verma, S., Mehlawat, M.K.: Optimization model of COTS selection based on cohesion and coupling for modular software systems under multiple applications environment. In: International Conference on Computational Science and Its Applications, 7335, pp. 87–102. Springer, Berlin, Heidelberg (2012)Google Scholar
- 5.Mohamed, A., Ruhe, G., Eberlein, A.: COTS selection: past, present, and future. In: 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (ECBS’07), IEEE, pp. 103–114 (2007)Google Scholar
- 6.Jayesh, D.M., Tailor, A.R.: Genetic algorithm based hybrid approach to solve uncertain multi-objective COTS selection problem for modular software system. J. Int. Fuzzy Syst. 34.4, 2103–2120 (2018)Google Scholar
- 7.Dhodiya, J.M., Tailor, A.R.: Genetic algorithm based hybrid approach to solve fuzzy multi-objective assignment problem using exponential membership function. SpringerPlus 5.1 2028, 1–29 (2016)Google Scholar