Optimization under Fuzzy Constraints: From a Heuristic Algorithm to an Algorithm That Always Converges

Authors

Vladik Kreinovich, The University of Texas at El PasoFollow
Juan Carlos Figueroa-Garcia, Universidad Distrital, Bogotá, D.CFollow

Publication Date

7-2018

Comments

Technical Report: UTEP-CS-18-52

Kreinovich V., Figueroa-García J.C. (2018) Optimization Under Fuzzy Constraints: From a Heuristic Algorithm to an Algorithm that Always Converges. In: Figueroa-García J., López-Santana E., Rodriguez-Molano J. (eds) Applied Computer Sciences in Engineering. WEA 2018. Communications in Computer and Information Science, vol 915. Springer, Cham
https://doi.org/10.1007/978-3-030-00350-0_1

Abstract

An efficient iterative heuristic algorithm has been used to implement Bellman-Zadeh solution to the problem of optimization under fuzzy constraints. In this paper, we analyze this algorithm, explain why it works, show that there are cases when this algorithm does not converge, and propose a modification that always converges.