Abstract
In this chapter, multi-objective reliability-cost optimization problems have been investigated by utilizing uncertain, vague and imprecise information. During the formulation, a reliability of each component of the system is represented in the form of the triangular interval. The conflicting nature of the objectives is resolved with the help of intuitionistic fuzzy programming technique by recognizing the linear, as well as non-linear membership functions. A crisp model is formulated by using a product aggregation operator to aggregate their expected values. The resultant problem is solved with a gravitational search algorithm (GSA) and compared their results with the particle swarm optimization (PSO) and genetic algorithm (GA). Results are validated through a statistical simulation of the t-test.
Keywords: Bi-objective optimization, Gravitational search algorithm, Intuitionistic fuzzy set, Membership functions, Particle swarm optimization, Reliability-cost optimization.
Related Journals
Related Books
Computational Intelligence For Data Analysis
Machine Learning and Its Application: A Quick Guide for Beginners
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
A First Course in Artificial Intelligence
Artificial Intelligence: Models, Algorithms and Applications
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
Handbook of Mobile Application Development: A Guide to Selecting the Right Engineering and Quality Features
Ad Hoc and Sensor Wireless Networks: Architectures, Algorithms and Protocols
Applications of Modern High Performance Networks
Arduino Meets Matlab: Interfacing, Programs and Simulink