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.
