Finding minimum-weight spanning trees (MST) in graphs is a classic problem in operations research with important applications in network design. The basic MST problem can be solved eeciently, but the degree constrained and multiobjective versions are NP-hard. Current approaches to the degree-constrained single objective MST include Raidl's evolutionary algorithm (EA) which employs a direct tree...

The traffic signal design problem is a typical multiobjective optimization problem. In this research, a simulation-based multiobjective evolutionary approach for traffic signal design problems is proposed. This approach has a robust advantage that the numerical simulators and the optimization solvers can be changed and used easily for other applications. In this research, it combined the NSGA-I...

We study resource-constrained project scheduling problems with perturbation on activity durations. With the consideration of robustness and stability of a schedule, we model the problem as a multiobjective optimization problem. Three objectives—makespan minimization, robustness maximization, and stability maximization—are simultaneously considered. We propose a hybrid multiobjective evolutionar...

In this paper we present an approach to design robust controllers. The control design problem is formulated as a multiobjective optimization problem and solved by GAs.

In this paper, the preliminary design of a space mission is approached by introducing uncertainties on the design parameters and formulating the resulting reliable design problem as a multiobjective optimization problem. Uncertainties are modelled through evidence theory and the belief, or credibility, that the successful achievement of mission goals is maximized along with the reliability of c...

In the real-world optimization problems, coefficients of the objective function are not known precisely and can be interpreted as fuzzy numbers. In this paper we define the concepts of optimality for linear programming problems with fuzzy parameters (FLP). Then by using the concept of comparison of fuzzy numbers we transform FLP problem into a multiobjective linear programming (MOLP) problem. T...

This paper proposes a new perspective for solving systems of nonlinear equations. A system of equations can be viewed as a multiobjective optimization problem: every equation represents an objective function whose goal is to minimize difference between the right and left term of the corresponding equation in the system. We used an evolutionary computation technique to solve the problem obtained...

This paper presents a systematic comparative study of CMEA (constraint method-based multiobjective evolutionary algorithm) with several other commonly reported mulitobjective evolutionary algorithms (MOEAs) in solving a three-objective optimization problem. The best estimate of the noninferior space was also obtained by solving this multiobjective (MO) problem using a binary linear programming ...