This paper presents an improved multi-objective group search optimizer (IMGSO) that is based on Pareto theory that is designed to handle multi-objective optimization problems. The optimizer includes improvements in three areas: the transition-feasible region is used to address constraints, the Dealer’s Principle is used to construct the non-dominated set, and the producer is updated using a tab...

This paper presents a new approach to handle constrained optimization using evolutionary algorithms. The new technique converts constrained optimization to a two-objective optimization: one is the original objective function, the other is the degree function violating the constraints. By using Paretodominance in the multi-objective optimization, individual's Pareto strength is defined. Based on...

Evolutionary Algorithms have been applied to single and multiple objectives optimization problems, with a strong emphasis on problems, solved through numerical simulations. However in several engineering problems, there is limited availability of suitable models and there is need for optimization of realistic or experimental configurations. The multiobjective optimization of an experimental set...

Many decision making problems can be formulated as multi-objective optimization problems (MOP). There hardly exists the solution that optimizes all objective functions in MOP, and then the concept of Pareto optimal solution (or efficient solution) is introduced. Usually, there exist a lot of Pareto optimal solutions, which are considered as candidates of final decision making solution. It is an...

We propose a new approach to convex nonlinear multiobjective optimization that captures the geometry of the Pareto set by generating a discrete set of Pareto points optimally. We show that the problem of finding an optimal representation of the Pareto surface can be formulated as a mathematical program with complementarity constraints. The complementarity constraints arise from modeling the set...

In this paper, a bi-objective pharmaceutical supply chain network under uncertainty demand and transportation costs is modeled and developed. To control the uncertainty parameters, the robust optimization method is considering. The main objective of this paper determines the number and location of potential facilities such as drug manufacture centers and drug distribution centers by considering...

Bi-objective optimization of the availability allocation problem in a series–parallel system with repairable components is aimed in this paper. The two objectives of the problem are the availability of the system and the total cost of the system. Regarding the previous studies in series–parallel systems, the main contribution of this study is to expand the redundancy allocation problems to syst...

robust control design of vehicles addresses the effect of uncertainties on the vehicle’s performance. in present study, the robust optimal multi-objective controller design on a non-linear full vehicle dynamic model with 8-degrees of freedom having parameter with probabilistic uncertainty considering two simultaneous conflicting objective functions has been made to prevent the rollover. the obj...

In the present paper, a bicriterial optimal control problem governed by an abstract evolution problem and bilateral control constraints is considered. To compute Pareto optimal points and the Pareto front numerically, the (Euclidean) reference point method is applied, where many scalar constrained optimization problems have to be solved. For this reason a reduced-order approach based on proper ...