Multiobjective optimization is the process of simultaneously optimizing two or more conflicting objectives and is used for solving real-world optimization problems in various fields, from product design to process optimization. One of the most effective ways of solving problems with multiple objectives is to use multiobjective evolutionary algorithms (MOEAs). MOEAs draw inspiration from adaptat...

Bao Truong VARIATIONAL ANALYSIS AND APPLICATIONS TO MULTIOBJECTIVE OPTIMIZATION

Consider a multiobjective decision problem with uncertainty in the objective functions, given as set of scenarios. In single-criterion case, robust optimization methodology helps to identify solutions which remain feasible and good quality for all possible A well-known alternative method single-objective case is compare decisions under optimal benefit hindsight, i.e. minimize (possibly scaled) ...

Multiobjective Placement Optimization for High-performance Nanoscale Integrated Circuits

In recent years, the development of new algorithms for multiobjective optimization has considerably grown. A large number performance indicators been introduced to measure quality Pareto front approximations produced by these algorithms. this work, we propose a review total 63 partitioned into four groups according their properties: cardinality, convergence, distribution and spread. Application...

Different optimization models presented on literature show optimization schemes over MPLS or GMPLS networks. However, it has not been showed models that optimize one or more parameters integrating these two types of networks. In this paper a multiobjective scheme for multicast transmissions from a source node to a set of destination nodes in MPLS passing through a GMPLS optical backbone is show...

This paper presents a multiobjective fuzzy genetic algorithm optimization approach to design the submersible induction motor with two objective functions: the full load torque and the manufacturing cost. A multiobjective fuzzy optimization problem is formulated and solved using a genetic algorithm. The optimally designed motor is compared with an industrial motor having the same ratings. The re...

Multiobjective optimization strategy so-called Physical Programming allows controller designers a flexible way to express design preferences with a ’physical’ sense. For each objective (settling time, overshoot, disturbance rejection, etc.) preferences are established through categories as desirable, tolerable, unacceptable, etc. assigned to numerical ranges. The problem is translated into a un...

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...