abstract. when a production facility is designed, there are various parameters affecting the number machines such as production capacity and reliability. it is often a tedious task to optimize different objectives simultaneously. the other issue is the uncertainty in many design parameters which makes it difficult to reach a desirable solution. in this paper, we present a new mathematical model...

This paper details two problems that employ Genetic-based Multi-objective Optimization algorithms on dynamic systems. The first is a simple, single input system and the second is a complex two input system. First, the two systems are detailed with the derivation of their models. From these models, an optimization algorithm based on NSGA-II is run, constructing the Pareto Frontiers. The results ...

In this project, we consider minimizing multiple linear objective functions under a max-t-norm fuzzy relational equation constraint. Since the feasible domain of a max-Archimedean t-norm relational equation constraint is generally nonconvex, traditionally mathematical programming techiniques may have difficulty in yielding efficient solutions for such problems. In this paper, we apply the two-p...

despite the growing use of evolutionary multi-objective optimization algorithms in different categories of science, these algorithms as a powerful tool in portfolio optimization and specially solving multi-objective portfolio optimization problem is still in its early stages. in this paper, moeas have been used for solving multi-objective portfolio optimization problem in tehran stock market. f...

this paper proposes a method to solve multi-objective problems using improved particle swarm optimization. we propose leader particles which guide other particles inside the problem domain. two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. the first one is based on the mean of the m optimal particles and the second one is based on appoin...