In Guided Evolutionary Multi-objective Optimization the goal is to find a diverse, but locally focused non-dominated front in a decision maker’s area of interest, as close as possible to the true Paretofront. The optimization can focus its efforts towards the preferred area and achieve a better result [9, 17, 7, 13]. The modeled and simulated systems are often stochastic and a common method to ...

A fractional order (FO) PID or FOPID controller is designed for an Automatic Voltage Regulator (AVR) system with the consideration of contradictory performance objectives. An improved evolutionary Nondominated Sorting Genetic Algorithm (NSGA-II), augmented with a chaotic Henon map is used for the multiobjective optimization based design procedure. The Henon map as the random number generator ou...

In this article we build multi-objective hyperheuristics (MOHHs) using the multi-objective evolutionary algorithm NSGA-II for solving irregular 2D cutting stock problems under a bi-objective minimization schema, having a trade-off between the number of sheets used to fit a finite number of pieces and the time required to perform the placement of these pieces. We solve this problem using a multi...

This paper describes a scalable algorithm for solving multiobjective decomposable problems by combining the hierarchical Bayesian optimization algorithm (hBOA) with the nondominated sorting genetic algorithm (NSGA-II) and clustering in the objective space. It is first argued that for good scalability, clustering or some other form of niching in the objective space is necessary and the size of e...

The present-day evolutionary multi-objective optimization (EMO) algorithms had a demonstrated history of evolution over the years. The initial EMO methodologies involved additional niching parameters which made them somewhat subjective to the user. Fortunately, soon enough parameter-less EMO methodologies have been suggested thereby making the earlier EMO algorithms unpopular and obsolete. In t...

A new multi-objective evolutionary algorithm which can be applied to many nonlinear multi-objective optimization problems is proposed. Its aim is to obtain a discrete fixed size set approximating the complete Pareto-front quickly. It adapts ideas from different multiand single-objective optimization evolutionary algorithms, although it also incorporates new devices, namely, a new method to impr...

Tailored prosthesis design must to be done in as accurately as possible way to fit particular patient requirements. Aiming this intent, some techniques based on image processing have been being studied. In this context, the image quality is highly important and decisive in the accuracy, or even feasibility, of these methodologies. To do so, the image treatment is done through the gamma correcti...

Bi-objective portfolio optimization for minimizing risk and maximizing expected return has received considerable attention using evolutionary algorithms. Although the problem is a quadratic programming (QP) problem, the practicalities of investment often make the decision variables discontinuous and introduce other complexities. In such circumstances, usual QP solution methodologies can not alw...

We are interested in this paper in solving a multiobjective hybrid flowshop scheduling problem (HFS). The problem has different parameters and constraints such as release dates, due dates and sequence dependent setup times. Two different objectives should be optimized at once: the makespan and the total tardiness to be minimized. To solve the problem, we have developed two versions of a new dec...

This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, t...