This paper considers the allocation of maximum reliability to a complex system, while minimizing the cost of the system, a type of multi-objective optimization problem (MOOP). Multi-objective Evolutionary Algorithms (MOEAs) have been shown in the last few years as powerful techniques to solve MOOP .This paper successfully applies a Nondominated sorting genetic algorithm (NSGA-II) technique to o...

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 ...

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...

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...

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...

In this paper, we propose a multi-objective evolutionary metaheuristic approach based on the Pareto Ant Colony Optimization (P-ACO) and non-dominated genetic sorting algorithms (NSGA II NSGA III) to solve bi-objective portfolio optimization problem. P-ACO is used select best assets composing efficient portfolio. Then, III are separately find proportional weights of budget allocated selected The...

Currently, Offshore Wind Farms (OWFs) are designed to achieve high turbine density so as to reduce costs. However, due to wake interferences, densely packing turbines reduces energy production. Having insight into optimized trade-offs between energy production, capital investment and operational costs would be valuable to OWFs designers. To obtain this insight, the design of OWFs should be form...

In multi-objective evolutionary fuzzy systems, the process of tuning the membership functions plays an important role towards optimizing the systems accuracy. Although, the shape and position of the membership functions in the partition should not change too much with relation to the original partition, so that it does not lose its integrity. This paper presents and discusses multi-objective ev...

Water distribution networks (WDN) model optimization is an important part of smart water systems to achieve optimal strategies. WDN focuses on the nonlinearity discharge head loss equation, availability discrete properties pipe sizes, and conservation constraints. Multi-objective evolutionary algorithms (MOEAs) have been proposed successfully applied in field design optimization. Previous studi...

Several problems in the area of financial optimization can be naturally dealt with optimization techniques under multiobjective approaches, followed by a decision-making procedure on the resulting efficient solutions. The problem of portfolio optimization is one of them. This chapter studies the use of evolutionary multiobjective techniques to solve such problems, focusing on Venezuelan market ...