Evolutionary algorithms EA have proved to be well suited for optimization prob lems with multiple objectives Due to their inherent parallelism they are able to capture a number of solutions concurrently in a single run In this report we propose a new evolutionary approach to multicriteria optimization the Strength Pareto Evolutionary Algorithm SPEA It combines various features of previous multi...

There are currently three different game strategies to solve multi-criteria design problems: 1) Cooperative Games (Pareto Front), 2) Competitive Games (Nash Game) and 3) Hierarchical Games (Stackelberg Game). Nowadays EAs and Pareto front concept are more and more in solving practical design problems in industry, but this process is time consuming. Here, deterministic optimization method and Pa...

We address the problem of optimizing a spacecraft trajectory by using three different multi-objective evolutionary algorithms: i) Non-dominated sorting genetic algorithm, ii) Pareto-based ranking genetic algorithm, and iii) Strength Pareto genetic algorithm. The trajectory of interest is an orbit transfer around a central body when the spacecraft uses a lowthrust propulsion system. We use a Lya...

In this paper, a combination method has been developed by coupling Multi-Objective Genetic Algorithms (MOGA) and Finite Element Method (FEM). This method has been applied for determination of the optimal stacking sequence of laminated composite plate against buckling. The most important parameters in optimization of a laminated composite plate such as, angle, thickness, number, and material of ...

When we try to implement a multi-objective genetic algorithm (MOGA) with variable weights for finding a set of Pareto optimal solutions, one difficulty lies in determining appropriate search directions for genetic search. In our MOGA, a weight value for each objective in a scalar fitness function was randomly specified. Based on the fitness function with the randomly specified weight values, a ...

The practical use of multiobjective optimization tools in industry is still an open issue. A strategy for reduction of objective function calls is often essential, at a fixed degree of Pareto Optimal Front (POF) approximation accuracy . To this aim an extension of single-objective NN-based GRS methods to Pareto Optimal Front (POF) approximation is proposed. Such an extension is not at all strai...

Though optimization problems in industrial electromagnetic design are often truly multiobjective, solving them by evolutionary Pareto Optimal Front approximation is often unpractical, due to the high computational cost of objective evaluations. In order to overcome this drawback, an extension of classical single-objective Generalized Response Surface (GRS) methods to Pareto-optimal front approx...

This vignette is intended to give a short introduction into the methods of the qualityTools package. The qualityTools package contains methods associated with the Define Measure Analyze Improve and Control (i.e. DMAIC) problem solving cycle of the Six Sigma Quality Management methodology. Usage of these methods is illustrated with the help of artificially created datasets. • Define: Pareto Char...