A novel multi-objective evolutionary algorithm (MOEA) is developed based on Imperialist Competitive Algorithm (ICA), a newly introduced evolutionary algorithm (EA). Fast non-dominated sorting and the Sigma method are employed for ranking the solutions. The algorithm is tested on six well-known test functions each of them incorporate a particular feature that may cause difficulty to MOEAs. The n...

When tackling the multicriteria optimization of a device in electrical engineering, the exhaustive sampling of Pareto optimal front implies the use of complex and timeconsuming algorithms that are unpractical from the industrial viewpoint. In several cases, however, the accurate identification of a few non-dominated solutions is often sufficient for the design purposes. An evolutionary methodol...

Constraint Satisfaction and Optimization are important areas of Artificial Intelligence. However, in many real-life applications, more functions should be optimized at the same time; the user needs to be provided a set of solutions and a posteriori choose the most preferable. In this paper, we propose an algorithm for solving Multi-Criteria Optimization problems in this setting. The algorithm i...

In this paper an approach to robustness analysis in evolutionary multi-objective optimization is applied to the problem of locating and sizing capacitors for reactive power compensation (VAR planning) in electric radial distribution networks. The main goal of this evolutionary algorithm is to find a non-dominated front containing the most robust non-dominated solutions also ensuring diversity a...

The hydraulic fracturing propagation regimes in the plane strain model are uniformly investigated using a numerical method based on the finite element method. The regimes range from toughness-dominated cases to viscosity-dominated cases, covering zero leak-off situations and small leak-off situations. Unlike the asymptotic solutions, the numerical method is independent of the energy dissipation...

This paper discusses a new model of parallel evolutionary algorithms (EAs) called the specialized island model (SIM) that can be used to generate a set of diverse non-dominated solutions to multiobjective optimization problems. This model is derived from the island model, in which an EA is divided into several subEAs that exchange individuals among them. In SIM, each subEA is responsible (i.e.,...

Among numerous multi-objective optimization algorithms, the Elitist non-dominated sorting genetic algorithm (NSGA-II) is one of the most popular methods due to its simplicity, effectiveness and minimum involvement of the user. This article develops a multi-objective variation of the Nelder-Mead simplex method and combines it with NSGA-II in order to improve the quality and spread of the solutio...

In this paper, a newly developed jumping genes evolutionary paradigm is proposed for optimizing the multiobjective resource management problem in direct sequence–wideband code division multiple access systems. This formulation enables both total transmission power and total transmission rate to be simultaneously optimized. Since these two objectives are conflicting in nature, a set of tradeoff ...

SUMMARY Convection-dominated problems are typiied by the presence of strongly directional features such as shock waves or boundary layers. Resolution of numerical solutions using an isotropic mesh can lead to unnecessary reenement in directions parallel to such features. This is particularly important in three dimensions where the grid size increases rapidly during conventional isotropic reenem...