NSGA methodology discussed in Section 3.1 suffers from three weaknesses: computational complexity, non-elitist approach and the need to specify a sharing parameter. An improved version of NSGA known as NSGA-II, which resolved the above problems and uses elitism to create a diverse Pareto-optimal front, has been subsequently presented (Deb et al 2002). The main features of NSGA-II are low comput...

systems in which reaction terms are coupled to diffusion and advection transports arise in awide range of chemical engineering applications, physics, biology and environmental. in these cases, thecomponents of the unknown can denote concentrations or population sizes which represent quantities andthey need to remain positive. classical finite difference schemes may produce numerical drawbacks s...

Non-dominated Sorting in Genetic Algorithms-II (NSGA-II) is a popular non-domination based genetic algorithm for solving multi-objective optimization problems. This paper investigates the application of NSGA-II technique for the design of a Thyristor Controlled Series Compensator (TCSC)-based controller and a power system stabilizer. The design objective is to improve both rotor angle stability...

We investigate stability of a gravitationally collapsing iron core against non-spherical perturbation. The gravitationally collapsing iron core is approximated by a similarity solution for dynamically collapsing polytropic gas sphere. We find that the similarity solution is unstable against non-spherical perturbations. The perturbation grows in proportion to (t − t0) −σ while the the central de...

Portfolio managers are typically constrained by turnover limits, minimum and maximum stock positions, cardinality, a target market capitalization and sometimes the need to hew to a style (such as growth or value). In addition, portfolio managers often use multifactor stock models to choose stocks based upon their respective fundamental data. We use multiobjective evolutionary algorithms (MOEAs)...

This paper presents a new algorithm for identifying all the supported nondominated vectors (or outcomes) in the objective space, as well as the corresponding efficient solutions in the decision space, for the multi-objective integer network flow problem. Identifying the set of supported non-dominated vectors is of the utmost importance for obtaining a first approximation of the whole set of non...

In this paper, we propose a dynamic, non-dominated sorting, multiobjective particle-swarm-based optimizer, named Hierarchical Non-dominated Sorting Particle Swarm Optimizer (H-NSPSO), for memory usage optimization in embedded systems. It significantly reduces the computational complexity of others MultiObjective Particle Swarm Optimization (MOPSO) algorithms. Concretely, it first uses a fast no...

This article introduces several algorithms for finding a representative subset of the non-dominated point set of a biobjective discrete optimization problem with respect to uniformity, coverage and the -indicator. We consider the representation problem itself as multiobjective, trying to find a good compromise between these quality measures. These representation problems are formulated as parti...

We analyze the effect of pressure on the evolution of perturbations of an Einstein-de Sitter Universe in the matter dominated epoch assuming an ideal gas equation of state. For the sake of simplicity the temperature is considered uniform. The goal of the paper is to examine the validity of the linear approximation. With this purpose the evolution equations are developed including quadratic term...