Based on the analysis on the basic principles and characteristics of the existing multiobjective genetic algorithm (MOGA), an improved multi-objective GA with elites maintain is put forward based on non-dominated sorting genetic algorithm (NSGA). NSGA-II algorithm theory and parallel hybrid evolutionary theory is described in detail. The design principle, process and detailed implementations of...

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...

many real water resources optimization problems involve conflicting objectives. in this study, multiobjective genetic algorithm nsga-ii, has been developed for optimization the conjunctive use of surface water and groundwater resources and optimal management of supply and demand of agricultural water. here, optimal allocation of land and water resources to the dominant products in najaf abad pl...

NSGA ( [5]) is a popular non-domination based genetic algorithm for multiobjective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare. A modified version, NSGAII ( [3]) was developed, which has a better sorting algorithm , incorporates elitism...

In this paper, a non-dominated sorting based multi objective EA (MOEA), called Elitist non dominated sorting genetic algorithm (Elitist NSGA) has been presented for solving the fault section estimation problem in automated distribution systems, which alleviates the difficulties associated with conventional techniques of fault section estimation. Due to the presence of various conflicting object...

In this work we give an extension of Kauffman’s NKLandscapes to multiobjective MNK-Landscapes in order to study the effects of epistasis on the performance of multiobjective evolutionary algorithms (MOEAs). This paper focuses on the development of multiobjective random one-bit climbers (moRBCs). We incrementally build several moRBCs and analyze basic working principles of state of the art MOEAs...

The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive...

Multiobjective cellular genetic algorithms (MOcGAs) are variants of evolutionary computation algorithms by organizing the population into grid structures, which are usually 2D grids. This paper proposes a new MOcGA, namely cosine multiobjective cellular genetic algorithm (C-MCGA), for continuous multiobjective optimization. The CMCGA introduces two new components: a 3D grid structure and a cosi...

In this paper, we present a practical case of the multiobjective knapsack problem which concerns the elaboration of the optimal action plan in the social and medico-social sector. We provide a description and a formal model of the problem as well as some preliminary computational results. We perform an empirical analysis of the behavior of three metaheuristic approaches: a fast and elitist mult...

The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive...