this paper focuses upon the development of an efficient method for conceptual design optimization of a satellite. there are many option for a satellite subsystems that could be choice, as acceptable solution to implement of a space system mission. every option should be assessment based on the different criteria such as cost, mass, reliability and technology contraint (complexity). in this rese...

controller design and optimization problems, with more than one objective, are referred as multiple objectives or multiple attributed problems. in this paper, a novel method is proposed for designing optimum pid controller that is called genetic multiple attributed decision making method (gmadm). this method is newer than the previous methods and in this paper some options are considered that h...

In this paper, we propose a multiobjective Genetic Algorithm (mo-GA) for solving a multiobjective reliability optimization problem. In order to obtain high efficiency of searching non-dominated solutions, we combine the Improved Saving Pareto solutions Strategy (ISPS) and the adoptive Local Search (LS) with the multiobjective Genetic Algorithm. Through some numerical experiments, we evaluate th...

Proposal in this paper is a method of interpolating the pareto optimal solution in Multiobjective Genetic Algorithms. In this method, some vacancies are maked up by resuming computation of Multiobjective Genetic Algorithms. A beginning field is set up by variables of both sides of the vacancy. Then, computation of Multiobjective Genetic Algorithms is started from the beginning field again. Visu...

Abstract—In this paper, a hybrid multiobjective evolutionary algorithm, MOEA/D-GO (Multiobjective Evolutionary Algorithm Based on Decomposition combined with Enhanced Genetic Operators), is proposed for fragment-type antenna design. It combines the ability and efficiency of MOEA/D to deal with multiobjective optimization problems with the specific character of two-dimensional chromosome coding ...

The Protein Structure Prediction problem, which involves correctly predicting the geometrical conformation of a fully folded protein, is extremely difficult to solve and there currently is no “best” method of generating solutions. This paper focuses on an energy minimization technique and the use of a multiobjective genetic algorithm, the multiobjective fast messy genetic algorithm (fmGA) to ob...

The Genetic Immune Strategy for Multiple Objective Optimization (GISMOO) is a hybrid algorithm for solving multiobjective problems. The performance of this approach has been assessed using a classical combinatorial multiobjective optimization benchmark: the multiobjective 0/1 knapsack problem (MOKP) [1] and two-dimensional unconstrained multiobjective problems (ZDT) [2]. This paper shows that t...

We present a multiobjective genetic algorithm that incorporates various genetic algorithm techniques that have been proven to be efficient and robust in their problem domain. More specifically, we integrate rank based selection, adaptive niching through coevolutionary sharing, elitist recombination, and non-dominated sorting into a multiobjective genetic algorithm called ERMOCS. As a proof of c...

This paper presents the Parallel Single Front Genetic Algorithm (PSFGA), a parallel Pareto-based algorithm for multiobjective optimization problems based on an evolutionary procedure. In this procedure, a population of solutions is sorted with respect to the values of the objective functions and partitioned into subpopulations which are distributed among the processors. Each processor applies a...