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...

We consider multiobjective shortest paths, a fundamental (NP-hard) problem in multiobjective optimization, where we are interested not in optimizing a single objective, but in finding a set of paths that captures the trade-off (the so-called Pareto curve) among several objectives in a digraph whose edges are associated with multidimensional cost vectors. We provide a new FPTAS for computing an ...

Title of Document: ONLINE APPROXIMATION ASSISTED MULTIOBJECTIVE OPTIMIZATION WITH HEAT EXCHANGER DESIGN APPLICATIONS Khaled Hassan Mohamed Saleh, Doctor of Philosophy, 2012 Directed By: Shapour Azarm, Professor, Department of Mechanical Engineering Computer simulations can be intensive as is the case in Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). The computational cost...

J. Deon Garrett. Ph.D. The University of Memphis. February, 2008. Multiobjective Fitness Landscape Analysis and the Design of Effective Memetic Algorithms. Major Professor: Dipankar Dasgupta, Ph.D. For a wide variety of combinatorial optimization problems, no efficient algorithms exist to exactly solve the problem unless P=NP. For these problems, metaheuristics have come to dominate the landsca...

− A new trend in the design of fuzzy rulebased systems is the use of evolutionary multiobjective optimization (EMO) algorithms. This trend is observed in various areas in machine learning. EMO algorithms are often used to search for a number of Pareto-optimal non-linear systems with respect to their accuracy and complexity. In this paper, we first explain some basic concepts in multiobjective o...

Although a number of multiobjective evolutionary algorithms have been proposed over the last two decades, not much effort has been made to deal with variable linkages in multiobjective optimization. Recently, we have suggested a general framework of multiobjective evolutionary algorithms based on decomposition (MOEA/D) [1]. MOEA/D decomposes a MOP into a number of scalar optimization subproblem...

We describe the development and testing of a metaheuristic procedure, based on the scatter search methodology, for the problem of approximating the efficient frontier of nonlinear multiobjective optimization problems with continuous variables. Recent applications of scatter search have shown its merit as a global optimization technique for single-objective problems. However, the application of ...

In this paper we propose a new algorithm for solving multiobjective optimization problems. Our proposal adapts the well-known scatter search template for single objective optimization to the multiobjective domain. The result is a hybrid metaheuristic algorithm called AbYSS, which follows the scatter search structure but using mutation and crossover operators coming from the field of evolutionar...

We describe the development and testing of a metaheuristic procedure, based on the scatter search methodology, for the problem of approximating the efficient frontier of nonlinear multiobjective optimization problems with continuous variables. Recent applications of scatter search have shown its merit as a global optimization technique for single-objective problems. However, the application of ...

In multiobjective state space graph problems, each solution-path is evaluated by a cost vector. These cost vectors can be partially or completely ordered using a preference relation compatible with Pareto dominance. In this context, multiobjective preference-based search (MOPBS) aims at computing the preferred feasible solutions according to a predefined preference model, these preferred soluti...