In many real-world optimization problems the quality of solutions is determined with several fundamentally different objectives, such as, for example, cost, performance and profit. These objectives are often mutually conflicting thus yielding several optimal solutions, where each of them represents a different tradeoff between the objectives. Classicalmethods solve multiobjective optimization p...

This paper presents a method to solve a linear or nonlinear Fuzzy Multiobjective Programming problem. We propose Genetic Search based approaches to the case in which fuzzy constraints are assumed.

In this paper a Multiobjective Optimization Genetic Algorithm, named Reduced Pareto Set Genetic Algorithm with Elitism (RPSGAe), is presented and its performance assessed. The algorithm is compared with other MOEA ́s using three difficult problems from the literature and a sophisticated statistical comparison technique. The preliminary results obtained showed that the RPSGAe outperform the other...

This paper presents a real jumping gene genetic algorithm (RJGGA) as an enhancement of the jumping gene genetic algorithm (JGGA) [T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, A jumping gene algorithm for multiobjective resource management in wideband CDMA systems, The Computer Journal 48 (6) (2005) 749–768; T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, Multiobjective optimization of radio-to-fiber rep...

In this paper, we investigate the issue of convergence in multiobjective optimization problems when using a MultiObjective Genetic Algorithm (MOGA) to determine the set of Pareto optimal solutions. Additionally, given a Pareto set for a multi-objective problem, the mapping between the performance and design space is studied to determine design variable configurations for a given set of performa...

With the advent of Cloud databases, query optimizers need to find paretooptimal solutions in terms of response time and monetary cost. Our novel approach minimizes both objectives by deploying alternative virtual resources and query plans making use of the virtual resource elasticity of the Cloud. We propose an exact multiobjective branch-and-bound and a robust multiobjective genetic algorithm ...

In multiobjective optimization problems, the identified Pareto Frontiers and Sets often contain too many solutions, which make it difficult for the decision maker to select a preferred alternative. To facilitate the selection task, decision making support tools can be used in different instances of the multiobjective optimization search to introduce preferences on the objectives or to give a co...

The aim of this study is to deal with a minimum cost network flow problem MCNFP in a largescale construction project using a nonlinear multiobjective bilevel model with birandom variables. The main target of the upper level is to minimize both direct and transportation time costs. The target of the lower level is to minimize transportation costs. After an analysis of the birandom variables, an ...

This paper describes a multiobjective approach for melody harmonization in evolutionary music. There are numerous methods and a myriad of results to a process of harmonization of a given melody. Some implicit rules can be extracted from musical theory, but some harmonic aspects can only be defined by preferences of a composer. Thus, a multiobjective approach may be useful to allow an evolutiona...

This paper presents a solution methodology for multiobjective optimization problems in the context of models for the placement of components on printed wiring boards (PWB’s). The methodology combines the use of a flow and heat transfer solver, a genetic algorithm for the adaptive search of optimal or near-optimal solutions, and a multiobjective optimization strategy [Pareto optimization or mult...