In this chapter some alternatives are discussed to take advantage of parallel computers in dynamic multi-objective optimization problems (DMO) using evolutionary algorithms. In DMO problems, the objective functions, the constraints, and hence, also the solutions, can change over time and usually demand to be solved online. Thus, high performance computing approaches, such as parallel processing...

Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the ed...

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...

A novel multiobjective optimisation accelerator is introduced that uses direct manipulation in objective space together with neural network mappings from objective space to decision space. This operator is a portable component that can be hybridized with any multiobjective optimisation algorithm. The purpose of this Convergence Acceleration Operator (CAO) is to enhance the search capability and...

Constrained financial portfolio optimization is a challenging domain where the use of multiobjective evolutionary algorithms has been thriving over the last few years. One of the major issues related to this problem is the dependence of the results on a set of parameters. Given the nature of financial prediction, these figures are often inaccurate, which results in unreliable estimates for the ...

A distribution generation (DG) multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper.The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. ...

Resumo Obaixo desempenho termoenergético das edificações resultou em uma progressão no consumo final de energia elétrica pelos setores da construção civil. A demanda por climatização artificial contribui significativamente para esse crescimento e pode estar diretamente relacionada a um inadequado planejamento arquitetônico. Através estratégia otimização multiobjetivo é possível identificar parâ...

Most biobjective evolutionary algorithms maintain a population of fixed size μ and return the final population at termination. During the optimization process many solutions are considered, but most are discarded. We present two generic postprocessing algorithms which utilize the archive of all non-dominated solutions evaluated during the search. We choose the best μ solutions from the archive ...

Invasive group A streptococcal (GAS) disease re-emerged in The Netherlands in the late 1980s. To seek an explanation for this resurgence, the genetic compositions of 22 M1 and 19 M28 GAS strains isolated in The Netherlands between 1960s and the mid-1990s were analyzed by using a mixed-genome DNA microarray. During this four-decade period, M1 and especially M28 strains acquired prophages on at l...

Local Indicators of Spatial Aggregation (LISA) can be used as objectives in a multicriteria framework when highly autocorrelated areas (hot-spots) must be identified and geographically located in complex areas. To do so, a Multi-Objective Evolutionary Algorithm (MOEA) based on SPEA2 (Strength Pareto Evolutionary Algorithm v.2) has been designed to evaluate three different fitness functions (fin...