This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics. It embeds some features and techniques for multi-objective resolution and aims to provide a set of classes allowing to e...

In this paper, we propose a method of MultiObjective Genetic Algorithms (MOGAs), NSGA-II and SPEA2, for document clustering with semantic similarity measures based on WordNet. The MOGAs showed a high performance compared to other clustering algorithms. The main problem in the application of MOGAs for document clustering in the Vector Space Model (VSM) is that it ignores relationships between im...

There has emerged a surge of research activity on multiobjective optimization using evolutionary computation in recent years and a number of well performing algorithms have been published. The quick and highly efficient multiobjective evolutionary algorithm based on dominating tree has been criticized mainly for its restricted elite archive and absence of density estimation. This paper improves...

This paper proposes and evaluates an evolutionary multiobjective optimization algorithm (EMOA) that uses a new quality indicator, called the prospect indicator, for parent selection and environmental selection operators. The prospect indicator measures the potential of each individual to reproduce offspring that dominate itself and spread out in the objective space. The prospect indicator allow...

As a result of the increase in accessibility of computational resources and the increase in the power of the computers during the last two decades, designers are able to create computer models to simulate the behavior of a complex products. To address global competitiveness, companies are forced to optimize their designs and products. Optimizing the design needs several runs of computationally ...

O problema conhecido como CAITO refere-se à determinação de uma ordem para integrar e testar módulos que minimize os custos associados à construção de stubs. Algoritmos de busca, particularmente os algoritmos evolutivos, têm sido utilizados. Entretanto, o problema é bastante complexo e envolve diferentes fatores associados à construção de stubs, tais como diferentes medidas de complexidade, que...

Abstract This paper proposes a method for improving the diversity of Pareto front and uniformity non-dominated solution distributions in fast elitist sorting genetic algorithm (NSGA-II), which is an evolutionary multi-objective optimization algorithm. Conventional NSGA-II has excellent convergence to front, but it been reported that some test cases, does not produce more diverse distribution th...

Hiring is a high-stakes decision-making process that balances the joint objectives of being fair and accurately selecting top candidates. The industry standard method employs subject-matter experts to manually generate hiring algorithms; however, this resource intensive finds sub-optimal solutions. Despite recognized need for algorithmic solutions address these limitations, no reported currentl...

District energy systems can not only reduce consumption but also set supply dispatching schemes according to demand. In addition economic cost, and pollutant are more worthy of attention when evaluating combined cooling, heating power (CCHP) models. this paper, the CCHP model is established with objective primary consumption, emissions. The mathematical expression system proposed, a multi-objec...

Abstract The ground station scheduling problem is a complex involving multiple objectives. Evolutionary techniques for multi-objective optimization are becoming popular among different fields, due to their effectiveness in obtaining set of trade-off solutions. In contrast some conventional methods, that aggregate the objectives into one weighted-sum objective function, evolutionary algorithms m...