In this paper we present a study of the application of Evolutionary Computation methods to the optimisation of the start-up of a combined cycle power plant. We propose a multiobjective approach considering different objectives for the optimisation in order to reduce the pollution emissions and to maximise the efficiency of the plant. We compare a multiobjective evolutionary algorithm (NSGA-II) ...

This paper addresses a multi-objective order scheduling problem in production planning under a complicated production environment with the consideration of multiple plants, multiple production departments and multiple production processes. A Pareto optimization model, combining a NSGA-II-based optimization process with an effective production process simulator, is developed to handle this probl...

Antenna design problems often require the optimization of several conflicting objectives such as gain maximization, sidelobe level (SLL) reduction and input impedance matching. Multiobjective Evolutionary Algorithms (MOEAs) are suitable optimization techniques for solving such problems. An efficient algorithm is Generalized Differential Evolution (GDE3), which is a multi-objective extension of ...

Two multi-objective genetic algorithms, an elitist version of MOGA and NSGA-II, were applied to solve two linear control design problems. The first was a H2 problem with a PI controller structure, for a first order stable plant. The second was a mixed H2/H4 control problem. In both cases, three indicators were used to evaluate each algorithm performance: Set coverage, spread and hypervolume. It...

One way to cope with a huge design space formed by several parameters is using methods for Automatic Design Space Exploration (ADSE). Recently we developed a Framework for Automatic Design Space Explorations focused on micro-architectural optimizations. In this article we evaluate the influence of three different evolutionary algorithms on the performance of design space explorations. More prec...

We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multiobjective evolutionary algorithm (MOEA), i.e., NSGA-II. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, a...

In this paper, a new genetic algorithm for multi-objective optimization problems is introduced. That is called ”Neighborhood Cultivation GA (NCGA)”. In the recent studies such as SPEA2 or NSGA-II, it is demonstrated that some mechanisms are important; the mechanisms of placement in an archive of the excellent solutions, sharing without parameters, assign of fitness, selection and reflection the...