This paper presents a new multiobjective evolutionary algorithm applied to a radial basis function (RBF) network design based on mult iobjective particle swarm optimization augmented with local search features. The algorithm is named the memetic multiobjective particle swarm optimization RBF network (MPSON) because it integrates the accuracy and structure of an RBF network. The proposed algorit...

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...

In this paper, a class of chance constrained multiobjective linear programming model with birandom coefficients is considered for vendor selection problem. Firstly we present a crisp equivalent model for a special case and give a traditional method for crisp model. Then, the technique of birandom simulation is applied to deal with general birandom objective functions and birandom constraints wh...

Many water resources systems are characterized by multiple objectives. For multiobjective optimization, typically there can be no single optimal solution which can simultaneously satisfy all the goals, but rather a set of technologically efficient noninferior or Pareto optimal solutions exists. Generating those Pareto optimal solutions is a challenging task and often difficulties arise in using...

This work proposes a quantitative, non-parametric interpretation of statistical performance of stochastic multiobjective optimizers, including, but not limited to, genetic algorithms. It is shown that, according to this interpretation, typical performance can be defined in terms analogous to the notion of median for ordinal data, as can other measures analogous to other quantiles. Non-parametri...

Two novel schemes of selecting the current best solutions for multiobjective differential evolution are proposed in this paper. Based on the search biases strategy suggested by Runarsson and Yao, a hybrid of multiobjective differential evolution and genetic algorithm with (N+N) framework for constrained MOPs is given. And then the hybrid algorithm adopting the two schemes respectively is compar...

A genetic algorithm for multiobjective optimization is presented which tries to evolve an evenly distributed set of solutions belonging to the Pareto set by: (i) ranking the population according to nondomination properties; (ii) defining a filter to retain Pareto set solutions and (iii) using adequate operators: exclusion, addition and single-objective operator which improves the individuals fr...

Multiobjective optimization strategy so-called Physical Programming allows controller designers a flexible way to express design preferences with a ’physical’ sense. For each objective (settling time, overshoot, disturbance rejection, etc.) preferences are established through categories as desirable, tolerable, unacceptable, etc. assigned to numerical ranges. The problem is translated into a un...