This paper presents the integration between a co-operative co-evolutionary genetic algorithm (CCGA) and four evolutionary multiobjective optimisation algorithms (EMOAs): a multi-objective genetic algorithm (MOGA), a niched Pareto genetic algorithm (NPGA), a nondominated sorting genetic algorithm (NSGA) and a controlled elitist nondominated sorting genetic algorithm (CNSGA). The resulting algori...

In this paper, we develop a novel algorithm which finds a subset of Pareto front of a Multi-Objective Distributed Constraint Optimization Problem. This algorithm utilizes the Lp-norm method, pseudo-tree, and Dynamic Programming technique. Furthermore, we show that this Lp-norm based algorithm can only guarantee to find a Pareto optimal solution, when we employ L1-norm (Manhattan norm).

A multi-objective heuristic particle swarm optimiser (MOHPSO) based on Pareto multi-objective theory is proposed to solve multi-objective optimality problems. The optimality objectives are the roof displacement and structure weight. Two types of structure are analysed in this paper, a truss structure and a framework structure. Performance-based seismic analysis, such as classical and modal push...

So far the focus of almost all multior many-objective performance metrics has been the convergence and distribution of solutions in the objective space (Pareto-surface). Pareto-surface metrics such as IGD, HV, and Spread are simple and provide knowledge about the overall performance of the solution set. However, these measures do not provide any insight into the distribution or spread of a solu...

 Evolutionary multi-objective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multi-objective problem. The purpose of this paper is to describe a newly developed evolutionary approach --Paretobased multi-objective differential evolution (MODE). In this paper, the concept of differential evolution, which is well-known in the continuou...

The analytical form of general affine transform families with givenmaximum likelihood estimators for the affine parameters is determined. In this context, the simultaneous maximum likelihood equations of the affine parameters in the generalised Pareto distribution cannot have a common solution. This pathological situation is removed by extending it to a four parameter family, called Pareto type...

This paper proposes a practical methodology for the solution of multi-objective system reliability optimization problems. The new method is based on the sequential combination of multi-objective evolutionary algorithms and data clustering on the prospective solutions to yield a smaller, more manageable sets of prospective solutions. Existing methods for multiple objective problems involve eithe...

Design of any complex system entails many objectives to reach and constraints to satisfy. This multi–objective nature of the problem ensures that the technology solution is always a compromise between conflicting objectives. The purpose of this paper is to demonstrate the application of Niched Pareto genetic algorithm as a relatively fast and straightforward method for obtaining technology sets...

Optimization is an important concept in science and engineering. Traditionally, methods are developed for unconstrained and constrained single objective optimization tasks. However, with the increasing complexity of optimization problems in the modern technological real world problems, multi-objective optimization algorithms are needed and being developed. With the advent of evolutionary algori...