The representation of data quality within established high-dimensional data visualization techniques such as scatterplots and parallel coordinates is still an open problem. This work offers a scale-invariant measure based on Pareto optimality that is able to indicate the quality of data points with respect to the Pareto front. In cases where datasets contain noise or parameters that cannot easi...

In this paper,we present a newpopulation-basedMonte Carlomethod, so-calledMOMCMC (Multi-Objective Markov Chain Monte Carlo), for sampling in the presence of multiple objective functions in real parameter space. The MOMCMC method is designed to address the ‘‘multi-objective sampling’’ problem, which is not only of interest in exploring diversified solutions at the Pareto optimal front in the fun...

An approach to constructing a Pareto front approximation to computationally expensive multiobjective optimization problems is developed. The approximation is constructed as a sub-complex of a Delaunay triangulation of a finite set of Pareto optimal outcomes to the problem. The approach is based on the concept of inherent nondominance. Rules for checking the inherent nondominance of complexes ar...

This paper reviews some state-of-the-art hybrid multiobjective evolutionary algorithms (MOEAs) dealing with multiobjective optimization problem (MOP). The mathematical formulation of a MOP and some basic definition for tackling MOPs, including Pareto optimality, Pareto optimal set (PS), Pareto front (PF) are provided in Section 1. Section 2 presents a brief introduction to hybrid MOEAs.

The use of Pareto-optimal performance fronts in emerging design methodologies for analog integrated circuits is a keystone to overcome the limitations of traditional design methodologies. However, most techniques to generate the fronts reported so far neglect the effect that the surrounding circuitry (such as the load impedance) has on the Pareto-front, thereby making it only realistic for the ...

The sailor assignment problem is a classic example of the generalized task assignment problem. In this instance, it is tried to assign the optimum tasks to the sailors in various time intervals according to their skills, experience and their location with different workloads. This problem has numerous applications in real world however due to many constraints and targets; the solution is not st...

Multi-objective evolutionary algorithms frequently use an archive of non-dominated solutions to approximate the Pareto front. We show that the truncation of this archive to a limited number of solutions can lead to oscillating and shrinking estimates of the Pareto front. New data structures to permit efficient query and update of the full archive are proposed, and the superior quality of fronta...

We propose a general methodology for approximating the Pareto front of multi-criteria optimization problems. Our search-based methodology consists of submitting queries to a constraint solver. Hence, in addition to a set of solutions, we can guarantee bounds on the distance to the actual Pareto front and use this distance to guide the search. Our implementation, which computes and updates the d...

In this paper, we present PICPA, the “Population and Interval Constraint Propagation Algorithm” which is able to produce high quality approximate solutions while giving guaranteed bounds for the Pareto optimal front. These bounds allow us to know whether the heuristic solutions are close to or far away from the optimal front. PICPA combines “Interval Constraint Propagation” (ICP) techniques [1,...

The fast convergence of particle swarm algorithms can become a downside in multi-objective optimization problems when there are many local optimal fronts. In such a situation a multi-objective particle swarm algorithm may get stuck to a local Pareto optimal front. In this paper we propose a new approach in selecting leaders for the particles to follow, which in-turn will guide the algorithm tow...