Next Article in Journal
Cost Efficiency Evaluation Based on a Data Envelopment Analysis Approach by Considering Undesirable Outputs on the Basis of the Semi-Disposability Assumption
Next Article in Special Issue
Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization
Previous Article in Journal
Efficient Methods to Calculate Partial Sphere Surface Areas for a Higher Resolution Finite Volume Method for Diffusion-Reaction Systems in Biological Modeling
Previous Article in Special Issue
Optimizing the Maximal Perturbation in Point Sets while Preserving the Order Type
Open AccessFeature PaperArticle

Non-Epsilon Dominated Evolutionary Algorithm for the Set of Approximate Solutions

Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
Departamento de Computación, CINVESTAV-IPN, Mexico City 07360, Mexico
School of Engineering, University of California Merced, Merced, CA 95343, USA
Author to whom correspondence should be addressed.
Math. Comput. Appl. 2020, 25(1), 3;
Received: 16 October 2019 / Revised: 18 December 2019 / Accepted: 25 December 2019 / Published: 8 January 2020
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2019)
In this paper, we present a novel evolutionary algorithm for the computation of approximate solutions for multi-objective optimization problems. These solutions are of particular interest to the decision-maker as backup solutions since they can provide solutions with similar quality but in different regions of the decision space. The novel algorithm uses a subpopulation approach to put pressure towards the Pareto front while exploring promissory areas for approximate solutions. Furthermore, the algorithm uses an external archiver to maintain a suitable representation in both decision and objective space. The novel algorithm is capable of computing an approximation of the set of interest with good quality in terms of the averaged Hausdorff distance. We underline the statements on some academic problems from literature and an application in non-uniform beams. View Full-Text
Keywords: evolutionary multi-objective optimization; nearly optimal solutions; non-uniform beams evolutionary multi-objective optimization; nearly optimal solutions; non-uniform beams
Show Figures

Figure 1

MDPI and ACS Style

Hernández Castellanos, C.I.; Schütze, O.; Sun, J.-Q.; Ober-Blöbaum, S. Non-Epsilon Dominated Evolutionary Algorithm for the Set of Approximate Solutions. Math. Comput. Appl. 2020, 25, 3.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop