An improved Estimation of Distribution Algorithm for Solving Constrained Mixed-Integer Nonlinear Programming Problems

In a mixed-integer nonlinear programming problem, integer restrictions divide the feasible region into discontinuous feasible parts with different sizes. Evolutionary Algorithms (EAs) are usually vulnerable to being trapped in larger discontinuous feasible parts. In this work, an improved version of...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2022 IEEE Congress on Evolutionary Computation (CEC) S. 01 - 08
Hauptverfasser: Molina Perez, Daniel, Alfredo Portilla-Flores, Edgar, Mezura-Montes, Efren, Vega-Alvarado, Eduardo
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 18.07.2022
Schlagworte:
Benchmark testing
Estimation
estimation of distribution algorithm
evolution-ary algorithms
Evolutionary computation
Histograms
integer restriction handling
mixed integer non-linear programming
Programming
Proposals
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In a mixed-integer nonlinear programming problem, integer restrictions divide the feasible region into discontinuous feasible parts with different sizes. Evolutionary Algorithms (EAs) are usually vulnerable to being trapped in larger discontinuous feasible parts. In this work, an improved version of an Estimation of Distribution Algorithm (EDA) is developed, where two new op-erations are proposed. The first one establishes a link between the learning-based histogram model and the \varepsilon -constrained method. Here, the constraint violation level of the \varepsilon -constrained method is used to explore the smaller discontinuous parts and form a better statistical model. The second operation is the hybridization of the EDA with a mutation operator to generate offspring from both the global distribution information and the parent information. A benchmark is used to test the performance of the improved proposal. The results indicated that the proposed approach shows a better performance against other tested EAs. This new proposal solves to a great extent the influence of the larger discontinuous feasible parts, and improve the local refinement of the real variables.
DOI:10.1109/CEC55065.2022.9870338