TY - GEN
T1 - An improved Estimation of Distribution Algorithm for Solving Constrained Mixed-Integer Nonlinear Programming Problems
AU - Molina Perez, Daniel
AU - Alfredo Portilla-Flores, Edgar
AU - Mezura-Montes, Efren
AU - Vega-Alvarado, Eduardo
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - estimation of distribution algorithm
KW - evolutionary algorithms
KW - integer restriction handling
KW - mixed integer non-linear programming
UR - http://www.scopus.com/inward/record.url?scp=85138717505&partnerID=8YFLogxK
U2 - 10.1109/CEC55065.2022.9870338
DO - 10.1109/CEC55065.2022.9870338
M3 - Contribución a la conferencia
AN - SCOPUS:85138717505
T3 - 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings
BT - 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Congress on Evolutionary Computation, CEC 2022
Y2 - 18 July 2022 through 23 July 2022
ER -