TY - GEN
T1 - Extending Boundary Updating Approach for Constrained Multi-objective Optimization Problems
AU - Rahimi, Iman
AU - Gandomi, Amir H.
AU - Nikoo, Mohammad Reza
AU - Chen, Fang
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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PY - 2023/1/1
Y1 - 2023/1/1
N2 - To date, several algorithms have been proposed to deal with constrained optimization problems, particularly multi-objective optimization problems (MOOPs), in real-world engineering. This work extends the 2020 study by Gandomi & Deb on boundary updating (BU) for the MOOPs. The proposed method is an implicit constraint handling technique (CHT) that aims to cut the infeasible search space, so the optimization algorithm focuses on feasible regions. Furthermore, the proposed method is coupled with an explicit CHT, namely, feasibility rules and then the search operator (here NSGA-II) is applied to the optimization problem. To illustrate the applicability of the proposed approach for MOOPs, a numerical example is presented in detail. Additionally, an evaluation of the BU method was conducted by comparing its performance to an approach without the BU method while the feasibility rules (as an explicit CHT) work alone. The results show that the proposed method can significantly boost the solutions of constrained multi-objective optimization.
AB - To date, several algorithms have been proposed to deal with constrained optimization problems, particularly multi-objective optimization problems (MOOPs), in real-world engineering. This work extends the 2020 study by Gandomi & Deb on boundary updating (BU) for the MOOPs. The proposed method is an implicit constraint handling technique (CHT) that aims to cut the infeasible search space, so the optimization algorithm focuses on feasible regions. Furthermore, the proposed method is coupled with an explicit CHT, namely, feasibility rules and then the search operator (here NSGA-II) is applied to the optimization problem. To illustrate the applicability of the proposed approach for MOOPs, a numerical example is presented in detail. Additionally, an evaluation of the BU method was conducted by comparing its performance to an approach without the BU method while the feasibility rules (as an explicit CHT) work alone. The results show that the proposed method can significantly boost the solutions of constrained multi-objective optimization.
KW - Constraint handling
KW - Evolutionary computation
KW - Multi-objective Optimization
KW - NSGA-II
UR - http://www.scopus.com/inward/record.url?scp=85159434263&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85159434263&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/21b78893-6224-3e3c-9619-bbb5ffbec750/
U2 - 10.1007/978-3-031-30229-9_7
DO - 10.1007/978-3-031-30229-9_7
M3 - Conference contribution
AN - SCOPUS:85159434263
SN - 9783031302282
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 102
EP - 117
BT - Applications of Evolutionary Computation - 26th European Conference, EvoApplications 2023, Held as Part of EvoStar 2023, Proceedings
A2 - Correia, João
A2 - Smith, Stephen
A2 - Qaddoura, Raneem
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023
Y2 - 12 April 2023 through 14 April 2023
ER -