English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

This paper proposes an improved epsilon constraint-handling mechanism, andcombines it with a decomposition-based multi-objective evolutionary algorithm(MOEA/D) to solve constrained multi-objective optimization problems (CMOPs).The proposed constrained multi-objective evolutionary algorithm (CMOEA) isnamed MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according tothe ratio of feasible to total solutions (RFS) in the current population. Inorder to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs withtwo and three objectives is designed, having large infeasible regions (relativeto the feasible regions), and they are called LIR-CMOPs. Then the fourteenbenchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and fourother decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR,MOEA/D-CDP and C-MOEA/D. The experimental results indicate that MOEA/D-IEpsilonis significantly better than the other four CMOEAs on all of the testinstances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPswith large infeasible regions. Furthermore, a real-world problem, namely therobot gripper optimization problem, is used to test the five CMOEAs. Theexperimental results demonstrate that MOEA/D-IEpsilon also outperforms theother four CMOEAs on this problem.

An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions