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Adaptive Multi/Many-Objective Transformation for Constrained Optimization
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DOI：10.1109/TSMC.2024.3489600.png)
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Abstract
En 中文
Transforming a constrained optimization problem (COP) into a multi/many-objective optimization problem (MOP/MaOP) represents a practical approach for solving COPs. This article introduces an adaptive multi/many-objective transformation technique, termed adaptive many-objective transformation technique (AMaOTCO), designed to effectively address COPs. The transformed many-objective optimization problem (MaOP) defines an objective using a convex combination of the objective function (or constraint violation function) and an auxiliary function. This auxiliary function is constructed through a convex combination of the objective function and a weighted constraint violation function. The adaptive tuning of all combination coefficients is based on population information. This adaptive tuning ensures an intelligent balance between minimizing various constraint violations and managing the tradeoff between objective function minimization and constraint violation reduction. The effectiveness of the proposed AMaOTCO is demonstrated through comparisons with state-of-the-art constrained evolutionary algorithms (CEAs) on a set of real-world COPs.
Keywords:
Optimization
Maintenance engineering
Linear programming
Tuning
Computer science
Pareto optimization
Software
Evolutionary computation
Chatbots
Vectors
Adaptive transformation
auxiliary function
constrained optimization
many-objective optimization
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W