Adaptive Multi/Many-Objective Transformation for Constrained Optimization

Genghui Li; Zhenkun Wang; Weifeng Gao; Laizhong Cui; Qingfu Zhang

doi:10.1109/TSMC.2024.3489600

Adaptive Multi/Many-Objective Transformation for Constrained Optimization

Genghui Li, Zhenkun Wang^*, Weifeng Gao, Laizhong Cui, Qingfu Zhang

^*Corresponding author for this work

Department of Computer Science

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

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. © 2024 IEEE.

Original language	English
Pages (from-to)	721-734
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	55
Issue number	1
Online published	19 Nov 2024
DOIs	https://doi.org/10.1109/TSMC.2024.3489600
Publication status	Published - Jan 2025

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62206120, Grant 62106096, Grant 62276202, and Grant 62276223; in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2024B1515040012; in part by the Research Team Cultivation Program of Shenzhen University under Grant 2023QNT015; in part by the Research Grants Council of the Hong Kong Special Administrative Region, China GRF under Project CityU11215622; in part by the Ministry of Education Joint Fund under Grant 8091B03072304; in part by the National Key Laboratory of Science and Technology on Space Microwave under Grant HTKJ2024KL504008; and in part by the Fundamental Research Funds for the Central Universities under Grant QTZX22047.

Research Keywords

Adaptive transformation
auxiliary function
constrained optimization
many-objective optimization

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10.1109/TSMC.2024.3489600

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abstract = "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. {\textcopyright} 2024 IEEE.",

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author = "Genghui Li and Zhenkun Wang and Weifeng Gao and Laizhong Cui and Qingfu Zhang",

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AU - Zhang, Qingfu

PY - 2025/1

Y1 - 2025/1

N2 - 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. © 2024 IEEE.

AB - 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. © 2024 IEEE.

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Adaptive Multi/Many-Objective Transformation for Constrained Optimization

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GRF: Few for Many: A Non-Pareto Approach for Many Objective Optimization

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Adaptive Multi/Many-Objective Transformation for Constrained Optimization

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GRF: Few for Many: A Non-Pareto Approach for Many Objective Optimization

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