Adaptive Multi/Many-Objective Transformation for Constrained Optimization

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

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.