An Adaptive Constraint Violation Evaluation Framework for Constrained Multiobjective Evolutionary Optimization
提出自适应约束违反评估框架ACVE，通过动态调整聚类数量来平衡约束满足与目标优化，减少对约束处理技术的依赖，并基于此开发双种群动态协同进化算法DDCo，在基准测试和锂离子电池充电协议优化中表现优异。
Constrained multiobjective optimization evolutionary algorithms cope with various constraints through the combination of a constraint violation evaluation (CVE) framework with a constraint handling technique. The evaluation of constraint violation is a critical problem that determines how effectively constraint information is utilized. However, this topic has received limited attention in existing research. To bridge this gap, an adaptive CVE (ACVE) framework that considers the evolutionary state is proposed in this paper. ACVE first divides solutions into multiple clusters. Each cluster is then reassigned a constraint violation value. By adjusting the number of clusters based on the evolutionary state, ACVE adaptively utilizes constraint information at different levels of granularity. This design allows ACVE to achieve a more optimal balance between constraint satisfaction and objective optimization, thereby reducing the dependency on constraint handling techniques. Extensive experiments conducted on several benchmark test suites demonstrate the effectiveness of ACVE. Based on ACVE, we develop the dual-population dynamic coevolutionary algorithm (DDCo). In experiments on multiple benchmark test suites, DDCo demonstrates superior or competitive performance compared with state-of-the-art algorithms, as evaluated using indicators such as inverted generational distance and hypervolume. Moreover, DDCo is successfully applied to optimize the charging protocols of lithium-ion batteries.