Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES)
This work addresses multi-objective optimization for continuous problems, but it appears incremental as it builds on existing CMA-ES methods and benchmarks on a known suite.
The authors tackled the problem of achieving good anytime performance in bi-objective optimization by proposing HMO-CMA-ES, a hybrid algorithm combining CMA-ES variants and BOBYQA, and benchmarked it on 55 problems from the COCO framework, showing performance assessed via the hypervolume metric.
We propose a multi-objective optimization algorithm aimed at achieving good anytime performance over a wide range of problems. Performance is assessed in terms of the hypervolume metric. The algorithm called HMO-CMA-ES represents a hybrid of several old and new variants of CMA-ES, complemented by BOBYQA as a warm start. We benchmark HMO-CMA-ES on the recently introduced bi-objective problem suite of the COCO framework (COmparing Continuous Optimizers), consisting of 55 scalable continuous optimization problems, which is used by the Black-Box Optimization Benchmarking (BBOB) Workshop 2016.