In this platform, we address multi-objective optimization(minimization) problem:
Several algorithms/techniques have been proposed and studied to solve such problems. With this context, these algorithms are assessed in one of two ways, viz. theoretical, and empirical analysis. In theoretical analysis, a principled methodology is carried out to derive an analytical bound of the (run-time) solution quality. After t evaluations/steps, the quality of the returned solution is evaluated by a loss/regret measure.
Alternatively, empirical analysis employs experimental simulations of the algorithm on complex problems, gaining an insight on the algorithm’s practicality/applicability on real-world problems. With this regard, most of the time, methods proposed to solve MOPs are benchmarked on a different set of problems under arbitrary budgets of function evaluation. We are interested in empirically assessing published/novel multi-objective optimization algorithms in a unified (constantly updated) framework.
We invite the multi-objective community to test their published/novel algorithms in solving 100 MOPs reported in the literature where the feasible decision space has simple bound constraints, i.e., problems for which X=[l,u] and l<u. The benchmark validates the efficacy of the algorithms by computing several quality indicators which are reported in terms of data profiles.
IMPORTANT DATES
- Paper Submission Deadline: 15 August 2016
- Notification of Acceptance: 12 September 2016
- Final Paper Submission Deadline: 10 October 2016
Brief Description
- For a brief description of the platform and its experimental setup, please refer to this report.
Paper Submission:
- Please follow IEEE SSCI 2016 Submission Web Site (http://ssci2016.cs.surrey.ac.uk/Paper%20Submission.htm).
Getting Started with BMOBench
- Please refer to the guidelines given here.
References
A. Al-Dujaili and S. Suresh, “BMOBench: Black-box multiobjective optimization benchmarking platform,” ArXiv e-prints, vol. arXiv:1605.07009, 2016.
Custódio, Ana Luísa, et al. "Direct multisearch for multiobjective optimization." SIAM Journal on Optimization 21.3 (2011): 1109-1140.
Brockhoff, Dimo, Thanh-Do Tran, and Nikolaus Hansen. "Benchmarking numerical multiobjective optimizers revisited." Genetic and Evolutionary Computation Conference (GECCO 2015). 2015.