Bayesian Optimization Approaches for Massively Multi-modal Problems

The optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the op- timization process to local optima. While evolutionary algorithms have been extensively investigated for these optimization problems, Bayesian Optimization algorithms have not been explored to the same extent. In this paper, we study the behavior of Bayesian Optimization as part of a hybrid approach for solving several massively multi-modal functions. We use well-known benchmarks and metrics to evaluate how different variants of Bayesian Optimization deal with multi-modality.TIN2016-78365-

Electrical Energy Storage In Transportation Systems

International audienc

Comparative study of stochastic optimization methods to solve load allocation problem for aircraft electrical power system design

International audienceDesigners of aircraft electrical power system must allocate electrical systems to the electrical power system busbars. Due to the high number of possible allocations and regarding the large diversity of potential sizing situations, finding the best allocation in order to optimize the electrical power system mass is a difficult task. In this work, several stochastic combinatorial optimization methods are investigated for solving the allocation problem in the context of the more-electrical aircraft

Robust and Efficient Genetic Algorithms with Hierarchical Niching and a Sustainable Evolutionary Computation Model

Abstract. This paper proposes a new niching method named hierarchical niching, which combines spatial niching in search space and a continuous temporal niching concept. The method is naturally implemented as a new genetic algorithm, QHFC, under a sustainable evolutionary computation model: the Hierarchical Fair Competition (HFC) Model. By combining the benefits of the temporally continuing search capability of HFC and this spatial niching capability, QHFC is able to achieve much better performance than deterministic crowding and restricted tournament selection in terms of robustness, efficiency, and scalability, simultaneously, as demonstrated using three massively multimodal benchmark problems. HFC-based genetic algorithms with hierarchical niching seem to be very promising for solving difficult real-world problems.