A grid-enabled branch and bound algorithm for solving challenging combinatorial optimization problems

Solving optimally large instances of combinatorial optimization problems requires a huge amount of computational resources. In this paper, we propose an adaptation of the parallel Branch and Bound algorithm for computational grids. Such gridification is based on new ways to efficiently deal with some crucial issues, mainly dynamic adaptive load balancing, fault tolerance, global information sharing and termination detection of the algorithm. A new efficient coding of the work units (search sub-trees) distributed during the exploration of the search tree is proposed to optimize the involved communications. The algorithm has been implemented following a large scale idle time stealing paradigm (Farmer-Worker). It has been experimented on a Flow-Shop problem instance ( ) that has never been optimally solved. The new algorithm allowed to realize a success story as the optimal solution has been found with proof of optimality, within days using about processors belonging to Nation-wide distinct clusters (administration domains). During the resolution, the worker processors were exploited with an average of while the farmer processor was exploited only of the time. These two rates are good indicators on the efficiency of the proposed approach and its scalability

Investigating surrogate-based hybrid acquisition processes. Application to Covid-19 contact mitigation

International audienceSurrogate models are built to produce computationally efficient versions of time-complex simulation-basedobjective functions so as to address expensive optimization. In surrogate-assisted evolutionary computations,the surrogate model evaluates and/or filters candidate solutions produced by evolutionary operators. Insurrogate-driven optimization, the surrogate is used to define the objective function of an auxiliary optimizationproblem whose resolution generates new candidates. In this paper, hybridization of these two types ofacquisition processes is investigated with a focus on robustness with respect to the computational budget andparallel scalability. A new hybrid method based on the successive use of acquisition processes during the searchoutperforms competing approaches regarding these two aspects on the Covid-19 contact mitigation problem. Tofurther improve the generalization to larger ranges of search landscapes, another new hybrid method based onthe dispersion metric is proposed. The integration of landscape analysis tools in surrogate-based optimizationseems promising according to the numerical results reported on the CEC2015 test suite