Novel parallelization of simulated annealing and Hooke & Jeeves search algorithms for multicore systems with application to complex fisheries stock assessment models

Estimating parameters of a statistical fisheries assessment model typically involves a comparison of disparate datasets to a forward simulation model through a likelihood function. In all but trivial cases the estimations of these models tend to be time-consuming due to issues related to multi-modality and non-linearity. This paper develops novel parallel implementations of popular search algorithms, applicable to expensive function calls typically encountered in fisheries stock assessment. It proposes two versions of both Simulated Annealing and Hooke & Jeeves optimization algorithms with the aim of fully utilizing the processing power of common multicore systems. The proposals have been tested on a 24-core server using three different input models. Results indicate that the parallel versions are able to take advantage of available resources without sacrificing the quality of the solution.