A particle swarm pattern search method for bound constrained global optimization

By A. F. Vaz, Luís Nunes Vicente. Journal of Global Optimization, 2007

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A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines (2013, methodology): “PSwarm, a hybrid algorithm for global minimization introduced in [40], [41], combines a heuristic approach for global optimization (particle swarm) with a rigorous method (pattern search) for local minimization.”
Derivative-free optimization: a review of algorithms and comparison of software implementations (2012, methodology): “2 that the stochastic solvers CMA-ES, DAKOTA/EA, and PSWARM perform a rather large number of function evaluations that cover the entire search space, while local search algorithms terminate quickly after improving the solution of the starting point locally.”
Direct Multisearch for Multiobjective Optimization (2011, methodology): “Such techniques could be easily fitted into DMS by means of an appropriate search step (such as a swarm search step; see [45, 46] for m = 1) or by selecting the poll centers using a spread metric (as reported in Section 6.”
A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls (2014, methodology): “our framework: MADS [4, 32], PSO [15], and the PSO– MADS hybrid method, which is a variant of the PSwarm algorithm introduced by Vaz and Vicente [41].”