Globally convergent evolution strategies for constrained optimization

Y. Diouane (), S. Gratton () and L. Vicente ()

Computational Optimization and Applications, 2015, vol. 62, issue 2, 323-346

Abstract: In this paper we propose, analyze, and test algorithms for constrained optimization when no use of derivatives of the objective function is made. The proposed methodology is built upon the globally convergent evolution strategies previously introduced by the authors for unconstrained optimization. Two approaches are encompassed to handle the constraints. In a first approach, feasibility is first enforced by a barrier function and the objective function is then evaluated directly at the feasible generated points. A second approach projects first all the generated points onto the feasible domain before evaluating the objective function. The resulting algorithms enjoy favorable global convergence properties (convergence to stationarity from arbitrary starting points), regardless of the linearity of the constraints. The algorithmic implementation (i) includes a step where previously evaluated points are used to accelerate the search (by minimizing quadratic models) and (ii) addresses the particular cases of bounds on the variables and linear constraints. Our solver is compared to others, and the numerical results confirm its competitiveness in terms of efficiency and robustness. Copyright Springer Science+Business Media New York 2015

Keywords: Evolution strategies; Constrained optimization; Global convergence; Extreme barrier function; Projection; Search step; Quadratic models; Bound and linear constraints (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10589-015-9747-3

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