Improved Global Convergence Probability Using Independent Swarms
Jaco F. Schutte, Raphael T. Haftka · 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference · 2005
In global optimization it may sometimes be more efficient to perform multiple independent optimization runs with a limited number of fitness evaluations in place of a single run with an equal number of total fitness evaluations. This is especially true for problems with a number of widely separated local optima in the design space because a single optimization has a larger probability of becoming entrapped in a local optimum than multiple independent runs. This approach can be further exploited by utilizing parallel computation. Some preliminary results of an investigation on the feasibility of multiple independent optimizations are reported. I. Nomenclature Pi = individual optimization global convergence probability Pc = multiple optimization cumulative global convergence probability Cr = convergence ratio N = number of optimization runs Nc = number of globally converged optimization runs nfe = number of fitness evaluations nb = budget of fitness evaluations allocated to solving problem ni = allowed fitness evaluations for each independent optimization nl = number of fitness evaluations required by optimization algorithm to find minima se = standard error