45,022 research outputs found
Modified constrained differential evolution for solving nonlinear global optimization problems
Nonlinear optimization problems introduce the possibility of
multiple local optima. The task of global optimization is to find a point
where the objective function obtains its most extreme value while satisfying
the constraints. Some methods try to make the solution feasible
by using penalty function methods, but the performance is not always
satisfactory since the selection of the penalty parameters for the problem
at hand is not a straightforward issue. Differential evolution has
shown to be very efficient when solving global optimization problems
with simple bounds. In this paper, we propose a modified constrained
differential evolution based on different constraints handling techniques,
namely, feasibility and dominance rules, stochastic ranking and global
competitive ranking and compare their performances on a benchmark
set of problems. A comparison with other solution methods available in
literature is also provided. The convergence behavior of the algorithm to
handle discrete and integer variables is analyzed using four well-known
mixed-integer engineering design problems. It is shown that our method
is rather effective when solving nonlinear optimization problems.Fundação para a Ciência e a Tecnologia (FCT
Optimization of force-limiting seismic devices connecting structural subsystems
This paper is focused on the optimum design of an original force-limiting floor anchorage system for the seismic protection of reinforced concrete (RC) dual wall-frame buildings. This protection strategy is based on the interposition of elasto-plastic links between two structural subsystems, namely the lateral force resisting system (LFRS) and the gravity load resisting system (GLRS). The most efficient configuration accounting for the optimal position and mechanical characteristics of the nonlinear devices is obtained numerically by means of a modified constrained differential evolution algorithm. A 12-storey prototype RC dual wall-frame building is considered to demonstrate the effectiveness of the seismic protection strategy
On the Benefits of Surrogate Lagrangians in Optimal Control and Planning Algorithms
This paper explores the relationship between numerical integrators and
optimal control algorithms. Specifically, the performance of the differential
dynamical programming (DDP) algorithm is examined when a variational integrator
and a newly proposed surrogate variational integrator are used to propagate and
linearize system dynamics. Surrogate variational integrators, derived from
backward error analysis, achieve higher levels of accuracy while maintaining
the same integration complexity as nominal variational integrators. The
increase in the integration accuracy is shown to have a large effect on the
performance of the DDP algorithm. In particular, significantly more optimized
inputs are computed when the surrogate variational integrator is utilized
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