Topographical Global Optimization

Törn, Aimo; Viitanen, Sami

doi:10.1515/9781400862528.384

Cited by 95 publications

(130 citation statements)

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“…Stochastic approaches, encompass among others simulated annealing [26], genetic algorithms [10], [3], and clustering methods [25]. A number of books [23], [30], [24], [16], [5], [11] summarize the latest developments in the area.…”

Section: Introductionmentioning

confidence: 99%

?BB: A global optimization method for general constrained nonconvex problems

1995

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Abstract.A branch and bound global optimization method, BB, for general continuous optimization problems involving nonconvexities in the objective function and/or constraints is presented. The nonconvexities are categorized as being either of special structure or generic. A convex relaxation of the original nonconvex problem is obtained by (i) replacing all nonconvex terms of special structure (i.e. bilinear, fractional, signomial) with customized tight convex lower bounding functions and (ii) by utilizing the parameter as defined in [17] to underestimate nonconvex terms of generic structure. The proposed branch and bound type algorithm attains finite -convergence to the global minimum through the successive subdivision of the original region and the subsequent solution of a series of nonlinear convex minimization problems. The global optimization method, BB, is implemented in C and tested on a variety of example problems.

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Section: Introductionmentioning

confidence: 99%

?BB: A global optimization method for general constrained nonconvex problems

1995

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“…Each planning system recommends the next view for the tracking system in the form of a set of actions. The optimal action set is planned with the global optimization technique of Adaptive Random Search [15], evaluating a total of 400 separate action sequences per time frame. Each action sequence contains the next actions for each camera for the next time steps.…”

Section: Methodsmentioning

confidence: 99%

Multi-step Multi-camera View Planning for Real-Time Visual Object Tracking

Deutsch

Wenhardt

Niemann

2006

Lecture Notes in Computer Science

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Abstract. We present a new method for planning the optimal next view for a probabilistic visual object tracking task. Our method uses a variable number of cameras, can plan an action sequence several time steps into the future, and allows for real-time usage due to a computation time which is linear both in the number of cameras and the number of time steps. The algorithm can also handle object loss in one, more or all cameras, interdependencies in the camera's information contribution, and variable action costs. We evaluate our method by comparing it to previous approaches with a prerecorded sequence of real world images.From K. Franke et al., Pattern Recognition, 28th DAGM Symposium, Springer, 2006, (pp. 536-545).

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“…The motivation for these hybrids is that these methods could decompose the search by allowing the EA to globally sample across the range of possible docking configurations while the local search method quickly minimizes points to find locally optimal configurations. Hart, Kammeyer and Belew (8, 101 argue that these types of hybrid EAs are better global optimizers than either EAs or local search separately, and Torn and iilinskas [22] note that mcst successful global optimization methods also apply the same principle of distinguishing the mechanisms for global and local search.…”

Section: Stochastic Competition Generates Ft From Pt-lmentioning

confidence: 99%

Improved Evolutionary Hybrids for Flexible Ligand Docking in AutoDock

Hart¹,

Rosin²,

Belew³

et al. 2000

Nonconvex Optimization and Its Applications

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In this paper we evaluate the design of the hybrid EAs that are currently used to perform flexible ligand binding in the Autodock docking software. Hybrid evolutionary algorithms (E&) incorporate specialized operators that exploit domain-specific features to accelerate an EA'S search. We consider hybrid EAs that use an integrated local search operator to r e h e individuals within each iteration of the search. We evaIuate several factors that impact the efficacy of a hybrid EA, and we propose new hybrid EAs that provide more robust convergence to low-energ d d i n g configurations than the methods currently available in Autodock.

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Topographical Global Optimization

Cited by 95 publications

References 0 publications

?BB: A global optimization method for general constrained nonconvex problems

?BB: A global optimization method for general constrained nonconvex problems

Multi-step Multi-camera View Planning for Real-Time Visual Object Tracking

Improved Evolutionary Hybrids for Flexible Ligand Docking in AutoDock

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