Simulated annealing optimization

The most popular optimization techniques are Newton-Raphson optimization, steepest ascent optimization, steepest descent optimization. Simplex optimization. Genetic Algorithm optimization, simulated annealing. - Variable reduction and - variable selection are also among the optimization techniques. 

Hajela, P. (1997). Stochastic search in discrete structural optimization—Simulated annealing, genetic algorithms and neural networks. In W. Gutkowski (Ed.), Discrete Structural Optimization. CISM International Centre for Mechanical Sciences. Vol. 373. 

Cunha, M. Sousa, J. (1999). Water distribution network design optimization Simulated annealing approach. Journal of Water Resources Planning and Management, 125 (4), 215-221. 

Note The segmentation operation yields a near-optimal estimate x that may be used as initialization point for an optimization algoritlim that has to find out the global minimum of the criterion /(.). Because of its nonlinear nature, we prefer to minimize it by using a stochastic optimization algorithm (a version of the Simulated Annealing algorithm [3]). 

This criterion resumes all the a priori knowledge that we are able to convey concerning the physical aspect of the flawed region. Unfortunately, neither the weak membrane model (U2 (f)) nor the Beta law Ui (f)) energies are convex functions. Consequently, we need to implement a global optimization technique to reach the solution. Simulated annealing (SA) cannot be used here because it leads to a prohibitive cost for calculations [9]. We have adopted a continuation method like the GNC [2]. 

Kirkpatrick S, Gelatt C D Jr and Vecchi M P 1983 Optimization by simulated annealing Science 220 671... 

J. Kalivas, Adaption of Simulated Annealing to Chemical Optimization Problems. Elsevier Science, New York, 1995. 

Je next introduce the basic algorithms and then describe some of the mmy variants upon lem. We then discuss two methods called evolutionary algorithms and simulated anneal-ig, which are generic methods for locating the globally optimal solution. Finally, we discuss jme of the ways in which one might cinalyse the data from a conformational malysis in rder to identify a representative set of conformations. 

Kirkpatrick S, C D Gelatt and M P Vecchi 1983. Optimization by Simulated Annealing. Science 220 671-680. 

Model optimization is a further refinement of the secondary and tertiary structure. At a minimum, a molecular mechanics energy minimization is done. Often, molecular dynamics or simulated annealing are used. These are frequently chosen to search the region of conformational space relatively close to the starting structure. For marginal cases, this step is very important and larger simulations should be run. 

Figure 3 A schematic representation of two optimization schemes, (a) Minimization, which leads to the closest local minimum (h) simulated annealing, which can overcome intermediate energy haiTiers along its path.

Alternative algorithms employ global optimization methods such as simulated annealing that can explore the set of all possible reaction pathways [35]. In the MaxFlux method it is helpful to vary the value of [3 (temperamre) that appears in the differential cost function from an initially low [3 (high temperature), where the effective surface is smooth, to a high [3 (the reaction temperature of interest), where the reaction surface is more rugged. 

Figure 5 Optimization of the objective function in Modeller. Optimization of the objective function (curve) starts with a random or distorted model structure. The iteration number is indicated below each sample structure. The first approximately 2000 iterations coiTespond to the variable target function method [82] relying on the conjugate gradients technique. This approach first satisfies sequentially local restraints, then slowly introduces longer range restraints until the complete objective function IS optimized. In the remaining 4750 iterations, molecular dynamics with simulated annealing is used to refine the model [83]. CPU time needed to generate one model is about 2 mm for a 250 residue protein on a medium-sized workstation.

To overcome the limitations of the database search methods, conformational search methods were developed [95,96,109]. There are many such methods, exploiting different protein representations, objective function tenns, and optimization or enumeration algorithms. The search algorithms include the minimum perturbation method [97], molecular dynamics simulations [92,110,111], genetic algorithms [112], Monte Carlo and simulated annealing [113,114], multiple copy simultaneous search [115-117], self-consistent field optimization [118], and an enumeration based on the graph theory [119]. 

Plumb AP, Rowe RC, York P, Doherty C. Effect of varying optimization parameters in optimization by guided evolutionary simulated annealing (GESA) using... 

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