Optimization algorithm performance

Table 4.1 Summary of single-trajectory optimization algorithm performance.

Sariff, N. B., Buniyamin, N. (2009). Comparative study of genetic algorithm and ant colony optimization algorithm performances for robot path planning in global static environments of different complexities. In Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), (pp. 132-137). Mara, Malaysia IEEE Press. 

A recent development uses the quadratic synchronous transit approach to get close to a transition state, and then a Newton or eigenvector-following algorithm to complete the optimization. It performs optimizations in redundant internal coordinates. The key reference is due to Peng, Ayala and Schlegel. 

The multilevel coordinate search (MGS) method was used to optimize parameters to get best fit between the experimental and predicted intensities. The NOE R-factor was used as the energy function to be minimized. A version of the MGS method was written based on the version presented by Huyer and Neumaier [65]. The algorithm performs the minimization by a standard coordinate search method. The method was carefully tested by the use of different starting points for the coordinate search and using simulated data sets. This alleviates local minima trapping by MGS, and identifies fhe global minimum within the parameter ranges used in the optimization. 

The data shown in this section demonstrate that the simultaneous optimization of the solute geometry and the solvent polarization is possible and it provides the same results as the normal approach. In the case of CPCM it already performs better than the normal scheme, even with a simple optimization algorithm, and it will probably be the best choice when large molecules are studied (when the PCM matrices cannot be kept in memory). This functional can thus be directly used to perform MD simulations in solution without considering explicit solvent molecules but still taking into account the dynamics of the solvent. On the other hand, the DPCM functional presents numerical difficulties that must be studied and overcome in order to allow its use for dynamic simulations in solution. 

The program is reported to carry out simple Hiickel molecular orbital calculations to determine the relative sensitivity of aromatic carbon atoms to oxidation and the relative stability of keto and enol tautomers. Klopman et al. (1999) have reported that for polycyclic aromatic hydrocarbons, adequate reactivity is an essential but not sufficient condition for enzyme catalyzed reaction. The accessibility of the reactive site (i.e., the absence of steric hindrance) was also found to be important. Genetic algorithms have been used to optimize the performance of the biotransformation dictionary by treating the initial priority scores set by expert assessment as adjustable parameters (Klopman et al., 1997). 

Of course, as mentioned earlier, the mere development of more efficient optimization algorithms could indirectly improve performance. This could happen, for example, through the use of nonlinear instead of linear models in on-line optimization. As stated in the Introduction, numerical efficiency issues are beyond the scope of this discussion. 

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