Tabu Search Based Strategies for Conformational Search

Abstract

This paper presents an application of the new nonlinear global optimization routine gradient only tabu search (GOTS) to conformational search problems. It is based on the tabu search strategy which tries to determine the global minimum of a function by the steepest descent-modest ascent strategy. The refinement of ranking procedure of the original GOTS method and the exploitation of simulated annealing elements are described, and the modifications of the GOTS algorithm necessary to adopt it to conformation searches are explained. The utility of the GOTS for conformational search problems is tested using various examples.

Empirical Review of Standard Benchmark Functions Using Evolutionary Global Optimization

“…We assume the rather high number of global optimization steps (also in comparison to the non-locopt case) to be due to a repeated finding of the same, non- optimal minima. Inclusion of taboo-search features [29] into the algorithm might be of help for real-world problems of such a type, not reducing the number of global optimization steps but the amount of time spent in local optimizations rediscovering already known minima. Just as Ackley's function discussed earlier, it is possible to conclude that Rastrigin's function should be solvable with almost linear scaling using contemporary algorithms.…”

A Variable Neighbourhood Descent Heuristic for Conformational Search Using a Quantum Annealer

“…To address the computational complexity presented by larger molecules, many metaheuristic approaches have been studied. Examples of such approaches include genetic algorithms [16,17], conformational space annealing [18,19], tabu search [20,21], molecular dynamics (MD) [22,23], and basin/funnel hopping [24]. Variations of the Monte Carlo (MC) method have also been widely used [25,26] as a less computationally expensive alternative to MD [8].…”

Gradient gravitational search: An efficient metaheuristic algorithm for global optimization

“…A good combination between intensification and diversification ensures the selection of the best solutions to improve the rate of algorithm convergence and its global optimality. [20] Metaheuristic optimization algorithms include the ability to reach a problems global minimum value at an early with a reduce number of control parameters at low computational cost, as well as robustness and wide application to several different problems based on Chemistry and Physics, [21][22][23][24][25][26] based on Engineering Design. [27][28][29][30][31][32][33][34][35][36][37][38][39] It is also quite evident that algorithms may not exactly fit into the mentioned requisites efficiently.…”