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Gradient tabu search.

Svetlana Stepanenko¹, Bernd Engels

¹Institut für Organische Chemie, Universität Würzburg, Am Hubland, D-97070 Würzburg, Germany.

Journal of Computational Chemistry

|December 23, 2006

Summary

Gradient Tabu Search (GTS) enhances optimization by using analytical gradients and Hessian elements to find local and global minima efficiently. This novel approach effectively blocks visited areas, showing promise for complex molecular and parameter optimization tasks.

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Area of Science:

Computational Chemistry
Optimization Algorithms
Mathematical Modeling

Background:

Local and global minima identification is crucial in various scientific domains.
Traditional optimization methods can struggle with complex, high-dimensional functions.
Existing tabu search algorithms may require enhancements for efficiency and effective search space exploration.

Purpose of the Study:

To introduce a novel optimization algorithm, Gradient Tabu Search (GTS).
To enhance the efficiency of finding local and global minima for differentiable functions.
To improve the blocking of previously explored search space regions.

Main Methods:

Modification of the tabu search algorithm.
Incorporation of analytical gradients for rapid minimization to local minima.
Utilization of analytical diagonal Hessian elements to escape local minima.
Introduction of tabu regions and tabu directions into the tabu list (TL) for efficient blocking.

Main Results:

GTS demonstrates a promising approach for determining local and global minima.
The method shows effectiveness on various well-known test functions.
Efficient blocking of visited areas is achieved through the enhanced tabu list.

Conclusions:

Gradient Tabu Search (GTS) offers a powerful new tool for optimization problems.
The algorithm's ability to efficiently find minima and avoid cycles makes it suitable for complex functions.
Potential applications include optimizing force field parameters and conformational searches in large molecules.

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