Problem-oriented software package for the numerical solution and research of structural bioinformatics problems using stochastic optimization methods

In this paper presented problem-oriented software package for performing computational experiments in structural bioinformatics problems: protein structure prediction and peptide-protein docking. These problemsare formulated as continuous global optimization tasks. The primary purpose of the presented software package is to provide functionality for performing computational experiments using various stochastic optimization methods. To perform experiments for the selected task the objective function and search space are provided for user. In this work the software packagefunctionality, implementation features and the results of various experimentsare presented. The software is written in C++ and provides the possibility ofusing parallel computing using OpenMP technology. The presented package is open source software that stored in the GitHub repositories.

Influence of a neighborhood shape on the efficiency of continuous variable neighborhood search

The efficiency of a Variable neighborhood search metaheuristic for continuous global optimization problems greatly depends on geometric shape of neighborhood structures used by the algorithm. Among the neighborhoods defined by balls in ?p, 1 ?p ? ? metric, we tested the ?1, ?2, and ?? ball shape neighborhoods, for which there exist efficient algorithms for obtaining uniformly distributed points. On many challenging high-dimensional problems, our exhaustive testings showed that, popular and the easiest for implementation, ?? ball shape of neighborhoods performed the worst, and much better efficiency was obtained with ?1 and ?2.

Convergence analysis of the plant propagation algorithm for continuous global optimization

The Plant Propagation Algorithm (PPA) is a Nature-Inspired stochastic algorithm, which emulates the way plants, in particular the strawberry plant, propagate using runners. It has been experimentally tested both on unconstrained and constrained continuous global optimization problems and was found to be competitive against well established algorithms. This paper is concerned with its convergence analysis. It first puts forward a general convergence theorem for a large class of random algorithms, before the PPA convergence theorem is derived and proved. It then illustrates the results on simple problems.

Parallel Evolutionary Optimization Algorithms for Peptide-Protein Docking

In this study we examine the possibility of using evolutionary optimization algorithms in protein-peptide docking. We present the main assumptions that reduce the docking problem to a continuous global optimization problem and provide a way of using evolutionary optimization algorithms. The Rosetta all-atom force field was used for structural representation and energy scoring. We describe the parallelization scheme and MPI/OpenMP realization of the considered algorithms. We demonstrate the efficiency and the performance for some algorithms which were applied to a set of benchmark tests.

A Weaker Sufficient Condition for the Chaoticity of Extended Perturbation-Based Updating System for Global Optimization

Recently, the gradient method with perturbation (GP) was proposed for metaheuristic methods of solving continuous global optimization problems. Its updating system based on the steepest descent method is chaotic because of its perturbations along the standard basis vectors, which can strengthen the diversification of search. The sufficient condition for its chaoticity was theoretically shown, which implies that two kinds of influence degrees of the perturbations in the updating system should be greater than some constants. In this paper, we extend the updating system of the GP into a more general one for metaheuristic methods, which does not necessarily require the descent direction of the objective function, and which can have perturbations along appropriate orthogonal basis vectors for each problem. Furthermore, since the condition for the chaoticity shown in the previous work is too restricted for practical use, we theoretically show a weaker sufficient condition for the extended system, which is derived by varying the constant lower bounds for the two kinds of influence degrees.