An Improved Ant Colony Optimisation Algorithm for the 2D HP Protein Folding Problem

Abstract

Abstract. The prediction of a protein's structure from its amino-acid sequence is one of the most important problems in computational biology. In the current work, we focus on a widely studied abstraction of this problem, the 2-dimensional hydrophobic-polar (2D HP) protein folding problem. We present an improved version of our recently proposed Ant Colony Optimisation (ACO) algorithm for this £ ¥ ¤ -hard combinatorial problem and demonstrate its ability to solve standard benchmark instances substantially better than the original algorithm; the performance of our new algorithm is comparable with state-of-the-art Evolutionary and Monte Carlo algorithms for this problem. The improvements over our previous ACO algorithm include long range moves that allows us to perform modification of the protein at high densities, the use of improving ants, and selective local search. Overall, the results presented here establish our new ACO algorithm for 2D HP protein folding as a state-of-the-art method for this highly relevant problem from bioinformatics.

An On/Off Lattice Approach to Protein Structure Prediction from Contact Maps

“…It makes use of a very effective stochastic search procedure, named Replica Exchange Monte Carlo (REMC for short) that is especially geared towards high-dimensional optimization problems. REMC-HPPFP has been shown to lead to superior results with respect to competing methods, such as PERM [5] and ACO-HPPFP-3 [13] in a set of synthetic and on biologically-derived benchmark instances [15]. The core features are the REMC optimization heuristic and the set of moves used to perform the search itself.…”

GPU Accelerated Nature Inspired Methods for Modelling Large Scale Bi-directional Pedestrian Movement

“…This allows the pedestrian to move towards their goal along the shortest path possible requiring the least effort to achieve their target. Subsequently, a variant of Ant Colony Optimization (ACO) [16]- [18] is also used to modify an agent's or After its initial development, ACO was first used to provide solutions to the travelling salesman problem [19] and has since then been adapted to several applications areas, such as vehicle routing [20], circuit design [21] and recently in complex applications like protein folding [22]. The pedestrian movement can also be modelled as ants, considering each pedestrian as an ant or agent that deposits a pheromone trail in the sense of signaling to following agents that the chosen path may be desirable.…”

Flexible Protein Folding by Ant Colony Optimization

“…The performance of the proposed FAC algorithm is compared with that of existing algorithms presented in [36], [38] and [43], which are the conventional Monte Carlo (EMC) algorithm, the genetic algorithm (GA) [36], the ant colony optimization (ACO) algorithm [38], and the immune algorithm (IA) [43]. The reason for choosing these algorithms is that their models and tests are the same as the ones used in this chapter.…”

“…Table 3 compares the best time (BestT), the best energy evaluations (B.E.E), and the best number of iterations (B.N.I) among the FAC, the EMC, the GA, and the IA. [36], [38] or [43].…”

“…In the proposed FAC algorithm, there is heuristic information for folding polar amino acids, which is different from that used in the ACO algorithm [38]. The performance of the FAC algorithm is compared with or without heuristic information to polar amino acids.…”