Artificial Ant System Research Articles

A causal, time-independent synchronization pattern for collective adaptive systems

Artificial ants are “small” units, moving autonomously on a shared, dynamically changing “space”, directly or indirectly exchanging some kind of information. Artificial ants are frequently conceived as a paradigm for collective adaptive systems. In this paper, we discuss means to represent continuous moves of “ants” in discrete models. More generally, we challenge the role of the notion of “time” in artificial ant systems and models. We suggest a modeling framework that structures behavior along causal dependencies rather than temporal relations. We present all arguments with the help of a simple example. As a modeling framework we employ Heraklit; an emerging framework that has already proven its worth in many contexts. Different concrete collective systems share similar features, despite differences in the size of basic sets, concrete values of functions, etc., and can therefore be conceived as instantiations of a single schema. Hence, we need a representation of systems on the schematic level.

Ant Colony System: An Approach for the Design of a Single Database to Establish Any Node-to-node Connectivity for Robot Path Planning in a Robot Colony

In Ant Colony System, the agents or the ants travel in search of foods by following one another that is they show the swarming behavior along the searching path of the food source and return path towards the hive. In the artificial ant system or the robot system, the same operation of the robot can be observed by the suitable design of the travelling path. The path design and the travelling technique are already proposed in my two previous works. Those designs include different types of algorithms for different opeartions. Kruskel’s algorithm is applied to find the shortest path in between any two nodes, one is the source and another is the destination node. But the disadvantage of this design is the longer simulation time as three algorithms is required to travel from one node to another node and naturally several numbers of the databases are required to hold all the information about the entire robot colony. In this paper, a single database is proposed that can hold all the connectivity and which can be driven with a single algorithm. The proposed method is compared with the other previous techniques of finding the shortest path and the result set represented here showing the efficiency of the proposed method.

A novel locally guided genome reassembling technique using an artificial ant system

DNA reassembling is an NP-hard problem (Brun, Theor Comput Sci 395:31---46, 2008; Medvedev et al 2007; Ma and Lombardi 2008). The present article presents a locally guided global learning system to solve the problem of genome reassembling. We have used a reference DNA sequence which is 99 % similar to an unknown DNA sequence. Two different sequences from the same organism generally have around 99 % similarity (Wei et al 2007). We have considered different DNA sequences from NCBI website (http://www.ncbi.nlm.nih.gov). Then we have simulated the tasks of cloning the sequence, followed by shearing the clones to a number of short reads. In our algorithm, we have introduced a new concept in the task of DNA reassembling using Ant Colony Optimization, where pheromone concentration is proportional to the score of assembled DNA fragments with some known reference sequences within the same organism. Unlike local overlapping, we have used here local alignment score of short reads with some known local reference region as the heuristic information. The result shows that our algorithm is capable of reassembling at par with the state-of-the-art. DNA reassembling techniques may need a massive parallel computation and huge memory space (Kurniawan et al 2008) because of size ~109bp of DNA sequences of mammals (Miller et al, Genomics 95:315---327, 2010; Blazewicz et al, Comput Biol Chem 33:224---230, 2009; Butler et al, Genome Res 18:810---820, 2008; Joshi et al 2011; Stupar et al, Arch Oncol 19:3---4, 2011; Quail et al, BMC Genomics 13:1471---2164, 2012), and ACO is inherently concurrent in nature (Dorigo and Stutzle 2004). Due to lack of appropriate computational resources, we had to confine ourselves to deal with the sequences of length up to ~105bp. We have considered 22 sequences of different organism, including Homo sapiens BRCA1 (127429bp) gene. For large sequences, we have applied hierarchical BAC-by-BAC sequencing (Fig. 2) (Myers, Comput Sci Eng 1:33---43, 1999), to stitch the individual segments to retrieve the original DNA sequence.

Combinatorial Chemistry by Ant Colony Optimization

Prioritizing building blocks for combinatorial medicinal chemistry represents an optimization task. We present the application of an artificial ant colony algorithm to combinatorial molecular design (Molecular Ant Algorithm [MAntA]). In a retrospective evaluation, the ant algorithm performed favorably compared with other stochastic optimization methods. Application of MAntA to peptide design resulted in new octapeptides exhibiting substantial binding to mouse MHC-I (H-2K(b)). In a second study, MAntA generated a new functional factor Xa inhibitor by Ugi-type three-component reaction. This proof-of-concept study validates artificial ant systems as innovative computational tools for efficient building block prioritization in combinatorial chemistry. Focused activity-enriched compound collections are obtained without the need for exhaustive product enumeration.

Design of MHC I stabilizing peptides by agent-based exploration of sequence space

Identification of molecular features that determine peptide interaction with major histocompatibility complex I (MHC I) is essential for vaccine development. We have developed a concept for peptide design by combining an agent-based artificial ant system with artificial neural networks. A jury of feedforward networks classifies octapeptides that are recognized by mouse MHC I protein H-2K(b). Prediction accuracy yielded a correlation coefficient of 0.94. Peptides were designed in machina by the artificial ant system and tested in vitro for their MHC I stabilizing effect. The behavior of the search agents during the design process was controlled by the jury network. The experimentally determined prediction accuracy was 89% for the designed stabilizing and 95% for the non-stabilizing peptides. Novel H-2K(b) stabilizing peptides were conceived that reveal extensions of known residue motifs. The combined network-agent system recognized context dependencies of residue positions. A diverse set of novel sequences exhibiting substantial activity was generated.

Group technology by an ant system algorithm

Clustering of items to form meaningful groups is a theoretically challenging problem. Moreover, it has considerable practical value in manufacturing. Therefore, extensive research is conducted in this field and numerous techniques have been developed. The starting point of these techniques is usually part–machine incidence matrices. This data structure models the cell formation problem as well. Here, the aim is to get a block-diagonalized structure. This is the basic problem of group technology. This paper presents a novel and potent technique to solve this basic problem. The grouping problem is first represented as an artificial ant system. Then better and better groupings are obtained as semi-blind ants find their way by a communication-supported random search process. Finally, the proposed technique is compared with other AI methodologies, namely genetic algorithms, simulated annealing and tabu search. The main concern in this evaluation phase was to devise an environment appropriate for a fair assessment. For that reason, the stated techniques are formulated with the simplest possible configurations and parallel structures. Tests made using the well-known data sets from the literature revealed a remarkable outcome: ant systems perform better than the other AI techniques as far as an equal number of solution alternatives are concerned.