Discrete bacteria foraging optimization algorithm for graph based problems - a transition from continuous to discrete

• Published in: Journal of experimental & theoretical artificial intelligence Vol. 30; no. 2; pp. 345 - 365
• Main Authors: Sur, Chiranjib, Shukla, Anupam
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 04.03.2018; Taylor & Francis Ltd
• Subjects: Algorithms; Ant colony optimization; Bacteria; Computer simulation; discrete problems; Exploration; Forage; graph problems; Graphical representations; Mathematical models; multi-objective optimisation; Multiple objective analysis; Road network optimisation; route formation; Water drops
• ISSN: 0952-813X; 1362-3079
• DOI: 10.1080/0952813X.2017.1421266

Bacteria Foraging Optimisation Algorithm is a collective behaviour-based meta-heuristics searching depending on the social influence of the bacteria co-agents in the search space of the problem. The algorithm faces tremendous hindrance in terms of its application for discrete problems and graph-based problems due to biased mathematical modelling and dynamic structure of the algorithm. This had been the key factor to revive and introduce the discrete form called Discrete Bacteria Foraging Optimisation (DBFO) Algorithm for discrete problems which exceeds the number of continuous domain problems represented by mathematical and numerical equations in real life. In this work, we have mainly simulated a graph-based road multi-objective optimisation problem and have discussed the prospect of its utilisation in other similar optimisation problems and graph-based problems. The various solution representations that can be handled by this DBFO has also been discussed. The implications and dynamics of the various parameters used in the DBFO are illustrated from the point view of the problems and has been a combination of both exploration and exploitation. The result of DBFO has been compared with Ant Colony Optimisation and Intelligent Water Drops Algorithms. Important features of DBFO are that the bacteria agents do not depend on the local heuristic information but estimates new exploration schemes depending upon the previous experience and covered path analysis. This makes the algorithm better in combination generation for graph-based problems and combination generation for NP hard problems.