ARBP: antibiotic-resistant bacteria propagation bio-inspired algorithm and its performance on benchmark functions

Abstract

Optimization algorithms are continuously evolving and considered as an active multidiscipline research area to design scalable solutions for complex optimization problems. Literature witnesses the constant effort by researchers to improve existing optimization algorithms or to develop a new algorithm to deal with single and multiple objective problems. This research paper presents a novel population-based, metaheuristic bio-inspired optimization algorithm. The algorithm contrived the propagation concept of antibiotic-resistant bacteria named as antibiotic-resistant bacteria propagation (ARBP) algorithm where properties of bacteria to acquire antibiotic resistance over time are used as a base concept. The optimization algorithm imitates the two prime mechanisms of horizontal gene transfer—Conjugation Gene Transfer Mechanism (CGTM) and Transformation Gene Transfer Mechanism (TGTM) to propagate antibiotic-resistant bacteria. CGTM and TGTM are used to explore the search space to handle single and multiple objective optimization problems. Conjugation mechanism is used for exploration of search space and exploitation concept is driven by transformation mechanism. The efficiency and importance of the ARBP algorithm are validated on varying classical and complex benchmark functions. An extensive comparative study is performed to detail the effectiveness of ARBP over other well-known swarm and evolutionary algorithms. This comparative analysis clearly depicts that the performance of ARBP is superior in terms of finding a better solution with high convergence as compared to other considered algorithms.