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

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 algorith...

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Published inAdvances in computational intelligence Vol. 4; no. 3; p. 10
Main Authors Aggarwal, Kirti, Arora, Anuja
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.09.2024
Springer Nature B.V
Subjects
Antibiotics
Artificial Intelligence
Bacteria
Benchmarks
Chemistry
Comparative studies
Computational Intelligence
Conjugation
Design optimization
Drug resistance
Engineering
Evolutionary algorithms
Exploitation
Foraging behavior
Genetic algorithms
Heuristic methods
Machine Learning
Multiple objective analysis
Optimization algorithms
Original Article
Physics
Propagation
Metaheuristic
Optimization algorithm
Antibiotic-resistant bacteria
Evolutionary algorithm
Bio-inspired algorithm
Online AccessGet full text
ISSN2730-7794
2730-7808
DOI10.1007/s43674-024-00077-3

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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.
AbstractList 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.
ArticleNumber 10
Author Aggarwal, Kirti
Arora, Anuja
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Snippet Optimization algorithms are continuously evolving and considered as an active multidiscipline research area to design scalable solutions for complex...
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SubjectTerms Antibiotics
Artificial Intelligence
Bacteria
Benchmarks
Chemistry
Comparative studies
Computational Intelligence
Conjugation
Design optimization
Drug resistance
Engineering
Evolutionary algorithms
Exploitation
Foraging behavior
Genetic algorithms
Heuristic methods
Machine Learning
Multiple objective analysis
Optimization algorithms
Original Article
Physics
Propagation
Title ARBP: antibiotic-resistant bacteria propagation bio-inspired algorithm and its performance on benchmark functions
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