Dynamically swarm shared mutation based bacterial foraging
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ORIGINAL RESEARCH
Dynamically swarm shared mutation based bacterial foraging Renu Nagpal1
•
Parminder Singh2 • B. P. Garg1
Received: 4 December 2019 / Accepted: 3 October 2020 Ó Bharati Vidyapeeth’s Institute of Computer Applications and Management 2020
Abstract In this paper, dynamically swarm shared mutation based Bacterial Foraging (DSSBFO) is proposed to optimize multidimensional, unimodal and multimodal functions. In BFO, due to fixed step size it requires more computational cost to get optimum solution with better accuracy. Chemotaxis and reproduction step of BFO are not sufficient for an effective search. So in this paper, the authors propose dynamic step size in BFO to achieve optimum solution with better accuracy with minimum cost. The dynamic step size i.e. mutation is achieved by modifying the position equation of GLBestPSO and momentum factor (mc) of SSMPSO used in modified equation to bring the bacteria in search space and not to cross the boundary of search space. The eight standard benchmark functions are used to prove the performance of DSSBFO in terms of precision and cost. DSSBFO performs well as compared to BFO and BSO (BFO hybridized with PSO) alogrithms interms of quality solution with faster convergence. Keywords Dynamically swarm shared mutation based bacterial foraging (DSSBFO) Benchmark functions Convergence
& Renu Nagpal [email protected] Parminder Singh [email protected] B. P. Garg [email protected] 1
IKG Punjab Technical University, Jalandhar, India
2
Department of Information Technology, Chandigarh Engineering College, Landran, India
1 Introduction Evolutionary Optimization techniques are popularly used in engineering to optimize multiobjective, multimodal, multidimensional and nonlinear function. Many optimization techniques proposed by researchers require more computational cost or produces infeasible solution. The accuracy of global optima with minimum cost is challenges for researchers to develop new algorithms. The Genetic algorithm (GA) ruled several years for solving complex real world problems [1]. Apart from Genetic algorithm (GA), new paradigms have been developed to solve unimodal and multimodal real word problems. Particle Swarm Optimization (PSO) [2–4], GLBestPSO, SSMPSO and Bacteria Foraging Optimization (BFO) [5] are proposed by researchers in recent years. New dimension of soft computing i.e. BFO and its variant i.e. BSO (BFO hybridized with PSO), is used to address multimodal and high dimensional optimization function, [6, 7]. A hybrid GA and BFO method work well with numerical benchmarks function and PID tuner design [8]. Adaptive BFOs (ABFOA1 and ABFOA2) outperformed well than PSO, GA, classical BFO and BSO [9, 10]. Unfortunately, BFO due to fixed step size performs poor on difficult benchmark functions like multidimensional, unimodal and multimodal in comparison to other techniques, such as GAs, PSO and DE [9, 10]. The recent variants of BFO and PSO are Chaos Enhanced BFO [11], Co evolutionary Structure-Redesigned-Based BFO [12], impr
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