Adaptation Schemes of Chemotactic Step Size of Bacterial Foraging Algorithm for Faster Convergence

Supriyono, Heru; Tokhi, M. O.

doi:10.3923/jai.2011.207.219

Cited by 8 publications

(8 citation statements)

References 10 publications

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“…The process in which bacteria move and search for food to increase energy levels is called chemotaxis. Swimming and tumbling are methods of chemotaxis, which describe the movement in two different directions [53,54]. Swimming: In every bacterial cell (BC) action, the bacteria numbered as {1,2,…, i} move and the bacteria {i + 1, i + 2, …, snnum} do not move.…”

Section: Proposed Ch Selection Algorithm Based Bacterial Foraging mentioning

confidence: 99%

Bacteria Interactive Cost and Balanced-Compromised Approach to Clustering and Transmission Boundary-Range Cognitive Routing In Mobile Heterogeneous Wireless Sensor Networks

Yalçın

Erdem

2019

Sensors

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The improvement of stable, energy-efficient mobile-based clustering and routing protocols in wireless sensor networks (WSNs) has become indispensable so as to develop large-scale, versitale, and adaptive applications. Data is gathered more efficiently and the total path length is shortened optimally by means of mobile sink (MS). Two algorithms as bacterial interaction based cluster head (CH) selection and energy and transmission boundary range cognitive routing algorithm with novel approach for heterogeneous mobile networks are proposed in this study. The more reliable and powerful CH selection is made with the greedy approach that is based on the interaction fitness value, energy node degree, and distance to adjacent nodes in a compromised manner. The best trajectories, thanks to intersection edge points of the visited CHs, are obtained in the proposed routing algorithm. In this way, the MS entry to transmission range boundaries of the CH has been a sufficient strategy to collect information. As in energy model, we adopt energy consumption costs of listening and sensing channel as well as transmit and receive costs. Comprehensive performance analyzes have been seriously carried out via the Matlab 2016a environment. We validate that the proposed scheme outperforms existing studies in terms of several performance metrics as simulations.

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Section: Proposed Ch Selection Algorithm Based Bacterial Foraging mentioning

confidence: 99%

Bacteria Interactive Cost and Balanced-Compromised Approach to Clustering and Transmission Boundary-Range Cognitive Routing In Mobile Heterogeneous Wireless Sensor Networks

Yalçın

Erdem

2019

Sensors

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“…Moreover, Gaussian membership function is smooth and concise, which can represent uncertainty in measurement more effectively. The Mamdani-type with centre of area defuzzification method is used due to its intuitiveness, widespread acceptance and suitability in dealing with human reasoning [8], [9]. Another important feature of fuzzy logic scheme is linguistic rule that comprises IF-THEN statement to establish relationship between antecedent and consequence.…”

Section: A Fuzzy Adaptive Spiral Dynamics Algorithmmentioning

confidence: 99%

“…The adaptive approach by incorporating mathematical function into bacterial foraging algorithm (BFA) with improved performance has been reported in [5], [6], where the performance of the algorithm has been analysed based on incorporating mathematical equation into the BFA. On the other hand, the adaptation scheme of varying step size of BFA through intelligent approach has been reported in [7], [8]. Intelligent approaches such as fuzzy logic have shown not only to improve the algorithm performance, but their simplicity in determining fuzzy rules based on intelligent human logic thinking to vary step size of a point in search space is offering more flexibility and very promising results.…”

Section: Introductionmentioning

confidence: 99%

Novel adaptive spiral dynamics algorithms for global optimization

Nasir

Tokhi

Ghani

et al. 2012

2012 IEEE 11th International Conference on Cybernetic Intelligent Systems (CIS)

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AbstractÑThis paper presents adaptive versions of spiral dynamics algorithm (SDA) referred to as adaptive SDA (ASDA). SDA is known as fast computing algorithm due to its simplicity in the structure and it has stable convergence response when approaching the optimum point in the search space. However, the performance of SDA is still poor due to incorporation of single radius value during the whole search process. In ASDA, the spiral radius is made dynamic by employing novel mathematical equations and incorporating non-mathematical fuzzy logic strategy establishing the relationship between fitness value and spiral radius. This results in better performance in terms of convergence speed, accuracy, and total computing time while retaining the simple structure of SDA. Several uni-modal and multi-modal benchmark functions are employed to test the algorithm in finding the global optimum point. The results show that ASDA outperforms SDA in all test functions considered.

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“…Four fuzzy rules based on triangular membership function were used in the adaptation scheme. Another adaptive scheme using fuzzy logic approach was presented by [3]. In the work, the authors used Mamdani-type fuzzy inference with Gaussian membership function which established relationship between bacteria step size and nutrient value.…”

Section: Introductionmentioning

confidence: 99%

“…The analytical work on the formulation was conducted by [6], 2 Applied Computational Intelligence and Soft Computing which showed that it was capable of improving convergence speed of BFA. Reference [3] presented a similar adaptive scheme with nonlinear equations where they offer more flexible range of bacteria step size, which can be defined within [0, max ], where max is maximum step size. Alternatively, instead of using cost function value, the bacteria step size can be varied based on number of iterations.…”

Section: Introductionmentioning

confidence: 99%

Novel Adaptive Bacteria Foraging Algorithms for Global Optimization

Nasir

Tokhi

Ghani

2014

Applied Computational Intelligence and Soft Computing

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This paper presents improved versions of bacterial foraging algorithm (BFA). The chemotaxis feature of bacteria through random motion is an effective strategy for exploring the optimum point in a search area. The selection of small step size value in the bacteria motion leads to high accuracy in the solution but it offers slow convergence. On the contrary, defining a large step size in the motion provides faster convergence but the bacteria will be unable to locate the optimum point hence reducing the fitness accuracy. In order to overcome such problems, novel linear and nonlinear mathematical relationships based on the index of iteration, index of bacteria, and fitness cost are adopted which can dynamically vary the step size of bacteria movement. The proposed algorithms are tested with several unimodal and multimodal benchmark functions in comparison with the original BFA. Moreover, the application of the proposed algorithms in modelling of a twin rotor system is presented. The results show that the proposed algorithms outperform the predecessor algorithm in all test functions and acquire better model for the twin rotor system.

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Adaptation Schemes of Chemotactic Step Size of Bacterial Foraging Algorithm for Faster Convergence

Cited by 8 publications

References 10 publications

Bacteria Interactive Cost and Balanced-Compromised Approach to Clustering and Transmission Boundary-Range Cognitive Routing In Mobile Heterogeneous Wireless Sensor Networks

Bacteria Interactive Cost and Balanced-Compromised Approach to Clustering and Transmission Boundary-Range Cognitive Routing In Mobile Heterogeneous Wireless Sensor Networks

Novel adaptive spiral dynamics algorithms for global optimization

Novel Adaptive Bacteria Foraging Algorithms for Global Optimization

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