Chemotaxis Step Research Articles

A modified bacterial foraging algorithm for improving road networks

A modified bacterial foraging algorithm for improving road networks

An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine

Abstract The Bacterial Foraging Optimization (BFO) algorithm is a swarm intelligent algorithm widely used in various optimization problems. However, BFO suffers from multiple drawbacks, including slow convergence speed, inability to jump out of local optima and fixed step length. In this study, an enhanced BFO with chaotic chemotaxis step length, Gaussian mutation and chaotic local search (CCGBFO) is proposed for overcoming the existing weakness of original BFO. First, a chaotic chemotaxis step length operation is used to produce adaptive chemotaxis step length. Then, by combining the optimal position in the current bacteria with the Gaussian mutation operation to make full use of the information of the optimal position. Finally, a chaotic local search is introduced into the chemotaxis step to ensure that the algorithm can explore a large search space in the early stage. The performance of CCGBFO was evaluated on a comprehensive set of numerical benchmark functions including IEEE CEC2014 and CEC2017 problems. In addition, CCGBFO was also used to tune the key parameters of kernel extreme learning machine for dealing with the real-world problems. The experimental results show that the proposed CCGBFO significantly outperforms the original BFO in terms of both convergence speed and solution accuracy.

NEW BACTERIA FORAGING AND PARTICLE SWARM HYBRID ALGORITHM FOR MEDICAL IMAGE COMPRESSION

For perfect diagnosis of brain tumour, it is necessary to identify tumour affected regions in the brain in MRI (Magnetic Resonance Imaging) images effectively and compression of these images for transmission over a communication channel at high speed with better visual quality to the experts. An attempt has been made in this paper for identifying tumour regions with optimal thresholds which are optimized with the proposed Hybrid Bacteria Foraging Optimization Algorithm (BFOA) and Particle Swarm Optimization (PSO) named (HBFOA-PSO) by maximizing the Renyi’s entropy and Kapur’s entropy. BFOA may be trapped into local optimal problem and delay in execution time (convergence time) because of random chemo taxis steps in the procedure of algorithm and to get global solution, a theory of swarming is commenced in the structure of HBFOA-PSO. Effectiveness of this HBFOA-PSO is evaluated on six different MRI images of brain with tumours and proved to be better in Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Fitness Function.

Nonlinear system control using a fuzzy cerebellar model articulation controller involving reinforcement-strategy-based bacterial foraging optimization

This article proposes a fuzzy cerebellar model articulation controller with reinforcement-strategy-based modified bacterial foraging optimization for solving the cart-pole balancing control problem. The proposed reinforcement-strategy-based modified bacterial foraging optimization is used to adjust the parameters of fuzzy receptive field functions and fuzzy weights for improving the accuracy of the fuzzy cerebellar model articulation controller output. An efficient strategic approach is applied in the chemotaxis step in the traditional bacterial foraging optimization algorithm. In the approach, each virtual bacterium swims for different run lengths and increases the bacterial diversity. Experimental results are presented to show the performance and effectiveness of the proposed reinforcement-strategy-based modified bacterial foraging optimization method.

Chaos Enhanced Bacterial Foraging Optimization for Global Optimization

The recently developed Bacterial Foraging Optimization algorithm (BFO) is a nature-inspired optimization algorithm based on the foraging behavior of Escherichia coli. Due to its simplicity and effectiveness, BFO has been applied widely in many engineering and scientific fields. However, when dealing with more complex optimization problems, especially high dimensional and multimodal problems, BFO performs poorly in convergence compared to other nature-inspired optimization techniques. In this paper, we therefore propose an improved BFO, termed ChaoticBFO, which combines two chaotic strategies to achieve a more suitable balance between exploitation and exploration. Specifically, a chaotic initialization strategy is incorporated into BFO for bacterial population initialization to achieve acceleration throughout early steps of the proposed algorithm. Then, a chaotic local search with a ‘shrinking’ strategy is introduced into the chemotaxis step to escape from local optimum. The performance of ChaoticBFO was validated on 23 numerical well-known benchmark functions by comparing with 10 other competitive metaheuristic algorithms. Moreover, it was applied to two real-world benchmarks from IEEE CEC 2011. The experimental results demonstrate that ChaoticBFO is superior to its counterparts in both convergence speed and solution quality in most of the cases. This paper is of great significance for promoting the research, improvement and application of the BFO algorithm.

A Novel Adaptive Chaotic Bacterial Foraging Optimization Algorithm

In this paper, an adaptive chaotic bacterial foraging optimization (ACBFO) is presented. The improved bacterial foraging algorithm contains two new features, adaptive chemotaxis step setting and chaotic perturbation operation in each chemotactic event. The adaptive chemotaxis step setting enables fast convergence speed and good convergence accuracy of the algorithm, and the bacteria chaotic perturbation operation further allows the search to escape from local optima and achieve better convergence accuracy. With five benchmark functions, ACBFO is proved to have a better performance than the original bacterial foraging optimization (BFO) and BFO with linear deceasing chemotaxis step (BFO-LDC).

A Review on Nature-based Swarm Intelligence Optimization Techniques and its Current Research Directions

Background/Objectives: In today’s world, finding a feasible solution for combinatorial problems becoming a crucial task. The main objective of this paper is to analyze and comprehend different nature based algorithms enabling to find optimal solution. Methods/statistical analysis: Bacterial Foraging Algorithm (BFOA), firefly algorithm, Ant Colony Optimization (ACO), bee colony optimization, cuckoo optimization etc. Which have been used in power load balancing, cost estimating, optimal routing, color segmentation were discussed. This paper also highlights the constraints and convergence properties of each algorithm to solve certain problems encountered in various fields of application. Findings: Ant colony algorithms were successful in finding solutions within 1% of known optimal solutions. Optimal solution was found in BFOA by adjusting chemo taxis step size. Also, this paper analyzes results of various research works done in numerous fields using the swarm intelligence techniques.

A Clustering Approach Using Fractional Calculus-Bacterial Foraging Optimization Algorithm for k-Anonymization in Privacy Preserving Data Mining

A tremendous amount of personal data of an individual is being collected and analyzed using data mining techniques. Such collected data, however, may also contain sensitive data about an individual. Thus, when analyzing such data, individual privacy can be breached. Therefore, to preserve individual privacy, one can find numerous approaches proposed for the same in the literature. One of the solutions proposed in the literature is k-anonymity which is used along with the clustering approach. During the investigation, the authors observed that the k-anonymization based clustering approaches all the times result in the loss of information. This paper presents a fractional calculus-based bacterial foraging optimization algorithm (FC-BFO) to generate an optimal cluster. In addition to this, the authors utilize the concept of fractional calculus (FC) in the chemotaxis step of a bacterial foraging optimization (BFO) algorithm. The main objective is to improve the optimization ability of the BFO algorithm. The authors also evaluate their proposed FC-BFO algorithm, empirically, focusing on information loss and execution time as a vital metric. The experimental evaluations show that our proposed FC-BFO algorithm generates an optimal cluster with lesser information loss as compared with the existing clustering approaches.

An effective bacterial foraging optimizer for global optimization

An effective bacterial foraging optimizer for global optimization is presented.The optimization utilizes combination of gravitational search and swarm diversity strategies.The algorithm is used to solve various types of benchmark functions and identify parameters of a chaotic system.The results are compared with the results of its competitors presented in the literatures. Bacterial foraging optimization (BFO) inspired by a behavior of bacteria called chemotaxis is a novel stochastic optimization algorithm, its chemotactic movement mimics a trial solution through random search directions. However, it may enable BFO to possess a poor optimizing performance as compared to other optimization methods over complex optimization problems. To improve the exploration and exploitation abilities of the standard BFO, this paper proposes an effective bacterial foraging optimization (EBFO). First a gravitational search strategy is incorporated into the chemotaxis step to adjust its unit length according to the swarm information. Then, a swarm diversity strategy is integrated into the reproduction step to enhance the reproduction mode depending on the swarm diversity. We evaluate the performance of the EBFO on 23 numerical benchmark functions, then it is applied to identifying parameters of a chaotic system. The simulation results show that the proposed algorithm is more effective than its competitors and can be extended to other global optimization problems.

A Fuzzy Cerebellar Model Articulation Controller Using a Strategy-Adaptation-Based Bacterial Foraging Optimization Algorithm for Classification Applications

This paper proposes a fuzzy cerebellar model articulation controller using a strategy-adaptation-based bacterial foraging optimization (SABFO) algorithm to solve classification problems. A strategic approach to the chemotaxis step in the SABFO algorithm was adopted: in this approach, each virtual bacterium swims on different run-lengths, and bacterial diversity is increased. The simulation results indicated that the performance of the proposed method was more favorable than that of other methods.

Adaptive comprehensive learning bacterial foraging optimization and its application on vehicle routing problem with time windows

Abstract This paper proposes a variant of the bacterial foraging optimization (BFO) algorithm with time-varying chemotaxis step length and comprehensive learning strategy which we call adaptive comprehensive learning bacterial foraging optimization (ALCBFO). An adaptive non-linearly decreasing modulation model is used to keep a well balance between the exploration and exploitation of the proposed algorithm. The comprehensive learning mechanism maintains the diversity of the bacterial population and thus alleviates the premature convergence. Compared with the classical GA, PSO, the original BFO and two improved BFO (BFO-LDC and BFO-NDC) algorithm, the proposed ACLBFO shows significantly better performance in solving multimodal problems. We also assess the performance of the ACLBFO method on vehicle routing problem with time windows (VRPTW). Compared with three other BFO algorithms, the proposed algorithm is superior and confirms its potential to solve vehicle routing problem with time windows (VRPTW).

A Clustering Approach for the l-Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm

In privacy preserving data mining, the l-diversity and k-anonymity models are the most widely used for preserving the sensitive private information of an individual. Out of these two, l-diversity model gives better privacy and lesser information loss as compared to the k-anonymity model. In addition, we observe that numerous clustering algorithms have been proposed in data mining, namely, k-means, PSO, ACO, and BFO. Amongst them, the BFO algorithm is more stable and faster as compared to all others except k-means. However, BFO algorithm suffers from poor convergence behavior as compared to other optimization algorithms. We also observed that the current literature lacks any approaches that apply BFO with l-diversity model to realize privacy preservation in data mining. Motivated by this observation, we propose here an approach that uses fractional calculus (FC) in the chemotaxis step of the BFO algorithm. The FC is used to boost the computational performance of the algorithm. We also evaluate our proposed FC-BFO and BFO algorithms empirically, focusing on information loss and execution time as vital metrics. The experimental evaluation shows that our proposed FC-BFO algorithm derives an optimal cluster as compared to the original BFO algorithm and existing clustering algorithms.

Bacterial-inspired algorithms for solving constrained optimization problems

Abstract Bio-inspired optimization techniques using analogy of swarming principles and social behavior in nature have been adopted to solve a variety of problems. In this paper Bacterial Foraging Optimization (BFO) is employed to achieve high-quality solutions to the constrained optimization problems. However, the chemotaxis step was set as only a constant in the original BFO, where no mechanism could keep the balance between global search and local search. To further improve the performance of the original BFO, we also come up with two modified BFOs, i.e. BFO with linear decreasing chemotaxis step (BFO-LDC) and BFO with non-linear decreasing chemotaxis step (BFO-NDC). In order to illustrate the efficiency of the proposed method (BFO-LDC and BFO-NDC), six well-known constrained benchmark problems from the optimization literature were selected as testing functions. The experimental results demonstrated that the modified BFOs are of greater efficiency in the speed of convergence as well as fine tune the search in the multidimensional space, and they can be used as a general approach for most nonlinear optimization problems with inequity constrains.

BFO Optimization Algorithms for Vehicle Routing Problem with Time Windows

Vehicle Routing Problem with Time Windows (VRPTW) is a constrained NP-hard problem that designs the least cost routes from one depot to a set of geographically scattered points. Most of traditional techniques are difficult to solve this kind of problem. In this paper, we explored the validity of another swarm-intelligence-based model-Bacterial Foraging Optimization (BFO) for VRPTW solving. Original BFO, BFO with linear decreasing chemotaxis step (BFO-LDC) and BFO with non-linear decreasing chemotaxis step (BFO-NDC) are used to obtain the best solutions of a given VRPTW problem, respectively. The experimental results demonstrated that the proposed BFO algorithms have the potential to solve Vehicle Routing Problem with Time Windows (VRPTW) with rapid convergence rate and the good result accuracy.

Adaptive bacterial colony chemotaxis multi-objective optimisation algorithm

This paper focuses on the multi-objective optimisation problem (MOOP). To improve the convergence speed and the diversity of bacterial chemotaxis multi-objective optimisation algorithm (BCMOA) and avoid falling into local minimum, this paper proposes an adaptive bacterial colony chemotaxis multi-objective optimisation (ABCCMO) algorithm. Firstly, fast non-dominated sorting approach is used to initialise the position of all the bacterial. Secondly, this proposed algorithm adopts the adaptive chemotaxis step length. Thirdly, colony intelligent optimisation thought is adopted. Experimental results show that ABCCMO is able to find much better Pareto front solutions.

Chemo-inspired genetic algorithm for function optimization

Finding global optimal solution for a non-linear optimization problem with high complexity became a challenge for the researchers. Evolutionary optimization process is being treated as an alternate paradigm to solve such problems. In this context, Bacterial Foraging Optimization (BFO) is a novel heuristic algorithm inspired from foraging behavior of Escherichia coli bacterium. At the same time, Genetic Algorithm (GA) has also achieved popularity from the academic and industrial communities to deal with such problems. To improve the solution quality further, the hybridization of GA with BFO (GA-BF) is proven to be much robust in recent past. It is found that while hybridizing GA with BFO, the behavior/mechanism of some of the operators seems to be repeated and it may lead to hamper the solution quality as well as increases the computational time. Hence, instead of taking the whole BFO to hybridize with GA (called GA-BF); only the chemotaxis step is picked from BFO mechanism and hybridized with GA. It is named as chemo-inspired Genetic Algorithm (CGA). The superiority of CGA over GA-BF algorithm is being realized through a set of four typical benchmark problems available in the literature. Later, the faster convergence of CGA is shown graphically. Hence, CGA outperforms GA-BF in terms of solution quality and the computational time. Moreover, two real life problems namely (a) solving system of linear equations and (b) frequency modulation sounds parameter identification problems have been solved, in order to justify the above conclusion.

A quantum bacterial foraging optimisation algorithm and its application in spectrum sensing

In order to improve bacterial foraging optimisation algorithm (BFOA) which has been widely applied in various aspects of science and engineering, a quantum bacterial foraging optimisation algorithm (QBFOA) is proposed. In QBFOA, quantum rotation gate is used to complete the chemotaxis step in order to reform the performance of BFOA. As a key step of QBFOA, chemotactic movement is modelled as quantum walk behaviour and thus may find the optimum solution. We compare the performance of QBFOA with classical BFOA, shuffled frog leaping algorithm (SFLA) and particle swarm optimisation (PSO), and some typical high-dimension complex functions have been presented to test these four bionic algorithms. The simulation results show that the proposed QBFOA has a better searching speed and an obvious accuracy. In addition, we applied our newly designed algorithm in spectrum sensing, which is a hot spot in cognitive radio domain. The computer simulation results proved that spectrum sensing method based on QBFOA is superior to the spectrum sensing methods based on previous intelligence algorithms.

Bacterial foraging based approaches to portfolio optimization with liquidity risk

This paper proposes a bacterial foraging based approach for portfolio optimization problem. We develop an improved portfolio optimization model by introducing the endogenous and exogenous liquidity risk and the corresponding indexes are designed to measure the endogenous/exogenous liquidity risk, respectively. Bacterial foraging optimization (BFO) is employed to find the optimal set of portfolio weights in the improved Mean-Variance model. BFO-LDC which is a modified BFO with linear deceasing chemotaxis step is proposed to further improve the performance of BFO. With four benchmark functions, BFO-LDC is proved to have better performance than the original BFO. And then comparisons of the results produced by BFO, BFO-LDC, particle swarm optimization (PSO), and genetic algorithms (GAs) for the proposed portfolio optimization model are presented. Simulation results show that BFOs can obtain both near optimal and the practically feasible solutions to the liquidity risk portfolio optimization problem. In addition, BFO-LDC outperforms BFO in most cases.

Chemical-Shift-Perturbation Mapping of the Phosphotransfer and Catalytic Domain Interaction in the Histidine Autokinase CheA from Thermotoga maritima,

Regulating the activity of the histidine autokinase CheA is a central step in bacterial chemotaxis. The CheA autophosphorylation reaction minimally involves two CheA domains, denoted P1 and P4. The kinase domain (P4) binds adenosine triphosphate (ATP) and orients the gamma phosphate for phosphotransfer to a reactive histidine on the phosphoacceptor domain (P1). Three-dimensional triple-resonance experiments allowed sequential assignments of backbone nuclei from P1 and P4 domains as well as the P4 assignments within a larger construct, P3P4, which includes the dimerization domain P3. We have used nuclear magnetic resonance chemical-shift-perturbation mapping to define the interaction of P1 and P3P4 from the hyperthermophile Thermotoga maritima. The observed chemical-shift changes in P1 upon binding suggest that the P1 domain is bound by interactions on the side opposite the histidine that is phosphorylated. The observed shifts in P3P4 upon P1 binding suggest that P1 is bound at a site distinct from the catalytic site on P4. These results argue that the P1 domain is not bound in a mode that leads to productive phosphate transfer from ATP at the catalytic site and imply the presence of multiple binding modes. The binding mode observed may be regulatory or it may reflect the binding mode needed for effective transfer of the histidyl phosphate of P1 to the substrate proteins CheY and CheB. In either case, this work describes the first direct observation of the interaction between P1 and P4 in CheA.

Viewpoint 4

Viewpoint 4