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Edge detection is the most commonly used method for cell image segmentation, where local search strategies are employed. Although traditional edge detectors are computationally efficient, they are heavily reliant on initialization and may produce discontinuous edges. In this paper, we propose a bacterial foraging-based edge detection (BFED) algorithm to segment cell images. We model the gradients of intensities as the nutrient concentration and propel bacteria to forage along nutrient-rich locations that mimic the behavior of Escherichia coli. Our nature-inspired evolutionary algorithm, can identify the desired edges and mark them as the tracks of bacteria. We have evaluated our algorithm against four edge detectors − the Canny, SUSAN, Verma ' s and an active contour model (ACM) technique − on synthetic and real cell images. Our results indicate that the BFED algorithm identifies boundaries more effectively and provides more accurate cell image segmentation. [ABSTRACT FROM AUTHOR]

This paper presents a modified bacterial foraging optimization algorithm called adaptive crossover bacterial foraging optimization algorithm (ACBFOA), which incorporates adaptive chemotaxis and also inherits the crossover mechanism of genetic algorithm. First part of the research work aims at improvising evaluation of the optimal objective function values. The idea of using adaptive chemotaxis is to make it computationally efficient and crossover technique is to search nearby locations by offspring bacteria. Four different benchmark functions are considered for performance evaluation. The purpose of this research work is also to investigate a face recognition algorithm with improved recognition rate. In this connection, we propose a new algorithm called ACBFO-Fisher. The proposed ACBFOA is used for finding optimal principal components for dimension reduction in linear discriminant analysis (LDA) based face recognition. Three well-known face databases, FERET, YALE and UMIST, are considered for validation. A comparison with the results of earlier methods is presented to reveal the effectiveness of the proposed ACBFO-Fisher algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Unbalanced data that are minority classes with few samples presented in many fields. The mean of unbalanced data is difficult to formalize so that traditional algorithms are limited in solving unbalanced data. In this paper, a novel algorithm based on analysis of variance (ANOVA), fuzzy C-means (FCM) and bacterial foraging optimization (BFO) is proposed to classify unbalanced data. ANOVA can measure the difference between the means of two or more groups in which the observed variance is partitioned into components due to various explanatory variables. FCM is a method of fuzzy clustering algorithm that allows one piece of data to belong to two or more clusters. Natural selection tends to eliminate animals with poor foraging strategies and favors the propagation of genes of those animals that have successful foraging strategies. BFO can model the mechanism of natural selection and solve many application problems. The proposed algorithm combines the advantages of ANOVA, FCM and BFO. ANOVA has the ability to select beneficial feature subsets. FCM has the ability to identify data into clusters with certain membership degrees, and BFO has the fast ability to converge to global optima. In this paper, microarray data of ovarian cancer and zoo dataset are used to test the performance for the proposed algorithm. The performance of the proposed algorithm is supported by simulation results. From simulation results, the classification accuracy of the proposed algorithm outperforms other existing approaches. [Copyright &y& Elsevier]

Abstract: The present paper describes the development of an indirect adaptive fuzzy control scheme employing feedback linearizing technique. The scheme proposes the development of a fuzzy certainty equivalence controller for controlling non-linear plants. This controller is designed on the basis of plant parameters estimated online at each sampling instant using bacterial foraging optimization (BFO) technique, a stochastic optimization technique, popularly employed in recent times. The utility of the proposed scheme is aptly demonstrated by implementing it to control the level in a surge tank under a variety of reference input commands, where the fuzzy controller could significantly out-perform the corresponding classical feedback linearizing controller and PSO-based fuzzy controller. [Copyright &y& Elsevier]

Abstract: This paper deals with hybrid system (GA–BF) based on the conventional GA (Genetic Algorithm) and BF (Bacterial Foraging) which is the social foraging behavior of bacteria. A variety of test function is introduced and simulated to illustrate the characteristics and performance by mutation, crossover, variation of step size, variation of chemotactic step, and variation of lifetime of bacteria in the proposed hybrid system GA–BF. The simulated results represent that the proposed method is highly satisfactory. This approach provides us with novel hybrid model based on foraging behavior and also with a possible new connection between evolutionary forces in social foraging and distributed nongradient optimization algorithm design for global optimization over noisy surfaces. [Copyright &y& Elsevier]

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. • This paper proposes an enhanced Bacterial Foraging Optimization (BFO) algorithm for global optimization. • Chaotic chemotaxis step length, Gaussian mutation and chaotic local search are integrated with BFO algorithm. • The effectiveness was proved by the extensive results on benchmark problems and real-world problems. [ABSTRACT FROM AUTHOR]