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With the emergence of Wireless Sensor Networks (WSNs), a large number of academics have worked over the last several decades to increase energy efficiency and clustering. Several clustering algorithm techniques, including optimization-based, fuzzy logic-based, and threshold-based, were created to minimize energy consumption and improve network performance. Optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), and their variants are presented. But the challenge of selecting the efficient Cluster Head (CH) and cluster formation around it with minimal overhead and energy consumption remains the same. We propose a novel energy-efficient and lightweight clustering technique for WSNs based on the Modified Bacterial Foraging Optimization Algorithm (MBFA). In this study, the goal of developing the MBFA is to reduce energy consumption, communication overhead, and enhance network performance. The MBFA-based CH selection procedure is based on a unique fitness function. The fitness function computes essential characteristics such as remaining energy, node degree, and distance from sensor node to Base Station (BS). Using the fitness value, the MBFA identifies the sensor node as CH. To justify efficiency, the suggested clustering protocol is simulated and tested against state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

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The Sierpinski gasket fractal antenna is most popular structure in the domain of fractal antennas. This fractal antenna has multi-band performance, and hence, the design of this antenna for the desired frequencies is a challenging problem. The artificial intelligence tools like artificial neural networks, fuzzy logic systems, bio-inspired optimization techniques are appropriate to provide accurate design solution in such cases. In this paper, three most popular bio-inspired optimization algorithms: genetic algorithms, particle swarm optimization (PSO), and bacterial foraging optimization, have been proposed to solve the design issues of Sierpinski gasket pre-fractal antenna. Their performances are analyzed and are compared with the experimental results. A simplified expression for calculation of resonant frequency of Sierpinski gasket pre-fractal antenna is proposed and is used as the objective function. Finally, the effectiveness is compared on the basis of three different measures: mean absolute percentage error, the average time taken by the models to evaluate the results, and the coefficient of correlation. The results indicate that the PSO algorithm is most suitable for this type of antenna. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [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 present paper introduces the use of BFO and ABFO techniques to develop an efficient forecasting model for prediction of various stock indices. The structure used in these forecasting models is a simple linear combiner. The connecting weights of the adaptive linear combiner based models are optimized using ABFO and BFO by minimizing its mean square error (MSE). The short and long term prediction performance of these models are evaluated with test data and the results obtained are compared with those obtained from the genetic algorithm (GA) and particle swarm optimization (PSO) based models. It is in general observed that the new models are computationally more efficient, prediction wise more accurate and show faster convergence compared to other evolutionary computing models such as GA and PSO based models. [Copyright &y& Elsevier]

Resonant frequency of rectangular microstrip antenna having various substrate thicknesses are generally calculated using the standard expressions presented in literatures. But these equations suffer from errors when compared with the experimental values due to Quasi-TEM propagation in the microstrip structure and fringing field. A number of researchers used soft computing approach such as neural networks and genetic algorithm on those equations to minimize the error for better accuracy. Bacterial foraging, an evolutionary optimization technique conceived in recent times, has many advantages over genetic algorithm and is yet to be applied on the design of microstrip patch antenna. In this article, a novel technique is developed to apply bacterial foraging optimization technique in conjunction with the expressions developed to calculate accurately the resonant frequency of rectangular microstrip antenna of any dimension and of any substrate thickness. Further, bacterial foraging is applied to the calculatation of the feed point of microstrip antenna. The technique developed in this article can be a generalized soft computing tool to calculate resonant frequency of rectangular microstrip antenna. Similarly, the idea of the article can be used for calculating the various parameters of microstrip antennas of different structure and geometry. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008. [ABSTRACT FROM AUTHOR]