Probabilistic cuing in large-scale environmental search.

Finding an object in our environment is an important human ability that also represents a critical component of human foraging behavior. One type of information that aids efficient large-scale search is the likelihood of the object being in one location over another. In this study we investigated the conditions under which individuals respond to this likelihood, and the reference frames in which this information is coded, using a novel, large-scale environmental search paradigm. Participants searched an array of locations, on the floor of a room, for a hidden target by pressing switches at each location. We manipulated the probability of the target being at a particular set of locations. Participants reliably learned target likelihoods when the possible search locations were kept constant throughout the experiment and the starting location was fixed. There was no evidence of such learning when room-based and body-based reference frames were dissociated. However, when this was combined with a more salient perceptual landmark, an allocentric cuing effect was observed. These data suggest that the encoding of this type of statistical contingency depends on the combination of spatial cues. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Information Foraging—A Model for Exploration Breadth and Depth

The optimal path choice theory from biology implies that foragers should evaluate all sites before selecting something feasible when site relocation costs are low and there are few sites to explore. In internet information foraging, although the cost of moving to another site is negligible, the number of relevant sites can be large and exploration costs vary. We examine human internet information foraging behaviors and propose a model relating them to established cognitive measures.     

An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images

In the domain of human brain image analysis, identification of tumor region and segmentation of tissue structures tend to be a challenging task. Automated segmentation of Magnetic Resonance (MR) brain images would be of great assistance to radiologist, as they minimize the complication evolved due to human interface and offer quicker segmentation results. Automated algorithms offer minimal time duration and lesser manual intervention to a radiologist during clinical diagnosis. Moreover, larger volumes of patient data could be assessed with the aid of an automated algorithm and one such algorithm is proposed through this research to identify the tumor region bounded between normal tissue regions and edema portions. The proposed algorithm offers a better support to a radiologist in the process of diagnosing the pathologies, since; it utilizes both optimization and clustering techniques. Bacteria Foraging Optimization (BFO) and Modified Fuzzy K − Means algorithm (MFKM) are the optimization and clustering techniques used to render efficient MR brain image analysis. The proposed combinational algorithm is compared with Particle Swarm Optimization based Fuzzy C − Means algorithm (PSO based FCM), Modified Fuzzy K − Means (MFKM) and conventional FCM algorithm. The suggested methodology is evaluated using the comparison parameters such as sensitivity, Specificity, Jaccard Tanimoto Co − efficient Index (TC) and Dice Overlap Index (DOI), computational time and memory requirement. The algorithm proposed through this paper has produced appreciable values of sensitivity and specificity, which are 97.14% and 93.94%, respectively. Finally, it is found that the proposed BFO based MFKM algorithm offers better MR brain image segmentation and provides extensive support to radiologists.     

Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system

This paper presents automatic generation control (AGC) of interconnected two equal area, three and five unequal-areas thermal systems provided with single reheat turbine and generation rate constraints of 3% per minute in each area. A maiden attempt is made to apply integral plus double derivative (IDD) controller in AGC. Controller gains in the two-area system are optimized using classical approach whereas in the three and five area systems controller gains and governor speed regulation parameters (R_i) are simultaneously optimized by using a more recent and powerful evolutionary computational technique called bacterial foraging (BF) technique. Investigations reveal on comparison that Integral (I), Proportional-Integral (PI), Integral-Derivative (ID), or Proportional-Integral-Derivative (PID) controllers all provide more or less same response where as Integral-Double Derivative (IDD) controller provides much better response. Sensitivity analysis reveals the robustness of the optimized IDD controller gains and R_i of the five area system to wide changes in inertia constant (H), reheat time constant (T_r), reheat coefficient (K_r), system loading condition and size and position of step-load perturbation.     

A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem

Dynamic economic dispatch (DED) is one of the most significant non-linear complicated problems showing non-convex characteristic in power systems. This is due to the effect of valve-points in the generating units’ cost functions, the ramp-rate limits and transmission losses. Hence, proposing an effective solution method for this optimization problem is of great interest. The original bacterial foraging (BF) optimization algorithm suffers from poor convergence characteristics for larger constrained problems. To overcome this drawback, a hybrid genetic algorithm and bacterial foraging (HGABF) approach is presented in this paper to solve the dynamic economic dispatch problem considering valve-point effects, ramp-rate limits and transmission losses. The HGABF approach can be derived by integrating BF algorithm and genetic algorithm (GA), so that the BF’s drawback can be treated before employing it to solve the complex and high dimensioned search space of the DED problem. To illustrate the effectiveness of the HGABF approach, several test systems with different numbers of generating units are used. The results of HGABF approach are compared with those obtained by other published methods employing same test systems. These results show the effectiveness and the superiority of the introduced method over other published methods.     

A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches

This paper presents a novel idea of intracranial segmentation of magnetic resonance (MR) brain image using pixel intensity values by optimum boundary point detection (OBPD) method. The newly proposed (OBPD) method consists of three steps. Firstly, the brain only portion is extracted from the whole MR brain image. The brain only portion mainly contains three regions–gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). We need two boundary points to divide the brain pixels into three regions on the basis of their intensity. Secondly, the optimum boundary points are obtained using the newly proposed hybrid GA–BFO algorithm to compute final cluster centres of FCM method. For a comparison, other soft computing techniques GA, PSO and BFO are also used. Finally, FCM algorithm is executed only once to obtain the membership matrix. The brain image is then segmented using this final membership matrix. The key to our success is that we have proposed a technique where the final cluster centres for FCM are obtained using OBPD method. In addition, reformulated objective function for optimization is used. Initial values of boundary points are constrained to be in a range determined from the brain dataset. The boundary points violating imposed constraints are repaired. This method is validated by using simulated T1-weighted MR brain images from IBSR database with manual segmentation results. Further, we have used MR brain images from the Brainweb database with additional noise levels to validate the robustness of our proposed method. It is observed that our proposed method significantly improves segmentation results as compared to other methods.     

Robust estimation of power system harmonics using a hybrid firefly based recursive least square algorithm

This paper presents a new and hybrid algorithm based on Firefly Algorithm (FA) and Recursive Least Square (RLS) for power system harmonic estimation. The hybrid FA–RLS algorithm is developed for estimating harmonics, inter harmonics and sub harmonics from a distorted and noise corrupted power signal. The basic strategy of the proposed algorithm is to integrate FA for getting the optimum initial weights for RLS algorithm that sequentially updates the unknown parameters (weights) of the harmonic signal. Simulation and practical validation is made with experimentation of the algorithms with real time data obtained from a solar connected inverter system. Comparison of results amongst recently proposed Artificial Bee Colony Least Square (ABC–LS), Bacteria Foraging Optimized Recursive Least Square (BFO–RLS) and FA–RLS algorithms reveals that proposed FA–RLS algorithm is the best in terms of accuracy, convergence and computational time.     

Stability analysis of the reproduction operator in bacterial foraging optimization

In his seminal paper published in 2002, Passino pointed out how individual and groups of bacteria forage for nutrients and how to model it as a distributed optimization process, which he named the Bacterial Foraging Optimization Algorithm (BFOA). One of the major operators of BFOA is the reproduction phenomenon of virtual bacteria, each of which models one trial solution of the optimization problem. During reproduction, the least healthy bacteria (with a lower accumulated value of the objective function in one chemotactic lifetime) die and the other healthier bacteria each split into two, which then starts exploring the search place from the same location. The phenomenon has a direct analogy with the selection mechanism of classical evolutionary algorithms. This paper attempts to model reproduction as a dynamics and then analyses the stability of the reproductive system very near to an equilibrium point, which in this case is an isolated optimum. It also finds conditions under which a stable reproduction event can take place, to direct a worse bacterium towards a better one. Our analysis reveals that a stable reproduction event contributes to the quick convergence of the bacterial population near optima.     

A Hybrid Bacterial Foraging-Particle Swarm Optimization Technique for Optimal Tuning of Proportional-Integral-Derivative Controller of a Permanent Magnet Brushless DC Motor

Abstract—The proportional-integral-derivative controllers were the most popular controllers of this century because of their remarkable effectiveness, and simplicity of implementation. However, proportional-integral-derivative controllers are usually poorly tuned in practice. This article presents a hybrid particle swarm optimization and bacterial foraging techniques for determining the optimal parameters of a proportional-integral-derivative controller for speed control of a permanent magnet brushless DC motor. The first part of the article deals with the system modeling and its verification where a model of modest accuracy cannot be expected to give a fair comparison of different controllers. The remaining parts of the article present the application of different optimization techniques to tune the proportional-integral-derivative controller as applied to the motor model. The particle swarm optimization, bacterial foraging, and bacterial foraging-particle swarm optimization algorithms are implemented in MATLAB while the GA Toolbox is used. The performance of the tuned controllers is simulated and experimentally verified to evaluate the main characteristics of each one. It is found that the proposed hybrid bacterial foraging-particle swarm optimization technique is more efficient in improving the step response characteristics and achieving the desired performance indices.     

Analysis of cascaded multilevel inverters for active harmonic filtering in distribution networks

This paper aims at the investigation of an active power filter (APF) comprised of a transformerless multilevel inverter (MLI) for power conditioning in three-phase three-wire distribution network. The inverter topologies used here are three, five, seven and nine-level. The system configuration mainly involves cascaded MLI structure of APF, generation of compensation filter currents based on instantaneous active and reactive current component (i_d–i_q) method and dc-link voltage regulation using a PI controller. Not many papers focus on the regulation of dc-link capacitor voltage. Here we have proposed the implementation of bacterial foraging optimization (BFO) to extract the gains of PI controller. The proposed work provides improved dc-link voltage regulation, quick prevail over current harmonics and reduction of overall source current THD. Adequate MATLAB/Simulink simulation results are presented for the different cascaded MLIs discussed above. Additionally, the performance has been validated in real-time using Opal-RT Lab simulator considering three different conditions of supply i.e., balanced sinusoidal, balanced non-sinusoidal and unbalanced sinusoidal.