Modified Bacterial Foraging Algorithm Research Articles

Environmental economic dispatch optimization using niching penalized chimp algorithm

The use of traditional fossil fuels has contributed to rapid economic expansion while simultaneously having negative implications, such as increasing global warming and the devastation of the biosphere. Niching Penalized Chimp Optimization (NPChOA) is presented in this research in order to address the Environmental Economic Dispatch (EED) problem. In terms of global search capability, robustness, convergence rate, and durability, the proposed NPChOA outperforms the current ChOA. Following that, a novel constrained handling operator addresses the multi-objective optimization difficulty. The performance of NPChOA is evaluated using an IEEE 30 bus with six generators and a ten-unit system. The result of NPChOA is compared with Grey Wolf Optimizer (GWO), Space Reduction Strategy Particle Swarm Optimization (SRSPSO), Chaotic Biogeography-Based Optimizer (CBBO), Dynamic Population Artificial Bee Colony (DPABC), Modified Bacterial Foraging Algorithm (MBFA), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution-Crossover Quantum Particle Swarm Optimization (DE-CQPSO), standard ChOA, Dynamic Levy Flight ChOA (DLF-ChOA), and Weighted ChOA (WChOA) as the most recent modified version of ChOA to confirm its efficiency. NPChOA's evaluation score and convergence rate are outstanding compared to other benchmarks for single and multi-objective optimizations. The NPChOA's efficacy and robustness in dealing with environmental economic dispatch challenges have been demonstrated by discovering a good compromise value.

Economic dispatch of wind integrated power systems with energy storage considering composite operating costs

An economic dispatch (ED) model is proposed in this study for accommodating high penetrations of wind power with the integration of battery energy storage (BES) in power systems. In the proposed ED model, a wind-storage combined system (WSCS) model is studied to collectively mitigate the output fluctuations and improve the wind power utilisation. In addition, the proposed concept of composite operating costs include the unit operation cost, environmental cost, reserve cost, compensation cost for wind power curtailment, and energy loss cost of ES. With the minimisation of the composite operating costs as the objective function of the proposed ED model with WSCS, a modified bacterial foraging algorithm (MBFA) is proposed to solve the optimisation problem based on a combination of the bacterial foraging algorithm (BFA) and the Particle Swarm Optimisation (PSO). Furthermore, the case study on the IEEE 30-bus system has been used to demonstrate the feasibility and effectiveness of the proposed ED model and the performance improvement of the MBFA algorithm over BFA or PSO alone. The comparison between different scenarios shows that the integration of ES in the proposed model effectively improves the wind power utilisation and the system efficiency.

Chromosomal Edge Detection using Modified Bacterial Foraging Algorithm

This paper proposes a modified bacterial foraging algorithm with a probabilistic derivative approach to detect edges in chromosome images. Chromosomal Edge Detection is fundamental for automatic karyotyping for noise reduction and getting useful messages from the edges. Subjected to staining and other imaging constraints, chromosomal banding patterns lack in resolution, contrast and suffer from noise. For this reason, chromosomal edge detection is highly preferred prior to the segmentation and classification of chromosomes. When the chromosomes occlude or overlap, edge detection becomes extremely difficult. Edge detection is highly challenging and this paper presents a Modified Bacterial Foraging Algorithm (MBFA) based on a probabilistic derivative methodology based on Ant Colony Optimization (ACO) for the detection of edges in chromosomes. Bacterium searches for the nutrients in the direction decided by a probabilistic derivative approach derived from ACO and the edge pixels are identified and traversed. The study reveals that MBFA gives the most promising results in detecting chromosomal edges, greatly reducing the computation time and memory requirements. Acceptable values of parameters for performance evaluation like Kappa (K) and Entropy (E) are achieved with the proposed algorithm in comparison to the other conventional methods of edge detection.

Solving Optimal Power Flow Using Modified Bacterial Foraging Algorithm Considering FACTS Devices

In this paper, a new Modified Bacterial Foraging Algorithm (MBFA) method is developed to incorporate FACTS devices in optimal power flow (OPF) problem. This method can provide an enhanced economic solution with the use of controllable FACTS devices. Two types of FACTS devices, thyristor controlled series compensators (TCSC) and Static VAR Compensator (SVC) are considered in this method. The basic bacterial foraging algorithm (BFA) is an evolutionary optimization technique inspired by the foraging behavior of the E. coli bacteria. The strategy of the OPF problem is decomposed in two sub-problems, the first sub-problem related to active power planning to minimize the fuel cost function, and the second sub-problem designed to make corrections to the voltage deviation and reactive power violation based in an efficient reactive power planning of multi Static VAR Compensator (SVC). The specified power flow control constraints due to the use of FACTS devices are included in the OPF problem. The proposed method decomposes the solution of such modified OPF problem into two sub problems’ iteration. The first sub problem is a power flow control problem and the second sub problem is a modified Bacterial foraging algorithm (MBFA) OPF problem. The two sub problems are solved iteratively until convergence. Case studies are presented to show the effectiveness of the proposed method.

Modified bacterial foraging algorithm based multilevel thresholding for image segmentation

Multilevel thresholding is one of the most popular image segmentation techniques. In order to determine the thresholds, most methods use the histogram of the image. This paper proposes multilevel thresholding for histogram-based image segmentation using modified bacterial foraging (MBF) algorithm. To improve the global searching ability and convergence speed of the bacterial foraging algorithm, the best bacteria among all the chemotactic steps are passed to the subsequent generations. The optimal thresholds are found by maximizing Kapur's (entropy criterion) and Otsu's (between-class variance) thresholding functions using MBF algorithm. The superiority of the proposed algorithm is demonstrated by considering fourteen benchmark images and compared with other existing approaches namely bacterial foraging (BF) algorithm, particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The findings affirmed the robustness, fast convergence and proficiency of the proposed MBF over other existing techniques. Experimental results show that the Otsu based optimization method converges quickly as compared with Kapur's method.

Economic emission load dispatch through fuzzy based bacterial foraging algorithm

This paper presents a newly developed optimization approach involving a modified bacterial foraging algorithm (MBFA) applied for the solution of the economic and emission load dispatch (EELD) problem. The approach utilizes the natural selection of global optimum bacterium having successful foraging strategies in the fitness function. The bacterial foraging algorithm (BFA) appears to be a robust and reliable optimization algorithm for the solution of the EELD problems. To obtain the best compromising solution a fuzzy decision making approach using MBFA is applied to the standard IEEE 30-bus six generator test system and a Taiwan power system of 40 generating units with valve point loading effects. The results confirm the potential and effectiveness of the proposed algorithm compared to various methods such as, linear programming (LP), multi-objective stochastic search technique (MOSST), differential evolution (DE), non-dominated sorting genetic algorithm (NSGA), niched pareto genetic algorithm (NPGA), strength pareto evolutionary algorithm (SPEA) and fuzzy clustering based particle swarm optimization (FCPSO) performed in different central load dispatch centers to solve EELD problems. The quality and usefulness of the proposed algorithm is demonstrated through its application to two standard test systems in comparison with the other existing techniques. The current proposal was found to be better than, or at least comparable to them considering the quality of the solutions obtained.