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bacterial foraging optimisation algorithm, particle swarm optimisation and genetic algorithm: a comparative study

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2017年第4期10卷 275-282页

作者： Sadeghiram, Soheila Univ Mohaghegh Fac Engn Dept Informat Technol Ardabili Iran

Nature inspired meta-heuristic algorithms have been widely used in order to find efficient solutions for optimisation problems, and granted results have been achieved. Particle swarm optimisation (PSO) algorithm is one of the most utilised algorithms in recent years, which has indicated acceptable efficiency. On the other hand, bacterial foraging optimisation algorithm (BFOA) is relatively new compared to other meta-heuristic algorithms, and like PSO has shown a good ability to solve different optimisation problems. Genetic algorithms (GAs) are a well-known group of meta-heuristic algorithms which have been in use earlier than the other in various research fields. In this paper, we compare the efficiency of BFOA and PSO algorithms in an identical condition by minimising different test functions (from two to 20 dimensional). In this experiment, GA is used as a basic method in comparing the two algorithms. The methodology and results are presented. Although results verify the accurate convergency of both algorithms, the efficiency of BFOA on high-dimensional functions is dramatically better than that of PSO.

关键词： particle swarm optimisation algorithm bacterial foraging optimisation algorithm BFOA genetic algorithms high-dimensional functions

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Non-linear active noise cancellation using a bacterial foraging optimisation algorithm

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IET SIGNAL PROCESSING 2012年第4期6卷 364-373页

作者： Gholami-Boroujeny, S. Eshghi, M. Shahid Beheshti Univ Med Sci Elect & Comp Engn Fac Tehran Iran

This study presents a new scheme for non-linear active noise control (ANC) systems. In the proposed ANC system, a new evolutionary algorithm known as bacterial foraging (BF) is used for optimising the adaptive controller. The proposed ANC system using bacterial foraging optimisation (BFO) has the ability to prevent falling into local minima. Moreover, using the BF algorithm to adapt the ANC filter coefficients removes the need for the preliminary identification of the secondary path. Several computer simulations are developed in order to analyse the performance of the proposed BFO-based ANC system (BFO-ANC). The experiments are carried out in two major groups including a linear and a non-linear secondary path, along with a non-linear primary path. In each group, the effect of different parameters of the BFO algorithm is investigated on the performance and robustness of the proposed ANC system. The authors also compare the results obtained by three ANC systems;the proposed BFO-based ANC, the GA-based ANC and the filtered-X LMS-based ANC. Simulation results demonstrate the effectiveness of the proposed BFO method in noise cancellation performance under several situations.

关键词： BFO-based ANC system adaptive controller Other nonelectric variables control evolutionary algorithm Interpolation and function approximation (numerical analysis) Nonlinear control systems filtered-X LMS-based ANC active noise control adaptive control GA-based ANC optimisation techniques least mean squares methods Self-adjusting control systems bacterial foraging optimisation algorithm genetic algorithms nonlinear active noise cancellation nonlinear control systems

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An adaptive approach for the fault tolerant control of a nonlinear system

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2017年第2期11卷 105-123页

作者： Sudha, K. Bai, V. Thulasi St Peters Univ Madras Tamil Nadu India Pratyusha Inst Technol & Management Dept Elect & Commun Engn Madras Tamil Nadu India

In this paper hybridisation between two optimisation methods such as modified bacterial foraging optimisation algorithm (MBFOA) and adaptive neuro fuzzy inference system (ANFIS) is presented for determining the optimal proportional-integral (PI) controller parameters, for fault tolerant control (FTC) in an wind energy conversion system (WECS). Initially, detection and diagnosis of fault is performed by the ANFIS. The wind turbine actual output performance and the estimated output values are given as an input to the ANFIS. The residuals, which are the outputs of ANFIS is used for deciding, whether the signal is a fault signal or a non-fault signal, which utilises the knowledge-based computation technique. By using the step size modified bacterial foraging optimisation algorithm-based FTC, the gain parameters are optimised based on the occurrence of fault. Then the proposed hybrid fault tolerant control model is implemented in the MATLAB/SIMULINK platform and the effectiveness is analysed by comparing with the other techniques. The comparison results demonstrate the superiority of the proposed approach and confirm its potential in controlling the fault in a WECS.

关键词： bacterial foraging optimisation algorithm BFOA wind energy conversion system WECS fault detection and diagnosis FDD fault tolerant control FTC

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Hybrid Heuristic algorithm Based Controller Tuning for a Bioreactor

Hybrid Heuristic Algorithm Based Controller Tuning for a Bio...

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International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE)

作者： Ramya, S. Latha, K. Anna Univ Madras Inst Technol Dept Instrumentat Engn Madras 600025 Tamil Nadu India

ISBN: (纸本)9781479949816

Bioreactor is one of the prime processing units widely employed to produce important chemical and biochemical compounds. In this paper, a hybrid heuristic algorithm has been attempted to tune PID controller for nonlinear Bioreactor model. The hybrid algorithm is a combination of bacterial foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm. Multiobjective performance indexes such as Integral Square Error, peak overshoot are considered to guide this algorithm for discovering best possible value of controller parameters. The controller tuning procedure is individually discussed for both stable and unstable steady state operating region of simulated bio-reactor model. The effectiveness of the proposed scheme has been validated through a comparative study with BFO, PSO based controller tuning methods proposed in the literature. The results show that, the hybrid method provides improved performance in reference tracking and load disturbance rejection with minimal ISE value.

关键词： Bioreactor bacterial foraging optimisation algorithm Particle Swarm Optimization algorithm PID controller

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Design and analysis of BFOA optimised PID controller with derivative filter for frequency regulation in distributed generation system

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2018年第2期12卷 291-323页

作者： Gorripotu, Tulasichandra Sekhar Kumar, Darapureddi Vijaya Boddepalli, Manmadha Kumar Pilla, Ramana Sri Sivani Coll Engn Dept Elect & Elect Engn Srikakulam 532402 Andhra Prades India Aditya Inst Technol & Management Dept Elect & Elect Engn Srikakulam 532201 Andhra Prades India GMR Inst Technol Dept Elect & Elect Engn Srikakulam 532127 Andhra Prades India

In this article, a bacterial foraging optimisation algorithm (BFOA)-based proportional integral derivative controller with derivative filter (PIDF) is proposed for frequency regulation of multi source hybrid power system. Initially, a two area, unequal area power system with PIDF controllers, are considered. The area 1 comprises of reheat thermal power system incorporated with distributed generation (DG) system comprising of wind turbine generators (WTGs), diesel engine generators (DEGs), fuel cells (FCs), aqua-electrolyser (AE), ultra capacitor (UC) and battery energy storage system (BESS). The area 2 comprises of hydrothermal power system. The gains of the PID controller with derivative filter are optimised by using integral time multiply absolute error (ITAE) criterion. The superiority of PIDF controller is demonstrated by comparing the dynamic responses with integral derivative (ID) and proportional integral (PI) controllers. The simulation results show that the performance of dynamic responses with PIDF controller is superior to others. Further, robustness analysis is performed by varying the system parameters and wind power variations. It is observed from the simulation results that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters.

关键词： bacterial foraging optimisation algorithm BFOA distributed generation frequency regulation proportional integral derivative controller with derivative filter PIDF robustness analysis

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