Modified Bacterial Foraging Optimization for Constrained Portfolio Optimization

This paper presents a novel modiﬁed bacterial foraging optimization(BFO) to solve economic loaddispatch (ELD) problems. BFO isalready successfully employed to solve variousoptimization problems. However original BFOfor small problems with moderate dimensionand searching space is satisfactory. As searchspace and complexity growexponentially in scalable ELD problems, it shows poorconvergence properties. To tackle this complex problem considering itshigh-dimensioned search space, the Evolution Strategies is introduced to thebasic BFO. The chemotactic step is adjusted to have a dynamic non-linearbehavior in order to improve balancing the global and local search. Theproposed algorithm is validated using several thermal generation test systems.The results are compared with those obtained by other algorithms previouslyapplied to solve the problem considering valve-point effects and power losses.

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Improved Bacterial Foraging Optimization Algorithm with Information Communication Mechanism

2014 Tenth International Conference on Computational Intelligence and Security ◽

10.1109/cis.2014.106 ◽

2014 ◽

Cited By ~ 6

Author(s):

Ben Niu ◽

Jing Liu ◽

Ying Bi ◽

Ting Xie ◽

Lijing Tan

Keyword(s):

Optimization Algorithm ◽

Information Communication ◽

Bacterial Foraging Optimization ◽

Bacterial Foraging ◽

Communication Mechanism ◽

Bacterial Foraging Optimization Algorithm

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Real-Time Implementation of the Predictive-Based Control with Bacterial Foraging Optimization Technique for Power Management in Standalone Microgrid Application

Energies ◽

10.3390/en14061723 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1723

Author(s):

Félix Dubuisson ◽

Miloud Rezkallah ◽

Hussein Ibrahim ◽

Ambrish Chandra

Keyword(s):

Real Time ◽

Power Management ◽

Renewable Energy Sources ◽

Optimization Technique ◽

Euler Method ◽

Small Scale ◽

Diesel Generator ◽

Bacterial Foraging Optimization ◽

Time Model ◽

Bacterial Foraging

In this paper, the predictive-based control with bacterial foraging optimization technique for power management in a standalone microgrid is studied and implemented. The heuristic optimization method based on the social foraging behavior of Escherichia coli bacteria is employed to determine the power references from the non-renewable energy sources and loads of the proposed configuration, which consists of a fixed speed diesel generator and battery storage system (BES). The two-stage configuration is controlled to maintain the DC-link voltage constant, regulate the AC voltage and frequency, and improve the power quality, simultaneously. For these tasks, on the AC side, the obtained power references are used as input signals to the predictive-based control. With the help of the system parameters, the predictive-based control computes all possible states of the system on the next sampling time and compares them with the estimated power references obtained using the bacterial foraging optimization (BFO) technique to get the inverter current reference. For the DC side, the same concept based on the predictive approach is employed to control the DC-DC buck-boost converter by regulating the DC-link voltage using the forward Euler method to generate the discrete-time model to predict in real-time the BES current. The proposed control strategies are evaluated using simulation results obtained with Matlab/Simulink in presence of different types of loads, as well as experimental results obtained with a small-scale microgrid.

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Bacterial Foraging Optimization Algorithm with Quorum Sensing Mechanism

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.3844 ◽

2014 ◽

Vol 556-562 ◽

pp. 3844-3848

Author(s):

Hai Shen ◽

Mo Zhang

Keyword(s):

Quorum Sensing ◽

Optimization Algorithm ◽

Complex Adaptive Systems ◽

Adaptive Systems ◽

Cell Communication ◽

Bacterial Foraging Optimization ◽

Sensing Mechanism ◽

Bacterial Foraging ◽

Bacterial Quorum ◽

Bacterial Foraging Optimization Algorithm

Quorum sensing is widely distributed in bacteria and make bacteria are similar to complex adaptive systems, with intelligent features such as emerging and non-linear, the ultimate expression of the adaptive to changes in the environment. Based on the phenomenon of bacterial quorum sensing and Bacterial Foraging Optimization Algorithm, some new optimization algorithms have been proposed. In this paper, it presents research situations, such as environment-dependent quorum sensing mechanism, quorum sensing mechanism with quantum behavior, cell-to-cell communication, multi-colony communication, density perception mechanism. Areas of future emphasis and direction in development were also pointed out.

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