Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifie...
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ISBN:
(纸本)9781479946655
Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of bacterialforagingoptimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.
In Vehicle Routing Problem with Simultaneous Delivery and Pick-up (VRP_SDP), each customer has both delivery and pick-up demand simultaneously. VRP_SDP is very difficult combinatorial optimization problem. For this re...
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In Vehicle Routing Problem with Simultaneous Delivery and Pick-up (VRP_SDP), each customer has both delivery and pick-up demand simultaneously. VRP_SDP is very difficult combinatorial optimization problem. For this reason, in recent years, it is observed studies focused on metaheuristic methods. In this study, a heuristic solution approach based on bacterial foraging optimization algorithm (BFOA) has been improved and its performance has been evaluated. In the scope of this study VRP_SDP has been solved in order to minimize the total distanced travelled and the results have been tested with the insertion based heuristic that is known in the literature. BFOA obtained good solutions about 24 problems of 40 test problems.
The type-1 fuzzy sets theory was proposed to handle uncertainty in control systems, but some cases showed some liabilities of type-1 fuzzy sets when faced with unpredictable disturbance and uncertainties. Therefore, t...
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The type-1 fuzzy sets theory was proposed to handle uncertainty in control systems, but some cases showed some liabilities of type-1 fuzzy sets when faced with unpredictable disturbance and uncertainties. Therefore, type-2 fuzzy sets were introduced and extended while providing more degrees of freedom in designing criteria. The most important specification of type-2 fuzzy sets is the interval between a superior membership function and an inferior membership function, which is called the footprint of uncertainty. This paper presents a bacterialforagingoptimization approach for optimizing the parameterized membership function. The above criterion is applied to an automatic voltage regulator system and results are presented and compared with the previous method.
Often real world provides some complex optimization problems that can not be easily dealt with available mathematical optimization methods. If the user is not very conscious about the exact solution of the problem in ...
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ISBN:
(纸本)9788132204862
Often real world provides some complex optimization problems that can not be easily dealt with available mathematical optimization methods. If the user is not very conscious about the exact solution of the problem in hand then intelligence emerged from social behavior of social colony members may be used to solve these kind of problems. Based on this concept, Passino proposed an optimization technique known as the bacterial foraging optimization algorithm (BFOA). The foraging behavior of bacteria produces an intelligent social behavior, called as swarm intelligence. Social foraging behavior of Escherichia coli is studied by researchers anti developed a new algorithm named bacterial foraging optimization algorithm (BFOA). BFOA is a widely accepted optimizationalgorithm and currently it is a growing field of research for distributed optimization and control. Since its inception, a lot of research has been carried out to make BFOA more and more efficient and to apply BFOA for different types of problems. This paper presents a review of BFOA modifications and it application areas.
This paper presents a heuristic optimization methodology, namely, bacterialforaging PSO-DE (BPSO-DE) algorithm by integrating bacterial foraging optimization algorithm (BFOA), Particle Swarm optimization (PSO) and Di...
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This paper presents a heuristic optimization methodology, namely, bacterialforaging PSO-DE (BPSO-DE) algorithm by integrating bacterial foraging optimization algorithm (BFOA), Particle Swarm optimization (PSO) and Differential Evolution (DE) for solving non-smooth non-convex Dynamic Economic Dispatch (DED) problem. The DED problem exhibits non-smooth, non-convex nature due to valve-point loading effects, ramp rate limits, spinning reserve capacity, prohibited operating zones and security constraints. The proposed hybrid method eliminates the problem of stagnation of solution with the incorporated PSO and DE operators in original bacterialforagingalgorithm. It achieves global cost by selecting the bacterium with good foraging strategies. The bacteria with good foraging strategies are obtained in the updating process of every chemo-tactic step by the PSO operator. The DE operator fine tunes the solution obtained through bacterialforaging and PSO operator. A 3- and 7-unit systems for static economic dispatch, a 26-bus, 6-generator test system and an IEEE 39-bus, 10-unit New England test systems are considered to show the effectiveness of the proposed method over other methods reported in the literature. (C) 2012 Elsevier Ltd. All rights reserved.
Transmission of high density digital information plays an important role in the present age of communication and information technology. These data are distorted while arriving at the receiver end due to inter symbol ...
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Transmission of high density digital information plays an important role in the present age of communication and information technology. These data are distorted while arriving at the receiver end due to inter symbol interference (ISI) in the channel. The adaptive channel equalizer alleviates this distortion and reconstructs the transmitted data faithfully. In recent years the area of bacterialforagingoptimization (BFO) has drawn attention of many researchers due to its broad applicability to different fields. In this paper, the proposed algorithm combines the foraging mechanism of E-coli bacterium introduced in bacterialforagingalgorithm (BFA) with the swarming pattern of birds in block introduced in Particle Swarm optimization (PSO).It incorporates the merits of the two bio-inspired algorithms to update the weights of the equalizer. Simulation study has been carried out to show superior performance of the proposed equalizer compared to that offered by least mean square (LMS) algorithm and genetic algorithm (GA) based training.
Transmission of high density digital information plays an important role in the present age of communication and information *** data are distorted while arriving at the receiver end due to inter symbol interference（...
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Transmission of high density digital information plays an important role in the present age of communication and information *** data are distorted while arriving at the receiver end due to inter symbol interference（ISI）in the *** adaptive channel equalizer alleviates this distortion and reconstructs the transmitted data *** recent years the area of bacterialforagingoptimization（BFO）has drawn attention of many researchers due to its broad applicability to different *** this paper,the proposed algorithm combines the foraging mechanism of E-coli bacterium introduced in bacterialforagingalgorithm（BFA）with the swarming pattern of birds in block introduced in Particle Swarm optimization（PSO）.It incorporates the merits of the two bio-inspired algorithms to update the weights of the *** study has been carried out to show superior performance of the proposed equalizer compared to that offered by least mean square（LMS）algorithm and genetic algorithm（GA） based training.
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