• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.807-812
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• 2007

Recently, Extreme learning machine(ELM), a novel learning algorithm which is much faster than conventional gradient-based learning algorithm, was proposed for single-hidden-layer feedforward neural networks. The initial input weights and hidden biases of ELM are usually randomly chosen, and the output weights are analytically determined by using Moore-Penrose(MP) generalized inverse. But it has the difficulties to choose initial input weights and hidden biases. In this paper, an advanced method using the bacterial foraging algorithm to adjust the input weights and hidden biases is proposed. Experiment at results show that this method can achieve better performance for problems having higher dimension than others.

• Journal of Environmental Science International
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• v.24 no.2
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• pp.261-266
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• 2015

We have analyzed the external morphology and the environment of the foraging site of Vespertilio sinensis. The external morphology was analyzed by twelve parameters and the environment characteristics of the foraging site was analyzed using GIS 10.1 program. The wing membrane was inserted into the ankle of the hind foot and the wing ratio was 1.42, the middle type between broad-short wing type and long-narrow wing type. The fur color was blackish brown but the guard hair color was whitish. The shape of the ear was a rounded triangle and tragus was a fan shape. This study showed that V. sinensis preferred the deciduous forest of the upper forest zone, where human interference was less. We believed that abundance of insects, depending on water system, was closely related to the use of the foraging site. This result showed that the environment characteristics was very similar to the nature preservation zone including Baekyang valley and Keumsun valley in Naejangsan National Park.

• Journal of Computing Science and Engineering
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• v.8 no.1
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• pp.1-10
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• 2014

The Bacterial Foraging Optimization Algorithm is a swarm intelligence optimization algorithm. This paper first analyzes the chemotaxis, as well as elimination and dispersal operation, based on the basic Bacterial Foraging Optimization Algorithm. The elimination and dispersal operation makes a bacterium which has found or nearly found an optimal position escape away from that position, which greatly affects the convergence speed of the algorithm. In order to avoid this escape, the sphere of action of the elimination and dispersal operation can be altered in accordance with the generations of evolution. Secondly, we put forward an algorithm of an adaptive adjustment of step length we called improved bacterial foraging optimization (IBFO) after making a detailed analysis of the impacts of the step length on the efficiency and accuracy of the algorithm, based on chemotaxis operation. The classic test functions show that the convergence speed and accuracy of the IBFO algorithm is much better than the original algorithm.

• Journal of Environmental Science International
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• v.26 no.7
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• pp.817-826
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• 2017

We analyzed how foraging area use changed in female Pipistrellus abramus during the breeding season. Radio tracking was used to follow 12 female P. abramus in Gyeongju City, from 2013 to 2015. We followed three bats in each of four stages of reproduction: early pregnancy, late pregnancy, lactation, and post-lactation. Our data showed that the usable area of a foraging site and the area that was actually used by bats in that site were different, and foraging site use also differed according to stage of reproduction. The bats used arable land the most, with use rates of 57%, 40.4%, and 73.2% during early pregnancy, late pregnancy, and lactation, respectively. Bats in a post-lactation state did not use arable areas at all and instead foraged over bodies of water 90% of the time. There was no difference in the use of each foraging environment between bats in early pregnancy and late pregnancy. However, bats in late pregnancy and those that were lactating did use arable land to different extents, and bats that were lactating and those that were post-lactation also used arable land and bodies of water to different extents.

• ALGAE
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• v.36 no.1
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• pp.51-60
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• 2021

The sea urchin, Mesocentrotus nudus, is widely distributed in North West Pacific regions. It has a substantial impact on macroalgal communities as a generalist herbivore. This study examined various aspects of its feeding ecology, including algal preference, foraging behaviors, and possible effects of past feeding history on its algal preference. We used six common algal species (Ulva australis, Undaria pinnatifida, Sargassum confusum, Dictyopteris divaricata, Grateloupia elliptica, and Grateloupia angusta) from the east coast of Korea as food choice in a series of indoor aquarium experiments. The first choice of starved M. nudus was exclusively U. pinnatifida, followed by G. elliptica and S. confusum. Unlike large urchins, small urchins equally preferred U. pinnatifida and G. elliptica. On the other hand, Undaria-fed urchins preferred to feed only G. elliptica, although its preference slightly differed over time. We then grouped sea urchins into three categories (starved, Undaria-fed, mixed species-fed) to observe 12-days feeding preference as well as early foraging movements. Foraging behaviors of the three groups were distinctively different, although they could not completely reflect the actual consumption. For example, U. australis was highly attractive, but rarely eaten. Undaria-fed urchins seemed to stay with only S. confusum and U. australis. This study demonstrates that M. nudus shows high flexibility in food preference depending on past feeding history and body size. Its foraging behaviors are also affected by past feeding conditions, exhibiting active chemoreceptive movements.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.155.3-155
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• 2001

Researches of multi-robot system are active in these days. The most remarkable characteristic of multirobot system is that the robots work cooperatively and achieve the task which a single robot cannot do. It is essential to investigate number effect of multi-robot system. In this paper, we chose foraging task and investigated their behavior. At first, we investigated the foraging behavior in case that interaction range is Infinite. Secondly, we investigated the behavior in case that interaction range is finite. In both case, we find out there is an optimum interaction duration.

• The Korean Journal of Ecology
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• v.22 no.2
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• pp.65-68
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• 1999

In December-January of 1996-1997 and 1997-1998, information was gathered about vertical distribution of foraging sites of tits in 34 flocks in coniferous and deciduous forests. There was a significant effect of forest type on the distribution of foraging sites of each species. Habitat was classified into 5 height layers vertically: ground, bushes (usually＜1.5 m, up to 3 m), tree layer 1 (up to 1/3 of tree height), tree layer 2 (1/3-2/3 tree height). and tree layer 3 (＞2/3 tree height). There were differences among species: great tit (Parus major) foraged mostly on the ground, coal tit (P. ater) and long-tailed tit (Acrocephalus caudatus) - on the highest tree layer, marsh tit (P. palustris) was often seen on bushes, and varied tit (P. varius) - in tree layer 2. Smaller species used upper and outer parts of trees. suggesting that, like in most other similar studies. larger dominant species prevented smaller species from using inner parts of trees.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.215-235
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• 2017

Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1092-1097
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• 2005

In this paper, design approach of PID controller with rejection function against external disturbance in motor control system is proposed using bacterial foraging based optimal algorithm. Up to the present time, PID Controller has been used to operate for AC motor drive because of its implementational advantages in practice and simple structure. However, it is not easy to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error in the industrial system with disturbance. To design disturbance rejection tuning, disturbance rejection conditions based on $H_{\infty}$ are illustrated and the performance of response based on the bacterial foraging is computed for the designed PID controller as ITSE (Integral of time weighted squared error). Hence, parameters of PID controller are selected by bacterial foraging based optimal algorithm to obtain the required response

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1716-1722
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.