• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.432-436
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• 2009

We have developed a new optimization algorithm termed simple bacteria cooperative optimization (sBCO) based on bacteria behavior patterns [1]. In [1], we have introduced the algorithm with basic operations and showed its feasibility with some function optimization problems. Since the sBCO was the first version with only basic operations, its performance was not so good. In this paper, we adopt a new operation, rank replacement, to the sBCO for improving its performance and compare its results to those of the simple genetic algorithm (sGA) which has been well known and widely used as an optimization algorithm. It was found from the experiments with four function optimization problems that the sBCO with rank replacement was superior to the sGA. This shows that our algorithm can be a good optimization algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.241-244
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• 2007

본 논문에서는 박테리아의 주화성에 기초한 Bacteria Cooperative Optimization(BCO) 알고리즘을 소개한다. BCO는 Ant Colony Optimization (ACO)처럼 자연계에 존재하는 생명체의 행동양식을 모방하여 만든 최적화 알고리즘으로 크게 초기화, 측정, 행동결정, 이동으로 구성된다. 우리는 먼저 BCO 알고리즘을 설명하고 2차원 함수 최적화 문제를 이용하여 BCO알고리즘과 Genetic Algorithm(GA) 그리고 Bacterial Foraging for Distributed Optimization(BFO)의 성능 측정 결과를 기술한다. 실험 결과 BCO의 성능이 GA나 BFO보다 우수함을 보였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.392-400
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• 2008

This paper proposes a novel optimization algorithm inspired by bacteria behavior patterns for foraging. Most bacteria can trace attractant chemical molecules for foraging. This tracing capability of bacteria called chemotaxis might be optimized for foraging because it has been evolved for few millenniums. From this observation, we developed a new optimization algorithm based on the chemotaxis of bacteria in this paper. We first define behavior and decision rules based on the behavior patterns of bacteria and then devise an optimization algorithm with these behavior and decision rules. Generally bacteria have a quorum sensing mechanism that makes it possible to effectively forage, but we leave its implementation as a further work for simplicity. Thereby, we call our algorithm a simple bacteria cooperative optimization (BCO) algorithm. Our simple BCO is tested with four function optimization problems on various' parameters of the algorithm. It was found from experiments that the simple BCO can be a good framework for optimization.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.67-75
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• 2010

This paper proposes a bacteria cooperative optimization (BCO) method applying individuals's speed for the performance improvements. All individuals in existing BCO methods move the same length at the same time because their speeds are constant. These methods had the problem that the individuals couldn't find the global optimum effectively because good individuals and bad individuals had same speeds. In order to overcome this problem, we applied the speed concept to the BCO algorithm that individuals moved different lengths according to their speeds assigned by the ranks of individuals according to the fitness of individuals. That is to say, we provide high speeds to bad individuals with low fitness in order to fast move to the areas with high fitness and provide low speeds to good individuals with high fitness because they may be near global optimum. It was found from experimental results of four function optimization problems that the proposed method outperformed the existing methods. Our method showed better performances even than the rank replacement method. This means that applying speed concepts to the individuals for BCO is very effective and efficient.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.23-31
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• 2011

The simple bacteria cooperative optimization (sBCO) algorithm that we developed as one of optimization algorithms has shown relatively good performances, but their performances were limited by step-by-step movement of individuals at a time. In order to solve this problem, we proposed a new method that assigned a speed to each individual according to its rank and it was confirmed that it improved the performances of sBCO in some degree. In addition to the assigning of speed to the individuals, we employed a new mutation operation that most existing evolutionary algorithms used in order to enhance the performances of sBCO in this paper. A specific percent of bad individuals are mutated within an area that is proportion to the rank of the individual in the mutation operation. That is, Gaussian noise of large standard deviation is added as the fitness of individuals is low. From this, the probability that the individuals with lower ranks can be located far from its parent will be increased. This causes that the probability of falling into local optimum areas is decreased and the probability of fast escaping the local optimum areas is increased. From experimental results with four function optimization problems, we showed that the performances of sBCO with mutation operation and individual speed were increased. If the optimization function is quite complex, however, the performances are not always better. We should devise a new method for solving this problem as a further work.