蜜蜂は社会性昆虫の一種であり，複数の蜜源に対し８の字ダンスを用いて適切な採蜜蜂の割り振りを行うことで有名である．しかし，採蜜蜂が貯蔵蜂との連携において採蜜蜂同士の採蜜基準自体を自律的に調整させていることはあまり知られていない．本研究ではこの採蜜基準の自動調整を模した閾値の調整を取り入れた最適化アルゴリズム Bee Total Optimization with Personal Judgment（BTO-PJ）を提案する．BTO-PJ はマルチエージェントシステムであり，採蜜蜂を模したエージェントは他のエージェントとの情報交換によって最適解を探索する．これを巡回セールスマン問題に適用した結果を示した．
Handwriting movement includes a lot of intelligence such as identity, personality, tacit or explicit skills and so on. Especially, identity authentication by biometric technologies is currently gaining popularity over traditional password based security systems. Online handwritten signature verification is a long time candidate in this area of research. In the present paper handwritten signature is considered as a behavioral biometric attribute produced by the dynamics of human hand movement. We propose an approach for improving verification accuracy from the analysis of reconstruction of the dynamics from multi-dimensional time series generated from online handwritten signature.The approach contains the proposal of a new similarity measure cross translation error (CTE) to measure similarity of local dynamics between two time series and an integration of the proposed measure with conventional Dynamic Time Warping (DTW) which belongs to global dynamics measure. The simulation results with a small scale generated data set and the benchmark data set used in Signature Verification Contest (SVC) 2004 show that the proposed measure is effective in detecting individuality from handwritten time series compared to the popular DTW based measures. The integrated approach is also promising for increasing verification accuracy.