As world become closer and many people travel around the world, infectious diseases are likely to be prevalent, Dengue, chikungunya, Zika virus would become endemic diseases in many places in the world as climate is more likely to be fit to mosquito vectors than before. As a matter of fact, Aedes albopictus mediating these diseases became endemic species in Japan and France recently. One of the things that we employ is that we need to apply the Internet of Things concept in disease vector control soon to increase the accuracy of vector species in time as they are present unexpectedly. Here in this presentation, many lines of current state-of-the-art technologies to control the disease vectors using big data informatics application and machine learning strategies. With this trend, this presentation will add future directions of integrated vector management with new research endeavors to monitor the population of disease vectors concisely and precisely, Application of internet of things (IoT) in disease vector management will be addressed in this presentation. This will change future research and development of apparatus to monitor and control disease vectors.

본 연구에서는 방향벡터(direction vector)를 이용한 지역 탐색법과 유전자 알고리즘을 결합한 새로운 알고리즘인 D-GA를 제안한다. 새로운 개체(individual)를 찾기 위한 방향벡터로는 진화과정 중에 습득되는 정보를 활용하기 위한 학습방향벡터(Loaming direction vector)와 진화와는 무관하게 한 개체의 주변을 탐색하는 랜덤방향벡터(random direction vector) 등 두 가지를 구성하였다. 그리고, 10 부재 트러스 설계 문제에 단순 유전자 알고리즘과 D-GA를 적용하여 최적화를 수행하였고, 그 결과를 비교 검토함으로써 단순 GA에 비하여 D-GA의 정확성 및 효율성이 향상되었음을 확인하였다.