브로드캐스트 스케줄링을 위한 그룹 탐색 최적화
Group Search Optimizer for Broadcast Scheduling
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초록
그룹탐색최적화 (Group Search Optimizer, GSO) 방법은 동물의 행태를 모방하여 어려운 최적화 문제를 해결할 수 있고 또한, 연속함수, 고차함수, 멀티모달(multi-modal)함수 문제를 해결하는데 좋은 성과를 낼 수 있다고 알려졌다. 본 논문의 목적은 브로드캐스트 스케줄링문제 (Broadcast Scheduling Problem, BSP)가 어려운 NP-complete이기 때문에 GSO를 적용하여 BSP에 적합하도록 개발하고 발전시키는 것이다. 실험결과에 따르면, BSP 최적설계 시 본 논문에서 새롭게 제안하는 방법은 기존 방법과 비교했을 때 효율적이고 경쟁력이 있다. 특히, BSP 문제의 복잡도가 높아질수록 새롭게 제안하는 방법이 기존 방법과 비교하여 성능이 우수한 것으로 분석되었다.
Abstract
The Group Search Optimizer (GSO) is to utilize research in animal behavioral ecology to tackle different hard optimization problems. GSO has superior search performance for continuous, high dimensional and multi-modal function. The objective of this paper is to find the optimum conflict-free broadcast schedule using GSO because Broadcast Scheduling Problem (BSP) is NP-complete binary integer programming. In this paper, experimental results of BSP benchmarking problems demonstrate that GSO is an effective and competitive approach in fairly satisfactory results with respect to solution quality (minimum number of time slots used and maximum utilization). Especially, our proposed method has relatively high performance comparing to previous methods for the high complex BSP.
참고문헌 (17)
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