Hokkaido University · 컴퓨터과학
Rui Zhong 교수의 연구실은 대규모·비용이 큰 최적화 문제를 해결하기 위한 혁신적인 메타휴리스틱 알고리즘과 보조 모델 기반 최적화 기법을 중심으로 연구를 전개하고 있습니다. 특히, 탐색과 집약의 균형을 높이기 위한 Q-러닝 기반 개선 알고리즘, 계층적 분할 전략, 서rogate 앙상블 기법 등을 통해 문제의 복잡성과 노이즈 환경에서도 뛰어난 수렴 성능을 확보하고자 합니다. 연구는 실용적이고 효율적인 최적화 솔루션을 설계하는 데 초점을 맞추고 있으며, 다양한 산업 응용 가능성을 고려한 알고리즘 설계가 특징입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Vegetation evolution (VEGE) is a newly proposed meta-heuristic algorithm (MA) with excellent exploitation but relatively weak exploration capacity. We thus focus on further balancing the exploitation and the exploration of VEGE well to improve the overall optimization performance. This paper proposes an improved Q-learning based VEGE, and we design an exploitation archive and an exploration archive to provide a variety of search strategies, each archive contains four efficient and easy-implement
Abstract This paper proposes a novel algorithm named surrogate ensemble assisted differential evolution with efficient dual differential grouping (SEADECC-EDDG) to deal with large-scale expensive optimization problems (LSEOPs) based on the CC framework. In the decomposition phase, our proposed EDDG inherits the framework of efficient recursive differential grouping (ERDG) and embeds the multiplicative interaction identification technique of Dual DG (DDG), which can detect the additive and multip
This paper introduces a hierarchical RIME algorithm with multiple search preferences (HRIME-MSP) to tackle complex optimization problems. Although the original RIME algorithm is recognized as an efficient metaheuristic algorithm (MA), its reliance on a single, simplistic search operator poses limitations in maintaining population diversity and avoiding premature convergence. To address these challenges, we propose a hierarchical partition strategy that categorizes the population into superior, b
Abstract Many optimization problems suffer from noise, and the noise combined with the large-scale attributes makes the problem complexity explode. Cooperative coevolution (CC) based on divide and conquer decomposes the problems and solves the sub-problems alternately, which is a popular framework for solving large-scale optimization problems (LSOPs). Many studies show that the CC framework is sensitive to decomposition, and the high-accuracy decomposition methods such as differential grouping (
Abstract This paper proposes a novel surrogate ensemble-assisted hyper-heuristic algorithm (SEA-HHA) to solve expensive optimization problems (EOPs). A representative HHA consists of two parts: the low-level and the high-level components. In the low-level component, we regard the surrogate-assisted technique as a type of search strategy and design the four search strategy archives: exploration strategy archive, exploitation strategy archive, surrogate-assisted estimation archive, and mutation st
