Hokkaido University · Computer Science
Professor Rui Zhong's research lab specializes in meta-heuristic optimization algorithms and their applications in solving complex, large-scale, and expensive optimization problems. The lab focuses on enhancing the balance between exploration and exploitation in nature-inspired algorithms, developing surrogate-assisted and cooperative coevolution frameworks, and improving robustness in noisy or high-dimensional environments. Key research directions include algorithmic innovation through hybridization with machine learning techniques like Q-learning and surrogate modeling, as well as designing adaptive and hierarchical search strategies for improved convergence and diversity.
Figures are computed from collected data and may differ slightly.
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
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