Y
Yang Yu
Researcher at University of Toyama
Publications - 24
Citations - 947
Yang Yu is an academic researcher from University of Toyama. The author has contributed to research in topics: Population & Optimization problem. The author has an hindex of 12, co-authored 24 publications receiving 468 citations.
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Chaotic Local Search-Based Differential Evolution Algorithms for Optimization
TL;DR: A novel JADE variant is presented by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem and has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.
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A multi-layered gravitational search algorithm for function optimization and real-world problems
TL;DR: Inspired by the two-layered structure of GSA, four layers consisting of population, iteration-best, personal-best and global-best layers are constructed and dynamically implemented in different search stages to greatly improve both exploration and exploitation abilities of population.
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CBSO: a memetic brain storm optimization with chaotic local search
TL;DR: A novel method which incorporates BSO with chaotic local search (CLS) to make BSO break the stagnation and keep the population’s diversity simultaneously, thus realizing a better balance between exploration and exploitation.
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A hierarchical gravitational search algorithm with an effective gravitational constant
TL;DR: Experimental results demonstrate the effective property of HGSA due to its hierarchical structure and gravitational constant, and the time complexity analysis concludes that HGSA has the same computational efficiency in comparison with other GSAs.
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Global optimum-based search differential evolution
TL;DR: A global optimum-based search strategy is proposed to alleviate the situation that the differential evolution usually sticks into a stagnation, especially on complex problems.