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Changhe Li

Researcher at China University of Geosciences (Wuhan)

Publications -  118
Citations -  4361

Changhe Li is an academic researcher from China University of Geosciences (Wuhan). The author has contributed to research in topics: Optimization problem & Evolutionary algorithm. The author has an hindex of 25, co-authored 90 publications receiving 3351 citations. Previous affiliations of Changhe Li include University of Leicester.

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CCFR2: A more efficient cooperative co-evolutionary framework for large-scale global optimization

TL;DR: An improved version of CCFR named CCFR2 is proposed, which can specify unequal-sized subpopulations for optimizing different subcomponents of variables and is a more efficient CC framework for solving large-scale optimization problems.
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Particle swarm optimisation with simple and efficient neighbourhood search strategies

TL;DR: A novel particle swarm optimiser (PSO) called PSO with simple and efficient neighbourhood search strategies (NSPSO), which employs one local and two global neighbourhoodsearch strategies.
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Comparative assessment of force, temperature, and wheel wear in sustainable grinding aerospace alloy using biolubricant

TL;DR: In this paper , a comparative assessment of biolubricant physicochemical properties was performed using titanium alloy, nickel-based alloy, and high-strength steel, and the results indicated that biol lubricant is a potential replacement of traditional cutting fluids because of its improved lubrication and cooling performance.

Benchmark Generator for the IEEE WCCI-2012 Competition on Evolutionary Computation for Dynamic Optimization Problems

TL;DR: Liang et al. as discussed by the authors used the generalized dynamic benchmark generator (GDBG) to construct dynamic environments for all the three solution spaces, including binary space, real space, and combinatorial space.
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A Sustainable Methodology Using Lean and Smart Manufacturing for the Cleaner Production of Shop Floor Management in Industry 4.0

TL;DR: In this paper , the authors developed a methodology for cleaner production management using lean and smart manufacturing in industry 4.0, which was validated by production enhancement achieved in two case study investigations within the automobile manufacturing industry and a mining machinery assembly unit.