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Bin Xin
Researcher at Beijing Institute of Technology
Publications - 109
Citations - 1887
Bin Xin is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Computer science & Motion planning. The author has an hindex of 19, co-authored 92 publications receiving 1256 citations.
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Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy
TL;DR: This paper attempts to comprehensively review the existing hybrids based on DE and PSO with the goal of collection of different ideas to build a systematic taxonomy of hybridization strategies and indicates several promising lines of research that are worthy of devotion in future.
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Optimal Contraction Theorem for Exploration–Exploitation Tradeoff in Search and Optimization
TL;DR: By analyzing a typical contraction-based three-stage optimization process, optimal contraction theorem is presented to show that T:Er&Ei depends on the optimization hardness of problems to be optimized and random sampling will become an outstanding optimizer when optimization hardness reaches a certain degree.
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Coordination Between Unmanned Aerial and Ground Vehicles: A Taxonomy and Optimization Perspective
TL;DR: It is shown how different types of UAGVSs can be built to realize the goal of a common task, that is target tracking, and how optimization problems can be formulated for a UAG VS to perform specific tasks.
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A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets
TL;DR: Numerical simulations exhibit that the algorithm proposed in this paper can find high-quality solutions to the DTSPN with lower computational cost and produce significantly improved performance over the other algorithms.
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An Efficient Rule-Based Constructive Heuristic to Solve Dynamic Weapon-Target Assignment Problem
TL;DR: An efficient rule-based heuristic to solve asset-based dynamic weapon-target assignment (DWTA) problems to directly achieve weapon assignment, without large number of function evaluations is proposed.