Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

By Kuk-Hyun Han, Jong-Hwan Kim. IEEE Transactions on Evolutionary Computation, 2002

1,536 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/f20a5269c425d7154ce250b0bf4074d60555686f

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In Artificial Intelligence

1. Yaochu Jin
2. Ran Cheng
3. Xing-yi Zhang
4. Ye Tian
5. Jun Zhang
6. Chao Wang
7. Henry Shu-Hung Chung
8. Yu-hui Shi

In Computational Theory and Mathematics

1. Yaochu Jin
2. Ran Cheng
3. Xing-yi Zhang
4. Ye Tian
5. Jun Zhang
6. Chao Wang
7. Shenkai Gu
8. Henry Shu-Hung Chung

Citations in the corpus

• Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, background): “…approaches in solving complex combinatorial optimization problems, various metaheuristics have been proposed, such as genetic algorithms (GAs) (Han and Kim 2002), differential evolution (DE) (Price et al. 2006), and particle swarm optimization (PSO) (Banks et al. 2008), among many others…”
• Hybrid Genetic Algorithms: A Review (2006, background): “Quantum-inspired genetic algorithms [73]-[75] borrow the concepts of quantum-bits and -states superposition from quantum computing.”
• A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems (2010, methodology): “This is because QEA needs to evaluate a lot of trigonometric functions, and BPSO uses the sigmoid function, which includes an “exp” operator.”
• Quantum-Inspired Particle Swarm Optimization for Valve-Point Economic Load Dispatch (2010, background): “Meanwhile, the QEAs can explore the search space with a smaller number of individuals and exploit global solution within a short span of time [19]–[21].”
• Quantum computing: A taxonomy, systematic review and future directions (2020, methodology): “tum entanglement have been discussed by Hey et al. [44]. Further, the experimental status has been reviewed to get a better understanding of the quantum computer’s physical implementation. Han et al. [43] proposed an algorithm that is evolutionary in nature and is inspired by the principles of quant”
• Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems (2010, methodology): “…have motivated the generation of optimization methods, see Spector, Barnum, Bernstein, and Swamy (1999), Narayanan and Moore (1996), Hogg and Portnov (2000), Protopescu and Barhen (2002), Han and Kim (2002), Hirvensalo (2002), Bulger, Baritopa, and Wood (2003), and Wang, Tang, and Wu (2005).”
• An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems (2014, background): “The knapsack problem, a well-known NP-complete combinatorial optimization problem, can be described as selecting from among various items that are most profitable, given that the knapsack has limited capacity.(25,32) The knapsack problem is to select a subset from the given number of items so as to ”
• An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, background): “Onthe other hand, some existing algorithms also use a real value to represent the probability of a variable being 0 or 1 [51], [52], but the score in SparseEA is completely different.”