The Experts below are selected from a list of 114 Experts worldwide ranked by ideXlab platform
Zhi-feng Yin - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Quantum Evolutionary Algorithms Based on Particle Swarm Theory
2006 1ST IEEE Conference on Industrial Electronics and Applications, 2006Co-Authors: Ya-fei Tian, Zhi-feng YinAbstract:Inspired by the idea of hybrid optimization algorithms, this paper proposes two hybrid quantum Evolutionary algorithms (QEA) based on combining QEA with particle swarm optimization (PSO) to improve the performance of QEA . The main idea of the first method called PSEQEA is to embed the Evolutionary equation of PSO in the Evolutionary Operator of QEA; while the main idea of the second method called PSSQEA is to replace the Evolutionary Operator of QEA using the Evolutionary equation of PSO which is redefined the meanings of the original Evolutionary equations. The experiment results of the knapsack problem, the function optimization problems and multiuser detection problem show that the both of the proposed methods not only have simpler algorithm structure, but also perform better than conventional QEA and PSO in terms of ability of global optimum
Elena Yarovaya - One of the best experts on this subject based on the ideXlab platform.
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On the Number of Positive Eigenvalues of the Evolutionary Operator of Branching Random Walk
Branching Processes and Their Applications, 2016Co-Authors: Ekaterina Antonenko, Elena YarovayaAbstract:We consider a continuous-time branching random walk on a multidimensional lattice with finite variance of jumps and a finite set of the particle generation centers, i.e. branching sources. The main object of interest is the Evolutionary Operator for the mean number of particles both at an arbitrary point and on the entire lattice. It is shown that the amount of its positive eigenvalues, counting their multiplicity, does not exceed the amount of branching sources on the lattice, while the maximal of these eigenvalues is always simple. We present also an example demonstrating that the symmetry of the spatial configuration of sources can lead to appearance of multiple lower eigenvalues in the spectrum of the Evolutionary Operator.
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Positive Discrete Spectrum of the Evolutionary Operator of Supercritical Branching Walks with Heavy Tails
Methodology and Computing in Applied Probability, 2016Co-Authors: Elena YarovayaAbstract:We consider a continuous-time symmetric supercritical branching random walk on a multidimensional lattice with a finite set of the particle generation centres, i.e. branching sources. The main object of study is the Evolutionary Operator for the mean number of particles both at an arbitrary point and on the entire lattice. The existence of positive eigenvalues in the spectrum of an Evolutionary Operator results in an exponential growth of the number of particles in branching random walks, called supercritical in the such case. For supercritical branching random walks, it is shown that the amount of positive eigenvalues of the Evolutionary Operator, counting their multiplicity, does not exceed the amount of branching sources on the lattice, while the maximal of these eigenvalues is always simple. We demonstrate that the appearance of multiple lower eigenvalues in the spectrum of the Evolutionary Operator can be caused by a kind of ‘symmetry’ in the spatial configuration of branching sources. The presented results are based on Green’s function representation of transition probabilities of an underlying random walk and cover not only the case of the finite variance of jumps but also a less studied case of infinite variance of jumps.
Ya-fei Tian - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Quantum Evolutionary Algorithms Based on Particle Swarm Theory
2006 1ST IEEE Conference on Industrial Electronics and Applications, 2006Co-Authors: Ya-fei Tian, Zhi-feng YinAbstract:Inspired by the idea of hybrid optimization algorithms, this paper proposes two hybrid quantum Evolutionary algorithms (QEA) based on combining QEA with particle swarm optimization (PSO) to improve the performance of QEA . The main idea of the first method called PSEQEA is to embed the Evolutionary equation of PSO in the Evolutionary Operator of QEA; while the main idea of the second method called PSSQEA is to replace the Evolutionary Operator of QEA using the Evolutionary equation of PSO which is redefined the meanings of the original Evolutionary equations. The experiment results of the knapsack problem, the function optimization problems and multiuser detection problem show that the both of the proposed methods not only have simpler algorithm structure, but also perform better than conventional QEA and PSO in terms of ability of global optimum
Jinglei Guo - One of the best experts on this subject based on the ideXlab platform.
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an ant colony optimization algorithm with Evolutionary Operator for traveling salesman problem
Intelligent Systems Design and Applications, 2006Co-Authors: Jinglei Guo, Wei LiuAbstract:Ant colony optimization (ACO) is an optimization computation inspired by the study of the ant colonies? behavior. The combinational optimization process sometimes is based on the pheromone model and solution construction process. It remains a computational bottleneck because the ACO algorithm costs too much time to find an optimal solution for large-scale optimization problems. In this paper, a quickly convergent method of the ACO algorithm with Evolutionary Operator (ACOEO) is presented. In the method, crossover and mutation Operator together provide a search capability that enhance rate of convergence. In addition, we adopt a dynamic selection means based on the fitness of each ant. The tours of better ants have high opportunity to obtain pheromone updating. Finally, our research clearly shows that ACOEO has the property of effectively guiding the search towards promising regions in the search space. The computer simulations demonstrate that the convergence speed and optimization performance are better than the ACO algorithm.
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ISDA (1) - An Ant Colony Optimization Algorithm with Evolutionary Operator for Traveling Salesman Problem
Sixth International Conference on Intelligent Systems Design and Applications, 2006Co-Authors: Jinglei GuoAbstract:Ant colony optimization (ACO) is an optimization computation inspired by the study of the ant colonies? behavior. The combinational optimization process sometimes is based on the pheromone model and solution construction process. It remains a computational bottleneck because the ACO algorithm costs too much time to find an optimal solution for large-scale optimization problems. In this paper, a quickly convergent method of the ACO algorithm with Evolutionary Operator (ACOEO) is presented. In the method, crossover and mutation Operator together provide a search capability that enhance rate of convergence. In addition, we adopt a dynamic selection means based on the fitness of each ant. The tours of better ants have high opportunity to obtain pheromone updating. Finally, our research clearly shows that ACOEO has the property of effectively guiding the search towards promising regions in the search space. The computer simulations demonstrate that the convergence speed and optimization performance are better than the ACO algorithm.
Mingfang Jiang - One of the best experts on this subject based on the ideXlab platform.
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Fine-Grained Ensemble of Evolutionary Operators for Objective Space Partition Based Multi-Objective Optimization
IEEE Access, 2021Co-Authors: Xuefeng Hong, Mingfang JiangAbstract:Decomposition-based multi-objective optimization algorithms have been widely accepted as a competitive technique in solving complex multi-objective optimization problems (MOPs). Motivated by the facts that Evolutionary Operators are sensitive to the properties of problems, and even different search stages of an Evolutionary Operator often pose distinct properties when solving a problem. So far, numerous ensemble approaches have been designed to adaptively choose Evolutionary Operators for evolving population during different optimization stages. Then, during one stage, all the subproblems/subspaces in these existing ensemble approaches use the same Evolutionary Operator. But, for a complex MOP, the properties of its subproblems/subspaces are different. Based on the fact that existing ensemble approaches ignore this point, this article develops a fine-grained ensemble approach, namely FGEA, to choose suitable Evolutionary Operators for different subspaces during one generation. To be specific, the local and global contributions for each Evolutionary Operator in each subproblem/subspace are first defined. Then, an adaptive strategy is designed to encourage Evolutionary Operators making more contributions to obtain more opportunities to generate more offspring solutions. When choosing an Evolutionary Operator for a subspace, the proposed adaptive strategy considers both the local and global contributions of the Evolutionary Operators. Finally, based on 35 complex MOPs, we evaluate the effectiveness of the proposed FGEA by comparing it with five baseline algorithms. The experimental results reveal the competitive performance of the FGEA, which achieves the lowest inverted generational distance (IGD) values and the highest hypervolume values on 20 and 19 MOPs, respectively.
