The Experts below are selected from a list of 96321 Experts worldwide ranked by ideXlab platform
Weicai Zhong - One of the best experts on this subject based on the ideXlab platform.
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Constrained Optimization Using Organizational Evolutionary Algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Jing Liu, Weicai ZhongAbstract:This paper designs a new kind of structured population and Evolutionary operators to form a novel Algorithm, Organizational Evolutionary Algorithm (OEA), for solving constrained optimization problems. A simple and non problem-dependent technique is incorporated into OEA to handle the constraints. In OEA, a population consists of organizations, and an organization consists of individuals. All Evolutionary operators are designed to simulate the interaction among organizations. In experiments, 4 well-studied engineering design problems are used to test the performance of OEA. The results show that OEA obtains good results both in the solution quality and the computational cost.
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SEAL - Constrained optimization using organizational Evolutionary Algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Jing Liu, Weicai ZhongAbstract:This paper designs a new kind of structured population and Evolutionary operators to form a novel Algorithm, Organizational Evolutionary Algorithm (OEA), for solving constrained optimization problems. A simple and non problem-dependent technique is incorporated into OEA to handle the constraints. In OEA, a population consists of organizations, and an organization consists of individuals. All Evolutionary operators are designed to simulate the interaction among organizations. In experiments, 4 well-studied engineering design problems are used to test the performance of OEA. The results show that OEA obtains good results both in the solution quality and the computational cost.
Chunguang Zhou - One of the best experts on this subject based on the ideXlab platform.
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a novel quantum swarm Evolutionary Algorithm and its applications
Neurocomputing, 2007Co-Authors: Yan Wang, Xiaoyue Feng, Yanxin Huang, Wengang Zhou, Yanchun Liang, Chunguang ZhouAbstract:In this paper, a novel quantum swarm Evolutionary Algorithm (QSE) is presented based on the quantum-inspired Evolutionary Algorithm (QEA). A new definition of Q-bit expression called quantum angle is proposed, and an improved particle swarm optimization (PSO) is employed to update the quantum angles automatically. The simulated results in solving 0-1 knapsack problem show that QSE is superior to traditional QEA. In addition, the comparison experiments show that QSE is better than many traditional heuristic Algorithms, such as climb hill Algorithm, simulation anneal Algorithm and taboo search Algorithm. Meanwhile, the experimental results of 14 cities traveling salesman problem (TSP) show that it is feasible and effective for small-scale TSPs, which indicates a promising novel approach for solving TSPs.
Jong-hwan Kim - One of the best experts on this subject based on the ideXlab platform.
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recombinant rule selection in Evolutionary Algorithm for fuzzy path planner of robot soccer
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence, 2006Co-Authors: Jonghwan Park, Jong-hwan Kim, Daniel Stonier, Byungha Ahn, Moongu JeonAbstract:A rule selection scheme of Evolutionary Algorithm is proposed to design fuzzy path planner for shooting ability in robot soccer. The fuzzy logic is good for the system that works with ambiguous information. Evolutionary Algorithm is employed to deal with difficulty and tediousness in deriving fuzzy control rules. Generic Evolutionary Algorithm, however, evaluate and select chromosomes which may include inferior genes, and generate solutions with uncertainty. To ameliorate this problem, we propose a recombinant rule selection method for gene level selection, which grades genes at the same position in the chromosomes and recombine new parent for next generation. The method was evaluated with application of designing the fuzzy path planner, where each fuzzy rule was encoded as a gene. Simulation and experimental results showed the effectiveness and the applicability of the proposed method.
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Quantum-inspired Evolutionary Algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation, 2002Co-Authors: Kuk-hyun Han, Jong-hwan KimAbstract:This paper proposes a novel Evolutionary Algorithm inspired by quantum computing, called a quantum-inspired Evolutionary Algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other Evolutionary Algorithms, QEA is also characterized by the representation of the individual, evaluation function, and population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments were carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic Algorithm.
Kwang Y Lee - One of the best experts on this subject based on the ideXlab platform.
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quantum inspired Evolutionary Algorithm for real and reactive power dispatch
IEEE Transactions on Power Systems, 2008Co-Authors: J G Vlachogiannis, Kwang Y LeeAbstract:This paper presents an Evolutionary Algorithm based on quantum computation for bid-based optimal real and reactive power (P-Q) dispatch. The proposed quantum-inspired Evolutionary Algorithm (QEA) has applications in various combinatorial optimization problems in power systems and elsewhere. In this paper, the QEA determines the settings of control variables, such as generator outputs, generator voltages, transformer taps and shunt VAR compensation devices for optimal P-Q dispatch considering the bid-offered cost. The Algorithm is tested on the IEEE 30-bus system, and the results obtained by the QEA are compared with those obtained by other modern heuristic techniques: ant colony system (ACS), enhanced GA and simulated annealing (SA) as well as the original QEA. Furthermore, in order to demonstrate the applicability of the proposed QEA, it is also implemented in a different problem, which is to minimize the real power losses in the IEEE 118-bus transmission system. The comparisons demonstrate an improved performance of the proposed QEA.
Jing Liu - One of the best experts on this subject based on the ideXlab platform.
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Constrained Optimization Using Organizational Evolutionary Algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Jing Liu, Weicai ZhongAbstract:This paper designs a new kind of structured population and Evolutionary operators to form a novel Algorithm, Organizational Evolutionary Algorithm (OEA), for solving constrained optimization problems. A simple and non problem-dependent technique is incorporated into OEA to handle the constraints. In OEA, a population consists of organizations, and an organization consists of individuals. All Evolutionary operators are designed to simulate the interaction among organizations. In experiments, 4 well-studied engineering design problems are used to test the performance of OEA. The results show that OEA obtains good results both in the solution quality and the computational cost.
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SEAL - Constrained optimization using organizational Evolutionary Algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Jing Liu, Weicai ZhongAbstract:This paper designs a new kind of structured population and Evolutionary operators to form a novel Algorithm, Organizational Evolutionary Algorithm (OEA), for solving constrained optimization problems. A simple and non problem-dependent technique is incorporated into OEA to handle the constraints. In OEA, a population consists of organizations, and an organization consists of individuals. All Evolutionary operators are designed to simulate the interaction among organizations. In experiments, 4 well-studied engineering design problems are used to test the performance of OEA. The results show that OEA obtains good results both in the solution quality and the computational cost.
