The selection of cooperative partners is one of the most important factors when creating a virtual enterprise, and has been extensively researched in recent years. It is difficult to solve the selection problem using ...
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The selection of cooperative partners is one of the most important factors when creating a virtual enterprise, and has been extensively researched in recent years. It is difficult to solve the selection problem using the traditional optimisation methods because the simultaneous consideration of many aspects of the problem is required, such as the running cost, the reaction time and the running risk. In this paper, an adaptive quantum swarm evolutionary algorithm with time-varying acceleration coefficients is proposed for the partner selection optimisation problem. Simulation and experimental results demonstrate that the improved algorithm is valid and outperforms other evolutionaryalgorithms.
DNA encoding is crucial to successful DNA computation, which has been extensively researched in recent years. It is difficult to solve by the traditional optimization methods for DNA encoding as it has to meet simulta...
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DNA encoding is crucial to successful DNA computation, which has been extensively researched in recent years. It is difficult to solve by the traditional optimization methods for DNA encoding as it has to meet simultaneously several constraints, such as physical, chemical and logical constraints. In this paper, a novel quantum chaotic swarmevolutionaryalgorithm (QCSEA) is presented, and is first used to solve the DNA sequence optimization problem. By merging the particle swarm optimization and the chaotic search, the hybrid algorithm cannot only avoid the disadvantage of easily getting to the local optional solution in the later evolution period, but also keeps the rapid convergence performance. The simulation results demonstrate that the proposed quantum chaotic swarmevolutionaryalgorithm is valid and outperforms the genetic algorithm and conventional evolutionaryalgorithm for DNA encoding. (c) 2008 Elsevier Ltd. All rights reserved.
In this paper, a novel quantum swarm evolutionary algorithm (QSE) is presented based on the quantum-inspired evolutionaryalgorithm (QEA). A new definition of Q-bit expression called quantum angle is proposed, and an ...
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In this paper, a novel quantum swarm evolutionary algorithm (QSE) is presented based on the quantum-inspired evolutionaryalgorithm (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. (c) 2006 Elsevier B.V. All rights reserved.
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