Improved Quantum-Behaved Particle Swarm Optimization
نویسندگان
چکیده
To enhance the performance of quantum-behaved PSO, some improvements are proposed. First, an encoding method based on the Bloch sphere is presented. In this method, each particle carries three groups of Bloch coordinates of qubits, and these coordinates are actually the approximate solutions. The particles are updated by rotating qubits about an axis on the Bloch sphere, which can simultaneously adjust two parameters of qubits, and can automatically achieve the best matching of two adjustments. The optimization process is employed in the n-dimensional space [-1, 1]n, so this approach fits to many optimization problems. The experimental results show that this algorithm is superior to the original quantum-behaved PSO.
منابع مشابه
OPTIMUM SHAPE DESIGN OF DOUBLE-LAYER GRIDS BY QUANTUM BEHAVED PARTICLE SWARM OPTIMIZATION AND NEURAL NETWORKS
In this paper, a methodology is presented for optimum shape design of double-layer grids subject to gravity and earthquake loadings. The design variables are the number of divisions in two directions, the height between two layers and the cross-sectional areas of the structural elements. The objective function is the weight of the structure and the design constraints are some limitations on str...
متن کاملAn Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization
An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPS...
متن کاملImproved Quantum-behaved Particle Swarm Optimization Algorithm with Memory and Singal Step Searching Strategy for Continuous Optimization Problems
Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which has been applied widely for continuous optimization problems. In this paper, we propose an improved quantum-behaved particle swarm optimization with memory according to the means of best position of particles and using sigal step seaching strategy for sovle the multidimentional prob...
متن کاملAn Improved Projection Pursuit Clustering Model and its Application Based on Quantum-behaved Particle Swarm Optimization
Extracting the information with biological significance in amounts of gene expression data is an important research direction. Clustering algorithm in this area has been increasingly widely applied. According to the characteristic of gene expression data, the improved projection pursuit cluster model was introduced in this area and Quantum-behaved Particle Swarm Optimization(QPSO) was put forwa...
متن کاملShort-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization
This paper presents an improved quantum-behaved particle swarm optimization (IQPSO) for short-term combined economic emission hydrothermal scheduling, which is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing emission cost. In this paper, quantum-behaved particle swarm optimization is improved employing heuristic strategies in order to handle the equality const...
متن کامل