A quantum particle swarm optimization pdf

Abstract: A new variant of particle swarm optimization (PSO), named phase angle-encoded and quantum-behaved particle swarm optimization (?-QPSO), is proposed. Six versions of ?-QPSO using different mappings are presented and compared through their application to solve continuous function optimization problems. 2. Quantum Particle Swarm optimization Algorithm (QPSO) Quantum particle swarm optimization (QPSO) algorithm is a kind of particle swarm algorithm based on the principles of quantum computing [19]. According to the characteristics of quantum entanglement and probability amplitude, a quantum bit can 1be represented not jus 0t . or , but also a Improved Quantum Particle Swarm Optimization by Bloch Sphere 137 As each qubit contains two probability amplitudes, each particle occupies two posi-tions in space, therefore it accelerates the searching process. Mutation operator was proposed in the QPSO to help increase the particles diver-sity and global search capability. In this paper we will be discussing about the working principles of a classical Particle Swarm Optimisation (PSO) algorithm. The nature of a PSO algorithm is similar to that of bird flocking. It also shares many common points with Genetic Algorithm Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper Particle swarm optimization (PSO) is a relatively new algorithm for solving complex real world optimization problems , . This algorithm is easy to implement. In recent times, delta-well quantum PSO (DQPSO) and harmonic-well quantum PSO (HQPSO) algorithms have emerged as new variants of PSO algorithm. A new quantum behaved particle swarm optimization BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference: @Booklet{EasyChair:2515, author = {Arnaud Flori and Hamouche Oulhadj and Patrick Siarry}, title = {Quantum Particle Swarm Optimization: Performance analysis for various particle neighborhood topologies}, howpublished = {EasyChair Preprint no. 2515}, year = {EasyChair, 2020}} particle swarm optimization with time-varying acceleration coef?cients (BPSOTVAC) (Chih et al. 2013) and binary accelerated particle swarm algorithm (BAPSA) (Beheshti et al. 2013). 2.2 Quantum computing Quantum computing is a recent ?eld in computer science which is interested in quantum computers using phenomena In this section, we propose a Bloch sphere-based quantum-behaved particle swarm optimization algorithm called BQPSO. 3.1. The Spherical Description of Qubits In quantum computing, a qubit is a twolevel quantum system- , described by a two-dimensional complex Hilbert space. From the superposition principles, any state of the qubit may be written as Sun proposed the quantum particle swarm optimization algorithm from the perspective of quantum mechanics (Clerc & Kennedy, 2002). Subsequently, the algorithm entered a stage of rapid development and was applied in various fields. An Image Enhancement Method Using the Quantum-Behaved Particle Swarm Optimization with an Adaptive Strategy Xiaoping Su , 1 Wei Fang , 2 Qing Shen , 3 and Xiulan Hao 3 1 School of Computer & Software Engineering, Nanjing Institute of Industry Technology, Nanjing 210046, China An Image Enhancement Method Using the Quantu