Bibtex does not have the right entry for preprints. Quantumbehaved particle swarm optimization clustering. The spherical description of qubits in quantum computing, a qubit is a twolevel quantum system, described by a twodimensional complex hilbert space. The qqpso algorithm is based on the characteristics of qpso, and uses interpolation based. Pdf quantum particle swarm optimization for electromagnetics. Quantum particle swarm optimization for electromagnetics core. Hybrid quantum particle swarm optimization algorithm and its application free download dear editor, quantumbehaved particle swarm optimization qpso is an evolutionary algorithm with quantum behavior. A collision detection algorithm based on improved quantum.
Diversitypreserving quantum particle swarm optimization. Quantuminspired differential evolution with particle swarm. Its searching performance is better than the original particle swarm optimization algorithm pso, but the control parameters are less and easy to fall into local optimum. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search. Quantum behaved particle swarm optimization with generalized local search operator for global optimization. Optimal tuning of linear quadratic regulators using. Quantum particle swarm optimization is a populationbased metaheuristic that becomes popular in recent years in the field of binary optimization.
Quantum behaved particle swarm optimization qpso is a novel optimization algorithm proposed in the previous work. It has been found to be extremely effective is solving a. Pdf sobol mutated quantum particle swarm optimization. A hybrid method based on krill herd and quantumbehaved particle swarm optimization. Kennedy proposed particle swarm optimization particle swarm optimization, pso 1. In this code, quantum particle swarm optimization qpso is utilized to solve multiobjective combined economic emission dispatch ceed problem formulated using cubic criterion function considering a uni wise maxmax price penalty factor. The particle swarm optimization algorithm is a new methodology in evolutionary computation.
Quantuminspired differential evolution with particle. Particle swarm optimization james kennedy russell eberhart the inventors. Swarm intelligence ken 01, originally entitled particle swarm optimization pso, my friend jim kennedy has devoted three chapters out of eleven to this subject, above all as an illustration of the more general concept of collective intelligence. In this paper, a qpso with weighted mean personal best position and adaptive local attractor alaqpso is proposed to simultaneously enhance the search performance of qpso.
Kishk center of applied electromagnetic systems research, department of electrical engineering, university of mississippi, university, ms 38677, usa abstract a new particle swarm optimization pso technique for electromagnetic applications is proposed. Improved quantumbehaved particle swarm optimization. A quantum particle swarm optimization algorithm with. Motivated by concepts in quantum mechanics and particle swarm optimization pso, quantumbehaved particle swarm optimization qpso was proposed as a variant of pso with better global search ability. Pdf the objective of the research is to extend the potential of standard quantum particle swarm optimization qpso method for. This book is the first to deal exclusively with particle swarm optimization. The particle swarm optimization pso algorithm is a global optimization heuristic method originally introduced by kennedy and eberhart in 1995 kennedy and eberhart, 1995. A quantum particle swarm optimization algorithm with teamwork. This paper explores a type of particle swarm optimization pso methods 3, 4 that are optimization methods for large and nondifferential searching space. In this section, we propose a bloch spherebased quantumbehaved particle swarm optimization algorithm called bqpso. Inspired by the quantum theory and the trajectory analysis of pso, sun et al. Swarm optimization algorithm2020ieee projectspapers. In this new algorithm, the particles were made to perform studies on itself.
Pdf this paper presents a variant of quantum behaved particle swarm optimization qpso named qqpso for solving global optimization problems. This globally convergent algorithm has fewer parameters, a faster convergence rate, and stronger. In computational science, particle swarm optimization pso is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Pso algorithm consists of population of candidate solutions called particles. Particle swarm and quantum particle swarm optimization. Quantum particle swarm optimization for multiobjective. This paper focuses on exploring the applicability of the qpso to data clustering. Particle swarm optimization, quantum behavior, probabilistic boundedness, convergence, parameter selection. The first algorithm uses the random observation method of quantum bit coding particles to simulate. It is based on the concept of social behavior of organsims like school of fish etc. Particle swarm optimization pso is one of the most wellregarded stochastic, populationbased algorithms in the literature of heuristics and. An application of quantuminspired particle swarm optimization to function optimization problems koichiro tazuke, noriyuki muramoto, nobuyuki matsui, and teijiro isokawa abstractquantuminspired particle swarm optimization qpso is an extension of particle swarm optimization pso methods, in which the concept of quantum mechanics is adopted.
Quantumbehaved particle swarm optimization with generalized local search operator for global optimization. To enhance the optimization ability of particle swarm algorithm, a novel quantuminspired particle swarm optimization algorithm is proposed. This later was the basic idea behind quantum behaved particle swarm optimization qpso algorithm, a quantum variant of pso algorithm. Therefore, there may exist the problem of not being able to converge to global optima in the multiobjective psobased. Based on the problem of traditional particle swarm optimization pso easily trapping into local optima, quantum theory is introduced into pso to strengthen particles diversities and avoid the premature convergence effectively. Abstract the particle swarm optimization pso algorithm, as one of the latest algorithms inspired from the nature, was introduced in the mid 1990s and since then, it has been utilized as an optimization tool in various applications, ranging from biological and medical applications to computer graphics and music composition. Pdf a new quantum behaved particle swarm optimization ajith abraham academia. Some techniques like genetic algorithm and neural network based methodology and performance of raised power series arrays have been described in 78 for. Pdf a quantum particle swarm optimization method with fitness. We apply quantumbehaved particle swarm optimization, a global optimization algorithm, to the active noise control system to improve noise reduction performance. In proceedings of international conference on intelligent computing, pp. 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. The mrqpso transplants the qpso on mapreduce model and makes the qpso parallel and distributed through partitioning the search space.
Quantum particle swarm optimization was proposed on trajectory analysis of particle swarm optimization pso and the quantum mechanism 35. Quantuminspired particle swarm optimization for feature. It can be used to solve optimization problems by establishing a potential well at the local attraction point to influence the location of particles. In this method, the particles are encoded by the probability amplitudes of the basic states of the multiqubits system. Quantumbehaved particle swarm optimization with weighted. In his swarm intelligence ken 01, originally entitled particle swarm optimization pso, my friend jim kennedy has devoted three chapters out of eleven to this subject, above all as an illustration of the more general concept of collective. Lncs 6145 improved quantum particle swarm optimization by. As a branch of pso, a probabilistic pso algorithm, which is quantumbehaved pso qpso, was proposed on the quantum mechanics and trajectory analysis of pso. Quantum particle swarm optimization for electromagnetics.
Particle swarm optimization is one of the computation technique to solve rpo problem. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the searchspace according to simple. Classical and quantum perspectives, the authors introduce their concept of quantumbehaved particles inspired by quantum mechanics, which leads to the quantumbehaved particle swarm optimisation qpso algorithm. The quantum particle swarm optimization algorithm is a global convergence guarantee algorithm. Request pdf a quantum particle swarm optimization the particle swarm optimization algorithm is a new methodology in evolutionary computation. Particle swarm optimization pso is a populationbased swarm intelligence algorithm and it has attracted a large number of widespread researchers. Quantumbehaved particle swarm optimization algorithm with. A novel multiobjective quantumbehaved particle swarm. Performance analysis for various particle neighborhood topologies, howpublished easychair preprint no. Reactive power optimization using quantum particle swarm. Each particle knows its own best value pbest and its xy.
Lncs 6145 improved quantum particle swarm optimization. In qpso, each particle has a target point, which is defined as a linear combination of the best previous position of the particle and the global best position. On extending quantum behaved particle swarm optimization. An improved quantum behaved particle swarm optimization algorithm with weighted mean best position. Quantum particle swarm optimization pso algorithm introduced by eberhart and kennedy 1995 is a swarm intelligence based metaheuristic approach inspired by the individual and social behaviour of the flocking birds. The paper proposed teamwork evolutionary strategy for balance global search and local search. Quantumbehaved particle swarm optimization qpso, motivated by analysis from particle swarm optimization pso and quantum mechanics, has shown excellent performance in finding the optimal solutions for many optimization problems. As a branch of pso, a probabilistic pso algorithm, which is quantum behaved pso qpso, was proposed on the quantum mechanics and trajectory analysis of pso.
Hybrid quantum particle swarm optimization algorithm and. Mar 30, 2018 particle swarm optimization pso is one of the most wellregarded stochastic, populationbased algorithms in the literature of heuristics and metaheuristics. Journal of low frequency noise, quantumbehaved particle. Quantum behaved particle swarm optimization qpso, motivated by analysis from particle swarm optimization pso and quantum mechanics, has shown excellent performance in finding the optimal solutions for many optimization problems. In view of the shortcomings of particle swarm optimization such as premature convergence to local optimization, particle swarm optimization algorithm based on quantum gate and particle swarm optimization algorithm based on quantum behavior are studied in this paper. A novel distributed quantumbehaved particle swarm optimization. A quantum particle swarm optimization approach for. Learn particle swarm optimization pso in 20 minutes. Quantum binary particle swarm optimization qbpso the particle swarm optimization algorithm has been proposed by kennedy and eberhart in 1995, developed as a stochastic and iterative algorithm, inspired by a social behavior of animals evolving into swarms ken95. Quantum particle swarm optimization for electromagnetics said mikki and ahmed a.
Through the comparisons of mrqpso andstandardqpso,itcouldbefoundthattheproposed. Quantum computing, particle swarm optimization, multiqubits probability amplitudes encoding, algorithm design 1. Particle swarm and quantum particle swarm optimization applied to dscdma multiuser detection in flat rayleigh channels leonardo d. Mutation operator was proposed in the qpso to help increase the particles diversity and global search capability. Quantuminspired particle swarm optimization algorithm. Pdf a new particle swarm optimization pso technique for electromagnetic applications is proposed. In the initialization phase, the qdepso uses the concepts of quantum computing as the. This is a hack for producing the correct reference. It exploits the concept that the knowledge needed for the search of an optimal solution can be modeled on the basis of observed social behavior. In the quantum space, the position and velocity of the. Hybrid quantum particle swarm optimization algorithm and its.
From the superposition principles, any state of the qubit may be written as. Particle swarm and quantum particle swarm optimization applied to dscdma multiuser detection in flat rayleigh channels. A quantum particle swarm optimization approach for feature. Particle swarm optimization algorithm, denoted bcqpso for selecting a subset of relevant features. To enhance the optimization ability of particle swarm algorithm, a novel quantum inspired particle swarm optimization algorithm is proposed. In this section, we propose a bloch spherebased quantum behaved particle swarm optimization algorithm called bqpso. A quantum particle swarm optimization shuyuan yang, min wang dept. Quantumbehaved particle swarm optimization file exchange. Due to its fast convergence and populationbased nature, particle swarm optimization pso has been widely applied to address the multiobjective optimization problems mops. Quantum computing is an emerging interdisciplinary, combining the information science and quantum mechanics, and its integration with intelligent optimization algorithms begun in the 1990s.
Research on particle swarm optimization algorithm based on. The particle swarms in some way are closely related to cellular automata ca. As a new optimization tool, it is now widely used in combinatorial optimization 2 and numerical op. Improved quantum particle swarm optimization by bloch sphere 7 as each qubit contains two probability amplitudes, each particle occupies two positions in space, therefore it accelerates the searching process. The main idea of qipso is to update the particle position represented as a quantum angle the common velocity update equation in conventional pso is modified to get a new quantum angle which is. Create scripts with code, output, and formatted text in a single executable document. Quantuminspired firefly algorithm with particle swarm. In qpso, the mean best position, defined as the average of the personal best positions of all the particles in a. The main aim of this algorithm is the minimization of the real power loss and to improvise the voltage in the system.
Quantum particle swarm optimization algorithm scientific. Pdf sobol mutated quantum particle swarm optimization ved. Premature convergence, the major problem that confronts evolutionary algorithms, is also encountered with the particle swarm optimization pso algorithm. However, the classical pso has been proved to be not a global search algorithm.
A quantuminspired version of the pso algorithm qpso was proposed very recently 5,6. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than pso. Quantumbehaved particle swarm optimization with novel. Object tracking based on quantum particle swarm optimization. A quantum particle swarm optimization ieee conference publication. Quantum particle swarm optimization for synthesis of non. In this paper, we investigate a novel quantum particle swarm optimization algorithm, which integrates a distancedbased diversitypreserving strategy for population management and a local optimization. Abstract this paper presents a variant of quantum behaved particle swarm optimization qpso named qqpso for solving global optimization problems. In addition, the scheme of detecting the abrupt path change in steady state and. Quantum behaved particle swarm optimization qpso, first introduced by sun et al. The rotation angles of multiqubits are determined based on the local optimum particle and the global optimal particle, and. An improved quantumbehaved particle swarm optimization algorithm with weighted mean best position. Customary smt encounters long time convergence towards the origin and the influence of the dithering. Particle swarm optimization in 1995 james kennedy and russell eberhart proposed an evolutionary algorithm that create a ripple in bioinspired algorithmic approach called particle swarm optimization pso.
Diversitypreserving quantum particle swarm optimization for. According to the characteristics of quantum entanglement and probability amplitude, a quantum bit can 1be represented not jus 0t. A hybrid quantum particle swarm optimization for the. Jan 31, 2020 bibtex does not have the right entry for preprints.
Quantumbehaved particle swarm optimization qpso is a novel optimization algorithm proposed in the previous work. In a simple term it is a method of optimization for continuous nonlinear function. Pdf a new quantum behaved particle swarm optimization. This is a quantuminspired particle swarm optimization, abbreviated as qpso, and this is a pso method with adopting quantum information processing proposed in 5, 6, 7, 8.
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