It is a complex and highly nonlinear constrained optimization problem. 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. Guide to conducting your own research clarifies how a motivated researcher could add constraints or make other improvements. Power system restoration using particle swarm optimization. Particle swarm and quantum particle swarm optimization applied to dscdma multiuser detection in flat rayleigh channels leonardo d. A novel chaotic quantumbehaved particle swarm optimization based on lateral inhibition licqpso is proposed in this paper, which is used to solve complicated image matching problems. A novel chaotic quantum behaved particle swarm optimization based on lateral inhibition licqpso is proposed in this paper, which is used to solve complicated image matching problems.
A quantum particle swarm optimization shuyuan yang, min wang dept. Request pdf a quantum particle swarm optimization the particle swarm optimization algorithm is a new methodology in evolutionary computation. An improved quantum behaved particle swarm optimization with elitist breeding ebqpso for unconstrained optimization is presented and empirically studied in this paper. The quantuminspired particle swarm optimization qpso algorithm has been developed to find and track an optimum for dynamic optimization problems. Svm parameters tuning with quantum particles swarm optimization. Quantum particle swarm optimization qpso is a global convergence guaranteed search method which introduces the quantum theory into the basic particle. A novel algorithm called quantum parallel particle swarm optimization qppso is proposed to solve the parameter estimation for fractionalorder chaotic systems. Quantum particle swarm optimization algorithm scientific. Though qpso has been shown to be effective, despite its simplicity, it does introduce an additional control parameter. Parameter estimation of fractionalorder chaotic systems by. A quantum particle swarm optimization algorithm based on self. In ebqpso, 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. Object tracking based on quantum particle swarm optimization. Lncs 6145 improved quantum particle swarm optimization by.
The system is initialized with a population of random solutions and searches for optima by updating. Apr 16, 20 quantumbehaved particle swarm optimization qpso, like other populationbased algorithms, is intrinsically parallel. On extending quantum behaved particle swarm optimization. Particle swarm optimization pso is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling. Particle swarm optimization pso eberhart and kennedy, 1995a, eberhart and kennedy, 1995b is an evolutionary optimization algorithm proposed by kennedy and eberhart in the mid 1990s while attempting to simulate the choreographed, graceful motion of swarms of birds as part of a sociocognitive study investigating. Svm parameters tuning with quantum particles swarm.
The particle swarm optimization research toolbox is currently designed to handle continuous, singleobjective optimization problems. Quantumbehaved particle swarm optimization algorithm with. Originally, these two started out developing computer software simulations of birds flocking around food. Improved quantum particle swarm optimization by bloch sphere. The particle swarm optimization algorithm is a new methodology in evolutionary computation. Review articlea comprehensive survey on particle swarm.
Lncs 6145 improved quantum particle swarm optimization. Inspired by the flocking and schooling patterns of birds and fish, particle swarm optimization pso was invented by russell eberhart and james kennedy in 1995. Particle swarm optimization pso is a kind of stochastic optimization algorithms proposed by kennedy and eberhart 1 that can be easily implemented and is computationally inexpensive. Multiobjective optimization subvector techniques comparison over problem spaces hybrids jim kennedy russ eberhart. Quantum particle swarm optimization for multiobjective. 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. A quantuminspired version of the pso algorithm qpso was proposed very. Quantumbehaved particle swarm optimization qpso algorithm is a variant of the traditional particle swarm optimization pso. 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. Such experiments with mixed factors seem to be of increasing interest, as can been seen in recent work in computer experiments see, for example, deng et al.
This book is the first to deal exclusively with particle swarm optimization. The parallel characteristic of quantum computing is used in qppso. 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. 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. Particle swarm and quantum particle swarm optimization. A swarm optimization genetic algorithm based on quantum. Selfadaptive quantum particle swarm optimization for dynamic. Dynamiccontext cooperative quantumbehaved particle. 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.
This paper analyzes the main factors that impact the search ability of qpso and converts the particle movement formula to the. Quantumbehaved particle swarm optimization using qlearning. The quantum inspired particle swarm optimization qpso algorithm has been developed to find and track an optimum for dynamic optimization problems. An improved quantumbehaved particle swarm optimization. Mar 27, 20 get notifications on updates for this project. Quantum behaved particle swarm optimization qpso algorithm is a variant of the traditional particle swarm optimization pso. A quantum particle swarm optimization ieee conference publication. 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. Comparison of particle swarm optimization and genetic algorithm in rational function model optimization somayeh yavari a, mohammad javad valadan zoej, mehdi mokhtarzadea, ali mohammadzadeha a k. Keywords transmission line, a shunt capacitor bank or a generation unit is artificial intelligence, distribution system, particle swarm optimization, power system restoration. In qpso, the only parameter contractionexpansion coefficient.
Parameter estimation of fractionalorder chaotic systems. Quantumbehaved particle swarm optimization using q. The paper proposed teamwork evolutionary strategy for balance global search and local search. 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. The qpso that was originally developed for continuous search spaces outperforms the traditional pso in search ability. Pdf object tracking based on quantum particle swarm optimization. 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. Quantumbehaved particle swarm optimization qpso has shown excellent performance in solving optimization problems which inspired by analysis from particle swarm optimization pso and quantum mechanics. It has been found to be extremely effective is solving a. Mathematical modelling and applications of particle swarm. Individuals interact with one another while learning from their own experience, and gradually the population members move into better regions of the problem space. 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.
Particle swarm optimization as described by the inventers james kennedy and russell eberhart, particle swarm algorithm imitates human or insects social behavior. Particle swarm optimization pso is a populationbased stochastic optimization algorithm motivated by intelligent collective behavior. Premature convergence, the major problem that confronts evolutionary algorithms, is also encountered with the particle swarm optimization pso algorithm. When the cycles are completed, the stored best particle solution is the expected particle solution with. This paper employs qlearning strategy and presents a novel. On extending quantum behaved particle swarm optimization to. Particle swarm optimization pso was originally designed and introduced by eberhart and kennedy. This later was the basic idea behind quantum behaved particle swarm optimization qpso algorithm, a quantum variant of pso algorithm. A quantum particle swarm optimization algorithm with. Origin of particle swarm optimization particle swarm optimization pso is a population based stochastic optimization technique developed by dr.
Quantum particle swarm optimization for electromagnetics core. Mutation operator was proposed in the qpso to help increase the particles diversity and global search capability. This paper analyzes the main factors that impact the search ability of qpso and converts the particle movement formula to the mutation condition by. An improved quantumbehaved particle swarm optimization with elitist breeding ebqpso for unconstrained optimization is presented and empirically studied in this paper. Table i summary of the probability density functions pdf and update equations. Toosi university of technology, geodesy and geomatics eng.
Index terms broadband array, frequency invariant pattern, first null beam width, quantum particle swarm optimization, side lobe level, firefly algorithm. Particle swarm optimization pso is a heuristic global optimization method, proposed. A quantum particle swarm optimization algorithm based on selfupdating mechanism. Quantum particle swarm optimization for electromagnetics.
Oct 18, 2018 a rudimentary pso variant, with just one implicit fixed topology, which is far to be the best one for multimodal problems. Quantumbehaved particle swarm optimization, diversitycontrolled, local search, global optimization. A chaotic quantumbehaved particle swarm optimization based. James kennedy in 1995, inspired by social behaviour of bird flocking or fish schooling. Table 2 is in same format like table 1 but it is for those test files which works.
Quantum particle swarm optimization for electromagnetics said mikki and ahmed a. Introduction as modern society has become increasingly dependent on. Particle swarm optimization james kennedy russell eberhart the inventors. 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. Quantum behaved particle swarm optimization qpso for multi.
A chaotic quantumbehaved particle swarm optimization. Pso shares many similarities with evolutionary computation techniques such as genetic algorithms ga. Group search optimization for applications in structural design. Dynamiccontext cooperative quantumbehaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation yangyang lia, licheng jiaoa, ronghua shanga and rustam stolkinb akey laboratory of intelligent perception and. The quantum particle swarm optimization algorithm is a global convergence guarantee algorithm. As one of the metaheuristic algorithms inspired by biological behaviors, particle swarm optimization pso has been successfully applied to image matching. Pdf in computer vision domain, moving object tracking. The particle swarms in some way are closely related to cellular automata ca.
This book presents the most recent and established developments of particle swarm optimization pso within a unified framework by noted researchers in the fieldprovided by publisher. These methods are particle swarm optimization algorithm, neural networks, genetic algorithms, ant colony optimization, artificial immune systems, and fuzzy optimization 6 7. After update qt step in the cycles, the best particle solution among pt is selected, if it is better than the stored best particle solution, then it will be stored. Selfadaptive quantum particle swarm optimization for. Populationbased optimization algorithms are useful tools in solving engineering problems. Oct 31, 2017 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. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than pso. The masterslave synchronous and asynchronous and static subpopulation parallel qpso models are investigated and applied to solve the inverse heat conduction problem of identifying the unknown boundary shape. Considerable research has been done on the chaotic behavior of fractionalorder dynamical systems. History of pso pso has been proposed by eberhart and kennedy in 1995.
Quantum particle swarm optimization for synthesis of non. Parallel quantumbehaved particle swarm optimization. This qpso is a natureinspired metaheuristic algorithm based on particle swarm optimization pso, which is already widely used in engineering and computer science to tackle complicated optimization problems. When the cycles are completed, the stored best particle solution is the expected particle solution with quantum particles swarm optimization. Particle swarm optimization algorithm algorithm outline. Pdf a new quantum behaved particle swarm optimization. Particle swarm optimization research toolbox documentation. Quantum behaved particle swarm optimization qpso for. Pdf this paper presents a variant of quantum behaved particle swarm optimization qpso named qqpso for solving global optimization problems. Application of particle swarm optimization algorithm to. Mar 24, 2006 particle swarm optimization pso was originally designed and introduced by eberhart and kennedy. In a simple term it is a method of optimization for continuous nonlinear function.
The particle swarm optimization algorithm abbreviated as pso is a novel. Quantum behaved particle swarm optimization, diversitycontrolled, local search, global optimization. Quantumbehaved particle swarm optimization qpso, like other populationbased algorithms, is intrinsically parallel. The chaotic behavior of the fractionalorder lorenz system was studied in, in which the authors determined that the system with. The pso is a population based search algorithm based on the simulation of the social behavior of birds, bees or a school of fishes. 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. This paper presents an elitist transposon quantumbased particle swarm algorithm to solve economic dispatch ed problems.
This paper presents an elitist transposon quantum based particle swarm algorithm to solve economic dispatch ed problems. There are several schools of thought as to why and how the pso algorithm can perform optimization a common belief amongst researchers is that the swarm behaviour varies between exploratory behaviour, that is, searching a broader region of the searchspace, and exploitative behaviour, that is, a locally oriented search so as to get closer to a possibly local optimum. A quantum particle swarm optimization algorithm with teamwork. Quantumbehaved particle swarm optimization file exchange. This article describes that during the progressive process, the aging. A quantum particle swarm optimization algorithm based on. The system is initialized with a population of random solutions and searches for optima by updating generations. Jan 17, 2017 particle swarm optimization pso is a populationbased stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish.
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