In some optimization tasks, called constrained optimization tasks, the elements in a candidate solution can be subject to certain constraints. P article swarm optimization p opulationbased stochastic optimization technique pur pose. Pdf improved quantumbehaved particle swarm optimization. Particle swarm optimization algorithm is analyzed base on quantumbehaved self learning, particles searching action and local attract point are studied. Koichiro tazuke, noriyuki muramoto, nobuyuki matsui, and teijiro isokawa. The qpso algorithm allows all particles to move under quantummechanical rules rather than the classical newtonian random motion. Particle swarm optimization with particles having quantum behavior. Pdf a new quantum behaved particle swarm optimization. Particle swarm optimization james kennedy russell eberhart the inventors. Pdf in computer vision domain, moving object tracking.
Particle swarm optimization for multimachine power system stabilizer design. Particle swarm optimization research base on quantum self. Mathematical modelling and applications of particle swarm. Analysis of particle swarm optimization and genetic. Improved quantum particle swarm optimization by bloch sphere. Quantum inspired particle swarm optimization qpso stimulated by perceptions from particle swarm optimization and quantum mechanics is a stochastic optimization method. In the classical environment, all bees are flying toward the optimum location defined by pm in 6. A novel particle swarm optimization for constrained. In 1995, kennedy and eberhart 33 introduced particle swarm optimization pso, which is a stochastic populationbased search method inspired by social behavior of animals such as birds and sh. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than pso. The final goal of an optimization task is to find the parameters in the search space that maximize or minimize this fitness 2. Quantumbehaved particle swarm optimization, diversitycontrolled, local search, global optimization. A particle swarm optimization demo written by students in the digital communication msc program 2010 at unikiel. Keywordsparticle swarm optimization, mutation operator, travelling salesman problem.
A quantum particle swarm optimization ieee conference publication. 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 mathematical formulae. Quantum particle swarm optimization for electromagnetics. Quantum particle swarm optimization for multiobjective. Robust particle swarm toolbox implementing trelea, common, and clerc types along with an alpha version of change detection. Each particle represents a candidate solution to the problem.
Proceedings of the workshop on particle swarm optimization. A superorganism emerges from the interaction of individuals. 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. Improved particle swarm optimization for traveling. International journal of information technology, modeling and computing ijitmc vol. Quantum particle swarm optimization qpso is a global convergence guaranteed search method which introduces the quantum theory into the basic particle. This is the first book devoted entirely to particle swarm optimization pso, which is a nonspecific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies. P article swarm optimization university of washington.
A quantum particle swarm optimization algorithm with. Improved quantumbehaved particle swarm optimization. Convergence analysis for particle swarm optimization opus 4. Premature convergence, the major problem that confronts evolutionary algorithms, is also encountered with the particle swarm optimization pso algorithm. Toosi university of technology, geodesy and geomatics eng. An improved quantumbehaved particle swarm optimization. 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. The particle swarm optimization algorithm pso is a populationbased optimization method that was rst proposed by kennedy and eberhart 10. A quantum particle swarm optimization driven urban traffic. Particle swarm and quantum particle swarm optimization applied to dscdma multiuser detection in flat rayleigh channels leonardo d. Individuals interact with one another while learning from their own experience, and gradually the population members move into better regions of.
The pso technique nds the optimal solution using a population of particles. 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. Particle swarm optimization research toolbox documentation. Particle swarm optimization particle swarm optimization pso is a swarmintelligencebased approximate nondeterministic optimization technique. Particle swarm optimization research base on quantum self learning behavior. Pdf a novel quantum inspired particle swarm optimization. The particle swarm optimization algorithm is a new methodology in evolutionary computation. The particles are then attracted to this location through the optimization process. Quantumbehaved particle swarm optimization algorithm with. But for now, it may not always satisfy the situations. 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.
The quantum particle swarm optimization algorithm is a global convergence guarantee algorithm. Mathematical modelling and applications of particle swarm optimization by. Pdf a new quantum behaved particle swarm optimization ajith abraham academia. 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 without dwelling on the details of practical im plementation. Based upon the acceptable assumption that any feasible solution is better than any infeasible solution, a new mechanism for constraints handling is incorporated in the standard particle swarm optimization. Object tracking based on quantum particle swarm optimization. A tutorial james blondin september 4, 2009 1 introduction particle swarm optimization pso is a technique used to explore the search space of a given problem to. Particle swarm optimization ieee swarm intelligence symposium 2005 pasadena, california usa, june 8, 2005 t12na 28102011 j. Quantum particle swarm optimization algorithm scientific. Pdf to enhance the performance of quantumbehaved pso, some improvements are proposed. Download particle swarm optimization dc2010 for free. Quantum particle swarm optimization for electromagnetics said mikki and ahmed a. It is motivated by the behavior of flying bird and their communication mechanism.
Particle swarm optimization with velocity adaptation. Quantumbehaved particle swarm optimization with gaussian distributed local attractor point jun suna. Pdf particle swarm optimization method for constrained. Metaoptimization is reported to have been used as early as in the late 1970s by mercer and sampson for finding optimal parameter settings of a genetic algorithm. Recently, a new variant of pso, called quantumbehaved particle swarm optimi. Particle swarm optimization pso is a populationbased stochastic optimization algorithm motivated by intelligent collective behavior of some animals such. Quantum particle swarm optimization to solve economic load. Particle swarm optimization pso is a very popular, randomized, nature inspired. Particle swarm optimization pso is a kind of stochastic optimization algorithms proposed by kennedy and eberhart 1 that can be easily implemented and. Pdf object tracking based on quantum particle swarm optimization.
Particle swarm optimization of artificial neural networks. The program finds the values of current excitation that will minimize sidelobe level and achieve a radiation pattern that matches closely with the desired pattern. The particle swarm optimization pso algorithm is a global search strategy that can efficiently handle arbitrary optimization problems. The evolutionary algorithm approach is not the only approach for stochastic optimization of anns. Table 2 is in same format like table 1 but it is for those test files which works.
It has been found to be extremely effective is solving a wide r. An improvement particle swarm optimization for travelling. In numerical optimization, metaoptimization is the use of one optimization method to tune another optimization method. Guide to conducting your own research clarifies how a motivated researcher could add constraints or make other improvements.
Quantumbehaved particle swarm optimization with gaussian. Xinli xu, xu cheng, zhongchen yang, xuhua yang and wanliang. A quantum particle swarm optimization algorithm with teamwork evolutionary strategy article pdf available in mathematical problems in engineering 20198. Particle swarm and quantum particle swarm optimization. The most successful swarm intelligence techniques are particle swarm optimization pso and ant colony optimization aco. First, an encoding method based on the bloch sphere is. Quantum behaved particle swarm optimization qpso for. Communication in particle swarm optimization illustrated by the traveling salesman problem. Abstractquantuminspired particle swarm optimization qpso is an extension of particle swarm optimization pso methods, in which the concept of quantum mechanics is adopted.
In 1995, kennedy and eberhart introduced the pso method for the first time. Particle swarm optimisation pso swarm intelligence collective intelligence. A quantum particle swarm optimization shuyuan yang, min wang dept. Purdue school of engineering and technology, iupui in press. The particle swarm optimization research toolbox is currently designed to handle continuous, singleobjective optimization problems. 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. Parameter selection in particle swarm optimization. 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. In pso, each particle flies through the multidimensional. Quantumbehaved particle swarm optimization qpso is an improved version of particle swarm optimization pso and has shown superior performance on many optimization problems. Particle swarm optimization method for constrained optimization problem chapter pdf available in frontiers in artificial intelligence and applications 76. This paper proposes a novel particle swarm optimization pso for solving constrained optimization problems.
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. The qqpso algorithm is based on the characteristics of qpso, and uses interpolation based. The algorithm is inspired by the interactions observed in social groups such as bird. 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. Pdf a quantum particle swarm optimization algorithm with.
A rudimentary pso variant, with just one implicit fixed topology, which is far to be the best one for multimodal problems. The particle swarm optimization program forms a part of a 24 array antenna model, and the whole idea is. Enhancing the radiation pattern of phase array antenna. An improved quantumbehaved particle swarm optimization with elitist breeding ebqpso for unconstrained optimization is presented and empirically studied in this paper. 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. Since its original development in 1995, pso has mainly been applied to continuousdiscrete heterogeneous strongly nonlinear numerical optimization and it is thus used almost everywhere in the world. The particle swarms in some way are closely related to cellular automata ca. This toolbox is designed for researchers in computational intelligence as well as application developers, students, and classroom labs. In this paper, an enhanced bml model ebml is proposed to effectively simulate the urban traffic where the timing scheduling optimization algorithm tso based on the quantum particle swarm optimization is creatively introduced to optimize the timing scheduling of traffic light. Particle swarm optimization 8, 9 is a populationbased stochastic search algorithm which has become very popular for solving continuous optimization problems in recent years. Quantum pso qpso as introduced in sfx is another variant which. It is introduced by kennedy and eberhart 1, 2 in 1995. In this paper, in order to balance the global and local searching abilities, we introduce a weight parameter in calculating the mean best position in qpso to render the importance of particles in population when they are evolving, and thus proposed an improved quantumbehaved particle swarm optimization algorithm, weighted qpso wqpso. It is known that pso is also successful for optimization.
In this paper, inspired by the analysis of convergence of pso, we study the individual particle of a pso system moving in a quantum multidimensional space. Particle swarm optimization as described by the inventers james kennedy and russell eberhart, particle swarm algorithm imitates human or insects social behavior. This is the particle with the lowest fitness value, closest to the roost. In this code, quantum computing qc inspired particle swarm optimization qpso technique is utilized to solve economic dispatch ed problem, which has strong, robust and reliable search capability with powerful convergence properties. Particle swarm optimiser with neighbourhood operator.
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