Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than pso. 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. Quantum particle swarm optimization the pso method is a populationbased optimization strategy introduced by james kennedy and russell c. Adaptive mutation behavior for quantum particle swarm optimization. Institute of information and system science, beifang university of nationalities, yinchuan, china. This later was the basic idea behind quantum behaved particle swarm optimization qpso algorithm, a quantum variant of pso algorithm. Novel qpso algorithm for electromagnetic applications. Particle swarm optimization in electromagnetics ieee. This paper introduces a conceptual overview and detailed explanation of the pso algorithm, as well as how it can be used for electromagnetic optimizations. 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. An analysis of publications on particle swarm optimisation. Particle swarm optimization pso is an optimization method in which multiple candidate solutions particles migrate through the solution space under the influence of. A collision detection algorithm based on improved quantum. A dynamically quantum particle swarm optimization algorithm with adaptive mutation.
Optimization of advanced electromagnetic devices and. Particle swarm optimization as described by the inventers james kennedy and russell eberhart, particle swarm algorithm imitates human or insects social behavior. By postulating that the swarm motion behaves similar to both classical and quantum particles, we establish a direct connection between what are usually assumed to be separate fields of study, optimization and physics. Modeling of composite metallic and dielectric structures, software and users. Individuals interact with one another while learning from their own experience, and gradually the population members move into better regions of. Qpso is stated only by the position vector and there is no velocity vector. But it may trap into local optima due to premature convergence because. You can build artificial intelligence models using neural networks to help you discover relationships, recognize patterns and make predictions in just a few clicks. Quantum particle swarm optimization for electromagnetics. Quantum particle swarm optimization algorithm scientific. Quantum behaved particle swarm optimization qpso for. Pdf a new particle swarm optimization pso technique for electromagnetic applications is proposed. Journal of microwaves, optoelectronics and electromagnetic applications, vol. 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.
Chenyang gao1, ning chen1, yuelin gao2, jiajiang zhang2. Quantum particle swarm optimization for multiobjective. Adaptive mutation behavior for quantum particle swarm. 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. 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 quantum particle swarm optimization algorithm based on.
Image enhancement techniques are very important to image processing, which are used to improve image quality or extract the fine details in degraded images. The path planning of unmanned aerial vehicle is a key problem for its autonomous flight. We propose an interdisciplinary approach to particle swarm optimization pso by establishing a molecular dynamics md formulation of the algorithm, leading to a physical theory for the swarm environment. An improved quantumbehaved particle swarm optimization. A quantuminspired version of the pso algorithm qpso was proposed very. Mutation operator was proposed in the qpso to help increase the particles diversity and global search capability. An image enhancement method using the quantumbehaved. Abstract the particle swarm optimization pso, new to the electromagnetics community, is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms. 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 movable dealing of the particle in qpso is different from that of the particle in standard pso. The particle swarm optimization algorithm pso proposed by james kennedy. Particle swarm optimisation can be and has been used across a wide range of applications. Quantum particle swarm optimization for electromagnetics said mikki and ahmed a. Theory, techniques and applications engineering tools, techniques and tables. Quantum particle swarm optimization for electromagnetics core. Quantum particle swarm optimization for synthesis of non. Abstractthis work aims to provide new introduction to the particle swarm optimization methods using a formal analogy with physical systems. Journal of microwaves, optoelectronics and electromagnetic applications. L172049, and in part by the scientific research common program of beijing municipal. 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.
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. This paper explores a type of particle swarm optimization pso methods 3, 4 that are optimization methods for large and nondifferential searching space. A quantum particle swarm optimization algorithm based on selfupdating mechanism. By postulating that the swarm motion behaves similar to both classical and. Unlike genetic algorithms gas 6, 7,whichrelyondarwinstheoryofnat. The system is initialized with a population of random solutions and searches for optima by updating generations. Particle swarm optimization pso is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling. Though the use of pso in training of ann finds optimal weights of the network it fails in the design of appropriate topology. Pdf design optimization of electromagnetic devices using. Applying particle swarm optimization to software testing. Particle swarm optimization pso is a kind of stochastic optimization algorithms proposed by kennedy and eberhart 1 that can be easily implemented and.
Quantum inspired particle swarm optimization qpso is widely used global convergence algorithm for complex design problems. Particle swarm optimization is a draft programming task. For the path planning of quadrotor, using the quantum particle swarm optimization algorithm, and made a lot of simulation and. A quantum particle swarm optimizer with chaotic mutation. Kishk, quantum particle swarm optimization for electromagnetics, ieee trans. A chaotic quantumbehaved particle swarm optimization. By the use of the global searching ability of the quantum particle swarm optimization qpso, qpsotwsvm can search the optimal parameters in the global scope and avoid itself falling into the local optimum. Quantum particle swarm optimization tuned artificial. But, qpso is capable of optimizing the network topology. A physicsbased approach synthesis lectures on computational electromagnetics ahmed kishk on. Twin support vector machines based on quantum particle.
Pdf quantum particle swarm optimization for electromagnetics. Quantum particle swarm optimization algorithm qpso is a good optimization. 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. In this respect it is similar to the genetic algorithm. Theory, techniques and applications engineering tools, techniques and tables olsson, andrea e. Eberhart ken 95 and has shown its robustness and efficacy in solving many optimization problems. Particle swarm optimization for antenna designs in. Technology support program of sichuan province grant number 2016rz0065. Quantumbehaved particle swarm optimization, diversitycontrolled, local search, global optimization. A collection of individuals called particles move in steps throughout a region. A quantum particle swarm optimization shuyuan yang, min wang dept. Digitally excited reconfigurable linear antenna array using swarm. In this paper, two novel objective functions based on the normalized incomplete beta transform function are proposed to evaluate the effectiveness of grayscale image enhancement and color image enhancement, respectively. The particles move in the space, according the best values of the particle itself, its neighborhood, and the entire swarm.
Path planning of quadrotor based on quantum particle swarm. The particle swarm optimization pso, new to the electromagnetics community, is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms. Object tracking based on quantum particle swarm optimization. A quantum inspired version of the pso algorithm qpso was proposed very.
Similarly to genetic algorithms 6, an evolutionary algorithm approach, pso is an optimization tool based on a population, where each member is seen as a particle, and each particle is a potential solution to the. In a pso algorithm, a swarm of particles cooperate to find an optimal solution to the problem. Nowadays, it becomes a hot research topic for autonomous flight of quadrotor in the complex environment and the realization of fully autonomous flight is still a big challenge. In a simple term it is a method of optimization for continuous nonlinear function.
This article uses artificial neural network ann trained with quantum behaved particle swarm optimization qpso for the problem of equalization. Pier online physical theory for particle swarm optimization. Particle swarm and quantum particle swarm optimization. A novel optimization algorithm qpso, which is rooted on the fundamental theory of particle swarm and properties of quantum mechanics, was proposed in 11. This work aims to provide new introduction to the particle swarm optimization methods using a formal analogy with physical systems. The particle swarm optimization pso originally developed by kennedy and eberhart in 1995, is a populationbased swarm algorithm. A new particle swarm optimization pso technique for electromagnetic applications is proposed. Particle swarm and quantum particle swarm optimization applied to dscdma multiuser detection in flat rayleigh channels leonardo d. 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.
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. Neural designer is a machine learning software with better usability and higher performance. An improved quantumbehaved particle swarm optimization with elitist breeding ebqpso for unconstrained optimization is presented and empirically studied in this paper. Quantumbehaved particle swarm optimization with gaussian distributed local attractor point jun suna. Communications in computer and information science, vol 472. According to the characteristics of quantum entanglement and probability amplitude, a quantum bit can 1be represented not jus 0t. Particle swarm optimization james kennedy russell eberhart the inventors.
This article describes that during the progressive process, the aging. Pso shares many similarities with evolutionary computation techniques such as genetic algorithms ga. This paper describes a method using quantum particle swarm optimization to obtain a broadband frequency invariant. It is not yet considered ready to be promoted as a complete task, for reasons that should be found in its talk page. Quantumbehaved particle swarm optimization with gaussian.
A novel quantum inspired particle swarm optimization algorithm for. A novel quantumbehaved particle swarm optimization qpso algorithm, the. Towards software test data generation using discrete quantum particle swarm optimization, proceedings of the 3rd india software engineering conference, february 2527, 2010, mysore, india. At each step, the algorithm evaluates the objective function at each particle. On extending quantum behaved particle swarm optimization. Lncs 6145 improved quantum particle swarm optimization. The particle swarms in some way are closely related to cellular automata ca. Eighth acis international conference on software engineering, artificial.
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