The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
To deal with the problems of topological structure cannot adjust adaptively, easy to trap into the local minimum and diversity losing in traditional particle swarm optimization algorithm, a newly adaptive PSO algorithm based on dynamic link matrix was proposed, which build the neighborhoods though link matrix and divide them into the sub-swarm based on feature clustering. The algorithm can adjust...
In this paper, we study a transmit null-steering beamforming problem for wireless sensor networks in uncertain communication environment. Given a desired receiver direction, the system requires to avoid the unknown eavesdroppers as well. In this case, we adjust both the sensors distribution and phase offsets such that beamforming is achieved at the desired direction, and meanwhile the communication...
This paper presents the routing recovery problem of mobile sink wireless sensor networks (mWSNs), which is caused by the sink mobility. We propose an immune orthogonal learning particle swarm optimization algorithm (IOLPSOA) based routing recovery method to build and optimize the alternative path, in order to repair the broken path and maintain the available route from the source nodes to the mobile...
In this paper, a novel improved multiobjective particle swarm optimization (IMOPSO) is proposed for solving the optimal reactive power dispatch (ORPD) problem with multiple and competing objectives. In order to improve the global search capability and the nondominated solutions diversity, time variant parameters, mutation operator, and dynamic crowding distance are incorporated into the MOPSO algorithm...
As one of the typical examples of Swarm Intelligence Algorithm, POS has been applied successfully in many fields. But it cannot guarantee a globally optimal solution. To improve that, researchers use related theories in quantum mechanics combined with the basic convergence of particle swarm and put forward a new model of PSO as Quantum-behaved PSO. The new algorithm is convenient for appliance and...
This paper aims at the characteristics of reactive power optimization of the electric power system with the wind farm; proposing a new method for reactive power optimization on the entire power grid which uses the Parallel Immune Particle Swarm Optimization. It uses integer and real number hybrid encoding, improves the efficiency of compiling. And it combines the continuous and discrete particle swarm...
One of the most important features of the PSO algorithm is its fast convergence. This is a positive feature as long as there's no premature convergence. Inspired by the phenomenon of quorum sensing behavior in the bacteria, we incorporate this bio-behavior into PSO and MOPSO to maintain the swarm diversity and promote global exploration when the velocity of each particle in the swarm is rather small...
With the development of intelligent algorithm, GA and PSO have become the hot spot for the study on multi-objective optimization in recently years. Information sharing is the core of PSO algorithm, Comparing with GA, PSO algorithm has less variables to adjust and is easy to achieve, so it is widely used in engineering. This paper focus on the comparation on several PSO algorithm and introduce a kind...
In order to improve convergence speed and precision of optimization in quantum particle swarm optimization (QPSO), an improved quantum particle swarm optimization (IQPSO) algorithm was presented. Chaotic sequences were used to initialize the origin angle position of particle, mutation operation algorithm was used to increase diversity of population and avoid premature convergence. The proposed algorithm...
By introducing the fractional-order difference into the updating formulas of the velocity and position, fractional-order particle swarm optimization algorithm is proposed. The effects on the convergence rate and accuracy are analyzed, by introducing fractional-orders in the updating formulas for the velocity and position. Moreover, the linear increasing methods to adjust the fractional-orders are...
In order to overcome the disadvantages of premature and local convergence in the traditional particle swarm optimization (PSO), an improved chaotic PSO algorithm based on adaptive inertia weight (AIWCPSO) is proposed. The initial population is generated by using chaotic mapping appropriately, in order to improve both the diversity of population and the periodicity of particles. The value of the new...
In this paper, a fast-sorting method called summation of normalized objectives and diversified selection (SNOV-DS) is embedded in Comprehensive Learning Particle Swarm Optimization (CLPSO) to solve multi-objective problems. Due to this method, the simulation time will be decreased. The convergence to true Pareto front and the spread of solutions can also be improved. The algorithm is tested on a set...
In the cold rolling production process in iron and steel industry, the operation optimization problem (OOP) is to determine the optimal setting for the control variables so as to maximize the product quality or minimize the total operation cost. Traditional OOP generally assumes that the production process is stable, however, the practical production process is dynamic and the dimensions of input...
In this paper, the cloud services optimization problem considering energy consumption cost is discussed. The queue model is presented for customer request service on data center. Since the server energy utility is based on the CPU core frequency, the novel trade-off optimization model between services revenue and energy loss cost is proposed in the paper, including allocating the dynamic CPU frequency...
With the diversification and personality of customer demand the modern chemical Polyester production has shifted to multi-product small batch production. The problem of the scheduling on multi-product small batch production is a new unresolved issue which is that people have been concerned about. This paper firstly analyzes the nature of the polyester engineering process and the optimal characteristics...
In wireless sensor networks, the energy supply is limited and the node will be dead while the energy is out of use. In order to solve the energy consumption problem about the sizes of clusters, a novel grid clustering algorithm based on location information is proposed in the paper: the node is planned to the corresponding grid according to the location information.While we can get the sizes of clusters...
In this paper, The improved Particle Swarm Optimization in dynamic objective function environment (DOFPSO) is purposed. The dynamic environment will change with the time t. The DOFPSO algorithm discuss that how to determine changes of the time (environment) and how to keep population diversity. The improved algorithm has the ability to fast response the change of environment and could find the best...
This paper proposed a method of path planning in three-dimensional space based on concentric spherical coordinate and improved particle swarm optimization algorithm. First the model of three-dimensional path planning of UAV was analyzed. Then the encoding method based on concentric spherical coordinate system was given. The constraints were combined with the improved particle swarm optimization algorithm...
Accurate and reliable prediction of melt mass flow rate is crucial in polypropylene production. In order to establish an accurate prediction model, a process state detection method and a novel dynamic modeling method is proposed, and the model parameters are indentified by improved swarm optimization algorithm. A polypropylene product melt mass flow rate soft sensor model is established based on process...
Particle swarm optimization is introduced to solve the problem in this paper. Instead of solving a group of non-linear equations, forward kinematics is solved by computing the extremum of a function. And accurate solutions can be obtained by the global and local searching abilities of advanced particle swarm optimization. It overcomes the shortage that precision is greatly influenced by initial values...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.