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.
Exchange rate forecasting involves many challenges in research. Due to the difficulty of selecting superior variables to design a good forecasting mode, few empirical studies have discussed the influence of explainable variables. In this paper, a new forecasting model is constructed; we adopt the particle swarm optimization (PSO) to select the optimal input layer neurons to predict NTD/USD exchange...
Stochastic particle swarm optimization is a novel variant of particle swarm optimization that convergent to the global optimum with probability one. However, the local search capability is not always well in some cases, therefore, in this paper, a technique, dynamic step length, is incorporated into the structure of stochastic particle swarm optimization aiming to further improve the performance....
This paper proposed a hybrid particle swarm optimization algorithm (Shadow hybrid PSO, SHPSO) to solve the flow-shop scheduling problem (FSSP). SHPSO adopts the idea of Kuoa's HPSO model by not only combines the random-key (RK) encoding scheme, individual enhancement (IE) scheme, but also adds the diversification mechanism such as ARPSO model and competitive shadow particles to prevent premature convergence...
Particle Swarm Optimization (PSO) is a new type of heuristic inspired by the flocking behavior of birds. This paper presents a Particle Swarm Optimization (PSO) to solve the permutation flowshop scheduling problem (PFSP) objectives, to minimize the makespan. To this end, we have proposed the use of discrete PSO algorithm for the position of the smallest value (SPV) to use a random key representation...
In this paper, we combined chaotic search into standard particle swarm search into one and proposed a new algorithm named as chaos particle swarm optimization algorithm(CPSO). The CPSO algorithm may speed the search process, and improve the ability of seeking the global optimal solution and convergence. And the CPSO algorithm was applied to analysis of one-dimensional tracing test date of river stream...
This paper in involved in the application of particle swarm optimization in the optimization problem of machining allowance of propeller. The optimization model of machining allowance of propeller is given at first, and the computer implementation on particle swarm optimization is discussed then for the optimization problem of machining allowance of propeller in this paper. The strategy which keeps...
This paper proposes an enhanced particle swarm optimization with gradient information (GPSO) for unconstrained optimization. Newton's method and mutation operation are embedded in the velocity update equation to improve the effect of cognition influence and social influence, respectively. Based on numerous test function taken from the literature, computational results via a variety of experimental...
This paper presents an improved particle swarm optimization algorithm with feasibility- based rules (FRIPSO) to solve mixed-variable constrained optimization problems. Different kinds of variables are dealt in different ways in FRIPSO algorithm. Constraint handling is based on simple feasibility-based rules without the use of a penalty function which is frequently cumbersome to parameterize, nor need...
This paper proposed the design of T-S fuzzy control for magnetic levitation systems. The maglev systems are linearized at the equilibrium point first. Then the error state equations are derived and the proportional integral (PI) controller is applied to eliminate the steady-state tracking error. The nonlinear dynamic equations of the magnetic levitation systems are represented by a T-S fuzzy model...
The virtual enterprises face more risk than traditional enterprises as they are dynamic, temporary and with multi-partners. In order to control the risk to the acceptable level, the multi strategies multi choices (MSMC) risk programming model is proposed in this paper considering the fuzzy characteristics and project organization mode of virtual enterprises. In order to deal with the multi control...
A new QSAR model for the classification of estrogen receptor-?? (ER??) selective ligand has been developed with adaptive boosting (Adaboost) and support vector machine (SVM). Compound structures were drawn in Molinspiration WebME Editor and imported into the E-Dragon 1.0 software to calculate seven categories descriptors. The selection of variables for each descriptor was performed with particle swarm...
Based on analyzing the influential factors and the characters of flatness forecasting, a hybrid optimized algorithm for RBF neural network based on modified particle swarm optimization (MPSO) is introduced in the paper to forecast the flatness. The chaotic optimization algorithm is introduced to decide the parameters of PSO. The number of units in RBF hidden layer is determined by using the rival...
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.