The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately

intelligence for business rules management improvement. The paper introduces the decision making model which is based on the application of Artificial Neural Networks (ANNs) and Particel Swarm Optimization (PSO) algorithm. In the proposed decision making model ANNs are applied in order to make the analysis

Abstract. In this paper, an intelligent controller of PM DC Motor drive is designed using particle swarm optimization (PSO) method for tuning the optimal proportional-integral-derivative (PID) controller parameters. The proposed approach has superior feature, including easy implementation, stable

system network. Approach: This study presents the implementation of Quantum Particle Swarm Optimization (QPSO) in solving the Reactive Power Optimization (RPO) problem. The main aim of this algorithm is the minimization of the real power loss and to improvise the voltage in the system. In this new

Abstract not found

Abstract—To enhance the optimization ability of quantumbehaved particle swarm optimization algorithm, some improvement measures are proposed. First, we propose a encoding approach based on qubits described on Bloch sphere. This approach makes each particle contain three groups of Bloch coordinates

Prototype P2 in operation with a regime R3a ii Synopsis: A Dropshaft is an energy dissipator connecting two channels with different invert elevations. In the present study, the hydraulics of vertical, rectangular dropshafts was systematically investigated in two prototypes and five smaller models. Experimental observations showed three basic flow regimes for dropshaft configurations with 180 degree outflow direction. For dropshaft configurations with 90 degree outflow direction, only two flow regimes were observed. At low flow rates, the rate of energy dissipation in the dropshafts was nearly 95%. In the models, the pool depth had little effects on the hydraulic properties of the dropshaft, but larger rate of energy dissipation was consistently observed with 90º outflow direction. Neutrally-buoyant particles were used to record particle residence times in the shafts. For low flow rates and deep shaft pools, the probability distribution functions of residence times exhibited a bi-modal distribution in both model and prototype. Detailed air-water flow and acoustic measurements were conducted in one prototype dropshaft under controlled flow conditions. Void fraction measurements demonstrated a strong aeration of the shaft pool for

Abstract—This paper introduces a novel forecasting algorithm that is a blend of micro and macro modelling perspectives when using Artificial Intelligence (AI) techniques. The micro component concerns the fine-tuning of technical indicators with population based optimization algorithms. This entails learning a set of parameters that optimize some economically desirable fitness function as to create a dynamic signal processor which adapts to changing market environments. The macro component concerns combining the heterogeneous set of signals produced from a population of optimized technical indicators. The combined signal is derived from a Learning Classifier System (LCS) framework that combines population based optimization and reinforcement learning (RL). This research is motivated by two factors, that of non-stationarity and cyclical profitability (as implied by the adaptive market hypothesis [10]). These two properties are not necessarily in contradiction but they do highlight the need for adaptation and creation of new models, while synchronously being able to consult others which were previously effective. The results demonstrate that the proposed system is effective at combining the signals into a coherent profitable trading system but that the performance of the system is bounded by the quality of the solutions in the population. I.

Abstract not found

Abstract—This study analyzes two implications of the Adaptive