STOCHASTIC WEIGHT TRADE OFF PARTICLE SWARM OPTIMIZATION FOR OPTIMAL POWER FLOW

Chapter 8
Introduction to Evolutionary Computation
Evolution is an optimization process where the aim is to improve the ability of an or- ganism (or system) to survive in dynamically changing and competitive environments. Evolution is a concept that has been hotly debated over centuries[r]

In PID controller design, an optimization algorithm is commonly employed to search for the optimal controller parameters. The optimization algorithm is based on a specific performance criterion which is defined by an objective or cost function. To this end, different objective functions have been pr[r]

6. Conclusion
In this paper we have studied the stochastic linear quadratic (LQ) optimal tracking control theory considering the preview information by state feedback and output feedback for the linear discrete-time Markovian jump systems affected by the white noises, which[r]

117 TRANG 7 DANH MỤC CHỮ VIẾT TẮT KÝ HIỆU MÔ TẢ TIẾNG ANH MÔ TẢ TIẾNG VIỆT AF Array factor Hệ số mảng APSO Adaptive particle swarm optimization Tối ưu bầy đàn thích nghi DEA Differential[r]

69 TRANG 12 LIST OF ABBREVIATIONS BP error Back-Propagation CCU Central Control Unit FAMAC Fully Automatic Multi-Agents Cooperation FL Fuzzy Logic GA Genetic Algorithm IAU Intelligent An[r]

Giải thuật tối ưu hĩa bầy đàn – PSO lần đầu tiên được giới thiệu vào năm 1995 bởi Kennedy, J. và Eberhart, R [ 15 ]. Trong một hội thảo quốc tế của IEEE về mạng neural tại Perth, Australia.
PSO là được khởi tạo bởi một nhĩm ngẫu nhiên các particles, sau đĩ tìm kiếm giải pháp tối ưu bằng việ[r]

TRANG 10 Figure O: Particle Size and Mechanical Separating Power [Same Column Length] For a given particle chemistry, mobile phase, and flow rate, as shown in Figure O, a column of the s[r]

The pseudo-random binary sequence (PRBS) is con- verted into parallel data by S/P converter, and then the NBPSO based on DSI method is processed for PAPR
reduction. The algorithm of NBPSO includes a loop: a dummy sequence is added to the end of the each parallel data, and then they are mapp[r]

This study investigates multiresponse optimization of turning process for an optimal parametric combination to yield the minimum power consumption, surface roughness and frequency of tool vibration using a combination of a Grey relational analysis (GRA).

To solve MOBPP-2D problems, a multiobjective evolutionary particle swarm optimization algorithm MOEPSO is proposed.. Without the need of combining both objectives into a composite scalar[r]

8. Conclusion
In this paper, a generalized PSO model for adaptive resource allocation in communication networks has been proposed to reduce computational complexity in centralized and dis- tributed resource allocation scenarios. The proposed model consists of PSO with VPs for central[r]

(Chen and Chen, 2005) study a two-echelon supply chain, in which a retailer maintains a stock of different products in order to meet deterministic demand and replenishes the stock by placing orders at a manufacturer who has a single production facility. The retailer’s problem is to decide when an[r]

The model is formulated as a multi-objective mixed-integer nonlinear programming in order to minimize the expected total cost of such a supply chain network comprising location, procurement, transportation, holding, ordering, and shortage costs. Moreover, we develop an effective solution approach on[r]

An optimization-simulation strategy has been applied by coupling a commercial process simulator (Aspen HYSYS ) with a programming tool (MATLAB ) to produce a precise steady state simulationbased optimization of a whole green-field saturated gas plant as a real case study. The plant has more than 100[r]

Oxley Act (SOX). While SOX contains many provisions, the overall intent of the legisla-
tion was to improve the accuracy of information given to both boards and shareholders. SOX attempted to achieve this goal in three ways: (1) by overhauling incentives and the independence in the auditing proce[r]

In order to find the solution for all fitness functions at the same time, we perform simultaneous multithreading of the PSO algorithm by defining PSO it as 1 class extends Thread class o[r]

Note
PSAT is a Matlab toolbox for static and dynamic analysis and control of elec- tric power systems. The PSAT project began in September 2001, while I was a Ph.D. candidate at the Universit´ a degli Studi di Genova, Italy. The first public version date back to November 2002, when[r]

In this book, we review the application of genetic algorithms, particle swarm optimization and ant colony optimization, as three different para- digms that help in the design of optimal [r]

using tracking features of PSO. In [13], continuous and discrete PSO has been used for joint channel and data esti- mation based on maximum likelihood principle. In [14], to decrease the effect of noise, angle domain PSO-LS algo- rithm which exploits most significant taps technique using a[r]

• Else go to step 19.
Step 19: Last G best _ position of particles is optimal solution.
6. Results and discussion
The NCHES generation scheduling has been done by Time Varying Acceleration Coefficients PSO (TVAC_PSO) on hourly basis, assuming all reservoirs full at starting of the[r]