scholarly journals Particle Swarm Optimization Technique for Solving Economic Emission Dispatch Problems

2012 ◽  
Vol 38 ◽  
pp. 2009-2021 ◽  
Author(s):  
H. Vennila ◽  
T.Ruban Deva Prakash
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Economic emission dispatch using an advanced particle swarm optimization technique

2019 ◽  
Vol 16 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Hamid Rezaie ◽  
Mehrdad Abedi ◽  
Saeed Rastegar ◽  
Hassan Rastegar
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Content Type ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Purpose This study aims to present a novel optimization technique to solve the combined economic emission dispatch (CEED) problem considering transmission losses, valve-point loading effects, ramp rate limits and prohibited operating zones. This is one of the most complex optimization problems concerning power systems. Design/methodology/approach The proposed algorithm has been called advanced particle swarm optimization (APSO) and was created by applying several innovative modifications to the classic PSO algorithm. APSO performance was tested on four test systems having 14, 40, 54 and 120 generators. Findings The suggested modifications have improved the accuracy, convergence rate, robustness and effectiveness of the algorithm, which has produced high-quality solutions for the CEED problem. Originality/value The results obtained by APSO were compared with those of several other techniques, and the effectiveness and superiority of the proposed algorithm was demonstrated. Also, because of its superlative characteristics, APSO can be applied to many other engineering optimization problems. Moreover, the suggested modifications can be easily used in other population-based optimization algorithms to improve their performance.

Materialized View Selection Using Swap Operator Based Particle Swarm Optimization

2021 ◽  
Vol 13 (1) ◽  
pp. 58-73
Author(s):  
Amit Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Data Warehouse ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Selection Problem ◽  
Data Repository ◽  
Swarm Optimization ◽  
View Selection ◽  
Particle Swarm Optimization Technique ◽  
Swap Operator

The data warehouse is a key data repository of any business enterprise that stores enormous historical data meant for answering analytical queries. These queries need to be processed efficiently in order to make efficient and timely decisions. One way to achieve this is by materializing views over a data warehouse. An n-dimensional star schema can be mapped into an n-dimensional lattice from which Top-K views can be selected for materialization. Selection of such Top-K views is an NP-Hard problem. Several metaheuristic algorithms have been used to address this view selection problem. In this paper, a swap operator-based particle swarm optimization technique has been adapted to address such a view selection problem.

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