Multiobjective Control of Power Plants Using Particle Swarm Optimization Techniques

2006 ◽  
Vol 21 (2) ◽  
pp. 552-561 ◽  
Author(s):  
J.S. Heo ◽  
K.Y. Lee ◽  
R. Garduno-Ramirez
Keyword(s):  
Particle Swarm Optimization ◽  
Power Plants ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Multiobjective Control

scholarly journals Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1795
Author(s):  
Manuel Cedillo-Hernandez ◽  
Antonio Cedillo-Hernandez ◽  
Francisco J. Garcia-Ugalde
Keyword(s):  
Fourier Transform ◽  
Particle Swarm Optimization ◽  
Discrete Fourier Transform ◽  
Image Watermarking ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Robust Watermarking ◽  
Swarm Optimization ◽  
General Terms ◽  
Operation Parameters

Robust digital image watermarking is an information security technique that has been widely used to solve several issues related mainly with copyright protection as well as ownership authentication. In general terms, robust watermarking conceals a small signal called a “watermark” in a host image in a form imperceptible to human vision. The efficiency of conventional robust watermarking based on frequency domain depend directly on the results of performance in terms of robustness and imperceptibility. According to the application scenario and the image dataset, it is common practice to adjust the key parameters used by robust watermarking methods in an experimental form; however, this manual adjustment may involve exhaustive tasks and at the same time be a drawback in practical scenarios. In recent years, several optimization techniques have been adopted by robust watermarking to allowing adjusting in an automatic form its key operation parameters, improving thus its performance. In this context, this paper proposes an improved robust watermarking algorithm in discrete Fourier transform via spread spectrum, optimizing the key operation parameters, particularly the amounts of bands and coefficients of frequency as well as the watermark strength factor using particle swarm optimization in conjunction with visual information fidelity and bit correct rate criteria. Experimental results obtained in this research show improved robustness against common signal processing and geometric distortions, preserving a high visual quality in color images. Performance comparison with conventional discrete Fourier transform proposal is provided, as well as with the current state-of-the-art of particle swarm optimization applied to image watermarking.

scholarly journals Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ya-zhong Luo ◽  
Li-ni Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Mission Design ◽  
Trajectory Design ◽  
Time Transfer ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Asteroid Mission

A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

scholarly journals Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

2021 ◽  
Vol 21 (1) ◽  
pp. 62-72
Author(s):  
R. B. Madhumala ◽  
Harshvardhan Tiwari ◽  
Verma C. Devaraj
Keyword(s):  
Particle Swarm Optimization ◽  
Virtual Machine ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Virtual Machine Placement ◽  
Placement Problem ◽  
Swarm Optimization ◽  
Cloud Datacenter

Abstract Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.

Multiobjective particle swarm optimization of geothermal power plants

2021 ◽  
pp. 249-262
Author(s):  
Khurram Kamal ◽  
Tahir Abdul Hussain Ratlamwala ◽  
Muhammad Afzal Sheikh ◽  
Sharjeel Ashraf Ansari ◽  
Muhammad Nouman Saleem
Keyword(s):  
Particle Swarm Optimization ◽  
Power Plants ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Geothermal Power

From Optimization to Clustering

2015 ◽  
pp. 594-619 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

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