scholarly journals A Crazy Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch

2019 ◽  
Vol 9 (2) ◽  
pp. 1205-1213
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Economic Load Dispatch ◽  
Time Varying ◽  
Swarm Optimization ◽  
Real Coded Genetic Algorithm ◽  
Conventional Optimization

In power generating plants, the expenses on combustible fuel is extremely costly and the concept of ELD (Economic Load Dispatch) make possible to save the considerable portion of profits. Practically generators have economic dispatch problems in terms of non-convexity. These kinds of problem cannot be resolved by conventional optimization techniques because the complication escalates due to manifold constrained that require to be fulfilled in all operating conditions. Recently a Particle Swarm Optimization (PSO) algorithm stimulated by collective conduct of swarm can be applied effectively to translate the ELD problems. The classical PSO bears the difficulty of early convergence mainly when the space of search is asymmetrical. To overcome the trouble “Crazy PSO with TVAC (Time Varying Acceleration Coefficients)” is launched which improve the search ability of the PSO by rebooting the vector of velocity whenever diffusion or saturation locate inside and to employ a scheme of parameter automation to maintain correct equilibrium between global hunt and local hunt and also circumvent the congestion. This arrangement is developed crazy PSO with TVAC and also demonstrated on two different model experimental structures of three generation units and six generation units. The result acquired from proposed method is evaluate with classical PSO and Real coded genetic algorithm (RGA) and it is found to be superior. This method is mathematically simple, gives fast convergence and robustness to resolve the rigid optimization inconvenience.

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.

Application of the particle swarm optimization method for the analysis of wide-angle X-ray diffraction curves of semicrystalline polymers

2017 ◽  
Vol 50 (1) ◽  
pp. 221-230 ◽  
Author(s):  
Małgorzata Rabiej
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Semicrystalline Polymers ◽  
Optimization Techniques ◽  
Wide Angle ◽  
X Ray Diffraction ◽  
Swarm Optimization ◽  
X Ray

The analysis of wide-angle X-ray diffraction curves of semicrystalline polymers is connected with a thorough decomposition of these curves into crystalline peaks and amorphous components. A reliable and unambiguous decomposition is the most important step in calculation of the crystallinity of polymers. This work presents a new algorithm dedicated to this aim, which is based on the particle swarm optimization (PSO) method. The PSO method is one of the most effective optimization techniques that employs a random choice as a tool for going through the solution space and searching for the global solution. The action of the PSO algorithm imitates the behaviour of a bird flock or a fish school. In the system elaborated in this work the original PSO algorithm has been equipped with several heuristics. The role of heuristics is performed by procedures which orient the search of the solution space using additional information. In this paper it is shown that this algorithm is faster to converge and more efficiently performs a multi-criterial optimization compared with other algorithms used for this purpose to date.

scholarly journals Efficient Energy Load Distribution Model using Modified Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2 (4) ◽  
pp. 226-231
Author(s):  
Vijayakumar T ◽  
Vinothkanna R
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Electric Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Economic Load Dispatch ◽  
Electric Power Generation ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Modified Pso

Reduction of emission and energy conservation plays a major role in the current power system for realizing sustainable socio-economic development. The application prospects and practical significance of economic load dispatch issue in the electric power market is remarkable. The various generating sets must be assigned with load capacity in a reasonable manner for reducing the cost of electric power generation. This problem may be overcome by the proposed modified particle swarm optimization (PSO) algorithm. The practical issue is converted and modelled into its corresponding mathematical counterpart by establishing certain constraints. Further, a novel interdependence strategy along with a modified PSO algorithm is implemented for balancing the local search capability and global optimization. Multiple swarms are introduced in the modified PSO algorithm. Certain standard test functions are executed for specific analysis. Finally, the proposed modified PSO algorithm can optimize the economic load dispatch problem while saving the energy resources to a larger extent. The algorithm evaluation can be performed using real-time examples for verifying the efficiency. When compared to existing schemes like artificial bee colony (ABC), genetic algorithms (GAs), and conventional PSO algorithms, the proposed scheme offers lowest electric power generation cost and overcomes the load dispatch issue according to the simulation results.

Parametric Fin Array Optimization for Radial Flux Machines Considering Time Varying Loads

2011 ◽  
Author(s):  
S. Andrew Semidey ◽  
J. Rhett Mayor
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Time Varying ◽  
Swarm Optimization ◽  
Radial Flux ◽  
Steady State Temperature ◽  
Array Optimization ◽  
Fin Spacing

This work utilizes a novel, generic, thermal model of an electric machine in conjunction with particle swarm optimization to optimize the electric machine’s fin array considering time varying loads. The maximum power rating on radial-flux electrical machines is typically based on the steady state temperature of the windings. This leads to over designs in applications in which only short periods of high power are required. The proposed optimization technique can be used in the design process to reduce the risk of over design therefore leading to reduced material costs for finned frames and increased power density in radial flux machines. Whilst many numerical optimization techniques exist, this paper will consider the application of particle swarm optimization techniques to optimize the fin array parameters. The parameter space to be investigated will consider the fin height (hf), fin width (wf), and fin spacing (sf).

Particle Swarm vs. Evolutionary Optimization Techniques in a Multiobjective Framework for Damage Identification

2009 ◽  
Vol 413-414 ◽  
pp. 661-668
Author(s):  
Ricardo Perera ◽  
Sheng En Fang ◽  
Antonio Ruiz
Keyword(s):  
Particle Swarm Optimization ◽  
Damage Identification ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Identification Problems ◽  
Swarm Optimization ◽  
The Family ◽  
Long Time ◽  
Evolutionary Optimization Techniques

In the context of real-world damage detection problems, the lack of a clear objective function advises to perform simultaneous optimizations of several objectives with the purpose of improving the performance of the procedure. Evolutionary algorithms have been considered to be particularly appropriate to these kinds of problems. However, evolutionary techniques require a relatively long time to obtain a Pareto front of high quality. Particle swarm optimization (PSO) is one of the newest techniques within the family of optimization algorithms. The PSO algorithm relies only on two simple PSO self-updating equations whose purpose is to try to emulate the best global individual found, as well as the best solutions found by each individual particle. Since an individual obtains useful information only from the local and global optimal individuals, it converges to the best solution quickly. PSO has become very popular because of its simplicity and convergence speed. However, there are many associated problems that require further study for extending PSO in solving multi-objective problems. The goal of this paper is to present the first application of PSO to multiobjective damage identification problems and investigate the applicability of several variations of the basic PSO technique. The potential of combining evolutionary computation and PSO concepts for damage identification problems is explored in this work by using a multiobjective evolutionary particle swarm optimization algorithm.

scholarly journals On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems

2006 ◽  
Vol 2006 ◽  
pp. 1-17 ◽  
Author(s):  
M. Senthil Arumugam ◽  
M. V. C. Rao
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Single Stage ◽  
Hybrid Manufacturing ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Real Coded Genetic Algorithm ◽  
The Impact

This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight (CIW), time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization (PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia weights separately to compute the optimal control of the single-stage hybrid manufacturing system, and it is observed that the PSO with the proposed inertia weight yields better result in terms of both optimal solution and faster convergence. Added to this, the optimal control problem is also solved through real coded genetic algorithm (RCGA) and the results are compared with the PSO algorithms. A typical numerical example is also included in this paper to illustrate the efficacy and betterment of the proposed algorithm. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

Particle Swarm Optimization Using Various Inertia Factor Variants

2011 ◽  
Vol 460-461 ◽  
pp. 54-59
Author(s):  
Jun Tang
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Time Varying ◽  
Swarm Optimization ◽  
The Impact

This paper presents an alternative and efficient method for solving the optimal control of manufacturing systems. Three different inertia factor, a constant inertia factor (CIF), time-varying inertia factor (TVIF), and global-local best inertia factor (GLbestIF), are considered with the particle swarm optimization(PSO) algorithm to analyze the impact of inertia factor on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia factor separately to compute the optimal control of the manufacturing system, and it is observed that the PSO with the proposed inertia factor yields better resultin terms of both optimal solution and faster convergence. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

Sign in / Sign up

Export Citation Format

Share Document