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 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.

Particle Swarm Optimization Algorithms Inspired by Immunity-Clonal Mechanism and Their Applications to Spam Detection

2012 ◽  
pp. 182-205 ◽  
Author(s):  
Ying Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Spam Detection ◽  
Swarm Optimization ◽  
Hole Model ◽  
Successive Generations ◽  
Benchmark Test Functions

Compared to conventional PSO algorithm, particle swarm optimization algorithms inspired by immunity-clonal strategies are presented for their rapid convergence, easy implementation and ability of optimization. A novel PSO algorithm, clonal particle swarm optimization (CPSO) algorithm, is proposed based on clonal principle in natural immune system. By cloning the best individual of successive generations, the CPSO enlarges the area near the promising candidate solution and accelerates the evolution of the swarm, leading to better optimization capability and faster convergence performance than conventional PSO. As a variant, an advance-and-retreat strategy is incorporated to find the nearby minima in an enlarged solution space for greatly accelerating the CPSO before the next clonal operation. A black hole model is also established for easy implementation and good performance. Detailed descriptions of the CPSO algorithm and its variants are elaborated. Extensive experiments on 15 benchmark test functions demonstrate that the proposed CPSO algorithms speedup the evolution procedure and improve the global optimization performance. Finally, an application of the proposed PSO algorithms to spam detection is provided in comparison with the other three methods.

Optimization Design of Gravity Dam Section Based on PSO Algorithm

2012 ◽  
Vol 424-425 ◽  
pp. 535-539
Author(s):  
Liang Ming Hu ◽  
Yi Zhi Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Gravity Dam ◽  
Actual Situation ◽  
Swarm Optimization

Particle Swarm Optimization (PSO) algorithm is a technique for optimization based on iteration, which initializes system to product a series of random solutions, in this solution space, particles commit themselves to search for a better solution and in the final the optimal one is found. Applying this algorithm to the design of gravity dam section then we find: PSO, as shown by the example given in this paper, is an available algorithm which is not only tally with the actual situation, but safe and economical. So, PSO provides a new idea and method for optimization design of gravity dam section.

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 Locally tuned hybridized particle swarm optimization for the calibration of the nonlinear Muskingum flood routing model

2020 ◽  
Vol 11 (S1) ◽  
pp. 343-358 ◽  
Author(s):  
Umut Okkan ◽  
Umut Kirdemir
Keyword(s):  
Particle Swarm Optimization ◽  
Initial Conditions ◽  
Particle Swarm ◽  
Solution Space ◽  
Global Solutions ◽  
Pso Algorithm ◽  
Flood Routing ◽  
Swarm Optimization ◽  
Model Benchmark ◽  
Lm Algorithm

Abstract In the literature about the parameter estimation of the nonlinear Muskingum (NL-MUSK) model, benchmark hydrographs have been subjected to various metaheuristics, and in these studies the minor improvements of the algorithms on objective functions are imposed as ‘state-of-the-art’. With the metaheuristics involving more control variables, the attempt to search global results in a restricted solution space is not actually practical. Although metaheuristics provide reasonable results compared with many derivative methods, they cannot guarantee the same global solution when they run under different initial conditions. In this study, one of the most practical of metaheuristics, the particle swarm optimization (PSO) algorithm, was chosen, and the aim was to develop its local search capability. In this context, the hybrid use of the PSO with the Levenberg–Marquardt (LM) algorithm was considered. It was detected that the hybrid PSO–LM gave stable global solutions as a result of each random experiment in the application for four different flood data. The PSO–LM, which stands out with its stable aspect, also achieved rapid convergence compared with the PSO and another hybrid variant called mutated PSO.

Rebirthing particle swarm optimization algorithm: application to storm water network design

2008 ◽  
Vol 35 (10) ◽  
pp. 1120-1127 ◽  
Author(s):  
M. H. Afshar
Keyword(s):  
Particle Swarm Optimization ◽  
Network Design ◽  
Particle Swarm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Network Design Problem ◽  
Storm Water ◽  
Water Network ◽  
Swarm Optimization ◽  
Original Algorithm

Stochastic search methods, such as the particle swarm optimization (PSO) algorithm, are primarily directed by two main features — exploration and exploitation. Exploration is the ability of the algorithm to broadly search through the solution space for new quality solutions, whereas exploitation is responsible for refining the search in the neighborhood of the good solutions found previously. Proper balance between these features is sought, to obtain good performance of these algorithms. An explorative mechanism is introduced in this paper to improve the performance of the PSO algorithm. The method is based on introducing artificial exploration into the algorithm by randomly repositioning the particles approaching stationary status. A velocity measure is used to distinguish between flying and stationary particles. This can be sought as a sudden death followed by a rebirth of these particles. Two options are tested for the rebirthing mechanism, which are (i) clearing and (ii) keeping the memory of rebirthing particles. The global best particle is exempted from rebirthing process so that the most useful of the swarm’s past experiences is not lost. The method is applied to a benchmark storm water network design problem and the results are presented and compared with those of the original algorithm and other methods. The proposed method, though simple, is shown to be very effective in avoiding local optima, leading to an improved version of the algorithm at no extra computational effort.

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.

Particle Swarm Optimization Algorithm Based on Escape Boundary

2011 ◽  
Vol 361-363 ◽  
pp. 1426-1431
Author(s):  
Wen Hua Han
Keyword(s):  
Boundary Condition ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Population Based ◽  
Global Optimum ◽  
Swarm Optimization

The particle swarm optimization (PSO) is a population-based stochastic evolutionary algorithm, noted for its capability of searching for the global optimum of complex problems. Particles flying out of the solution space will lead to invalid solutions. So often in engineering applications, boundary condition is used to confine the particles within the solution space. In this paper, a new boundary is proposed, which is called as escape boundary. The solution space is divided into three sections, that is, the inside，escape boundary and the outside of the boundary. The location of the global solution in the solution space, accordingly has two types, that is, the global optimum around the center of the solution space, and the global optimum close to the escape boundary. The proposed boundary is introduced into the PSO algorithm, and is compared to the damping boundary. The experimental results show that the PSO based on escape boundary has better search ability and faster convergence rate.

An Particle Swarm Optimization Approach for Assembly Sequence Planning

2009 ◽  
Vol 16-19 ◽  
pp. 1228-1232 ◽  
Author(s):  
Hong Yu ◽  
Jia Peng Yu ◽  
Wen Lei Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Assembly Sequence Planning ◽  
Assembly Planning ◽  
Optimization Approach ◽  
Assembly Sequence ◽  
Swarm Optimization ◽  
Sequence Planning

Assembly sequence planning (ASP) is the foundation of the assembly planning which plays a key role in the whole product life cycle. Although the ASP problem has been tackled via a variety of optimization techniques, the particle swarm optimization (PSO) algorithm is scarcely used. This paper presents a PSO algorithm to solve ASP problem. Unlike generic versions of particle swarm optimization, the algorithm redefines the particle's position and velocity, and operation of updating particle positions. In order to overcome the problem of premature convergence, a new study mechanism is adopted. The geometrical constraints, assembly stability and the changing times of assembly directions are used as the criteria for the fitness function. To validate the performance of the proposed algorithm, a 29-component product is tested by this algorithm. The experimental results indicate that the algorithm proposed in this paper is effective for the ASP.

Optimizing Assembly Sequence Time Using Particle Swarm Optimization (PSO)

2013 ◽  
Vol 315 ◽  
pp. 88-92 ◽  
Author(s):  
Jameel A.A. Mukred ◽  
Mohd Taufiq Muslim ◽  
Hazlina Selamat
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Optimization Techniques ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Swarm Optimization ◽  
Large Numbers ◽  
Searching Ability

Assembly sequence planning (ASP) plays an important role in the production planning and should be optimized to minimize production time and cost when large numbers of parts and sub-assemblies are involved in the assembly process. Although the ASP problem has been tackled via a variety of optimization techniques, these techniques are often inefficient when applied to larger-scale problems. In this study, an approach using particle swarm optimization (PSO) is proposed to tackle one of the ASP problems which are optimizing the assembly sequence time. PSO uses a number of agents (particles) that constitute a swarm moving around in the search space looking for the best solution. Each bird, called particle, learns from its own best position and the globally best position. Experimental results show that PSO algorithm can produce good results in optimizing the assembly time, has a powerful global searching ability and fast rate of convergence.

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