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.

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

2010 ◽  
Vol 1 (1) ◽  
pp. 64-86 ◽  
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.

Dynamic Particle Swarm Optimization with Any Irregular Initial Small-World Topology

2017 ◽  
pp. 1185-1208
Author(s):  
Shuangxin Wang ◽  
Guibin Tian ◽  
Dingli Yu ◽  
Yijiang Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Great Influence ◽  
Particle Swarm ◽  
Wind Farms ◽  
Small World ◽  
Laplacian Matrix ◽  
Pso Algorithm ◽  
Clustering Coefficient ◽  
Swarm Optimization ◽  
Benchmark Test Functions

It is realized that the topological structure of the particle swarm optimization (PSO) algorithm has a great influence on its optimization ability. This paper presents a new dynamic small-world neighborhood PSO (D-SWPSO) algorithm whose neighbourhood structure can be constructed with any irregular initial networks. The choice of the learning exemplar is not only based upon the big clustering coefficient and the average shortest distance for a regular network, but also based upon the eigenvalues of Laplacian matrix for irregular networks. Therefore, the D-SWPSO is a PSO algorithm based on small-world topological neighbourhood with universal significance. The proposed algorithm is tested by some typical benchmark test functions, and the results confirm that there is a significant improvement over the basic PSO algorithm. Finally, the algorithm is applied to a real-world optimization problem, the economic dispatch on the IEEE30 system with wind farms. The results demonstrate that the proposed D-SWPSO is a practically feasible and effective algorithm.

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.

scholarly journals Particle Swarm Optimization

2017 ◽  
pp. 614-635 ◽  
Author(s):  
Alaa Tharwat ◽  
Tarek Gaber ◽  
Aboul Ella Hassanien ◽  
Basem E. Elnaghi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Pso Algorithm ◽  
Search Space ◽  
Learning Problems ◽  
Swarm Optimization ◽  
Numerical Example

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

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 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.

scholarly journals Artificial Soul Optimization - An Invention

2019 ◽  
Vol 7 (5) ◽  
pp. 36-44
Author(s):  
Satish Gajawada ◽  
Hassan Mustafa
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Main Idea ◽  
Pso Algorithm ◽  
Search Space ◽  
Swarm Optimization

The Soul is eternal and exists even after death of a person or animal. The main idea that is captured in this work is that soul continues to exist and takes a different a body after the death. The primary goal of this work is to invent a new field titled "Artificial Soul Optimization (ASO)". The term "Artificial Soul Optimization" is coined in this paper. All the Optimization algorithms which are proposed based on Artificial Souls will come under "Artificial Soul Optimization" Field (ASO Field). In the Particle Swarm Optimization and Artificial Human Optimization, the basic entities in search space are Artificial Birds and Artificial Humans respectively. Similarly, in Artificial Soul Optimization, the basic entities in search space are Artificial Souls. In this work, the ASO Field concepts are added to Particle Swarm Optimization (PSO) algorithm to create a new hybrid algorithm titled "Soul Particle Swarm Optimization (SoPSO). The proposed SoPSO algorithm is applied on various benchmark functions. Results obtained are compared with PSO algorithm. The World's first Hybrid PSO algorithm based on Artificial Souls is created in this work.

Dynamic Particle Swarm Optimization with Any Irregular Initial Small-World Topology

2015 ◽  
Vol 6 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Shuangxin Wang ◽  
Guibin Tian ◽  
Dingli Yu ◽  
Yijiang Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Great Influence ◽  
Particle Swarm ◽  
Wind Farms ◽  
Small World ◽  
Laplacian Matrix ◽  
Pso Algorithm ◽  
Clustering Coefficient ◽  
Swarm Optimization ◽  
Benchmark Test Functions

It is realized that the topological structure of the particle swarm optimization (PSO) algorithm has a great influence on its optimization ability. This paper presents a new dynamic small-world neighborhood PSO (D-SWPSO) algorithm whose neighbourhood structure can be constructed with any irregular initial networks. The choice of the learning exemplar is not only based upon the big clustering coefficient and the average shortest distance for a regular network, but also based upon the eigenvalues of Laplacian matrix for irregular networks. Therefore, the D-SWPSO is a PSO algorithm based on small-world topological neighbourhood with universal significance. The proposed algorithm is tested by some typical benchmark test functions, and the results confirm that there is a significant improvement over the basic PSO algorithm. Finally, the algorithm is applied to a real-world optimization problem, the economic dispatch on the IEEE30 system with wind farms. The results demonstrate that the proposed D-SWPSO is a practically feasible and effective algorithm.

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 Artificial God Optimization - A Creation

2020 ◽  
Vol 13 (1) ◽  
pp. 41
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
The World ◽  
Complex Optimization ◽  
Primary Focus

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence, the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field, which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm, which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.

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