scholarly journals GPU-Based Parallel Particle Swarm Optimization Methods for Graph Drawing

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Jianhua Qu ◽  
Xiyu Liu ◽  
Minghe Sun ◽  
Feng Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Graph Drawing ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Population Based ◽  
Pictorial Representation ◽  
Swarm Optimization ◽  
Wide Range

Particle Swarm Optimization (PSO) is a population-based stochastic search technique for solving optimization problems, which has been proven to be effective in a wide range of applications. However, the computational efficiency on large-scale problems is still unsatisfactory. A graph drawing is a pictorial representation of the vertices and edges of a graph. Two PSO heuristic procedures, one serial and the other parallel, are developed for undirected graph drawing. Each particle corresponds to a different layout of the graph. The particle fitness is defined based on the concept of the energy in the force-directed method. The serial PSO procedure is executed on a CPU and the parallel PSO procedure is executed on a GPU. Two PSO procedures have different data structures and strategies. The performance of the proposed methods is evaluated through several different graphs. The experimental results show that the two PSO procedures are both as effective as the force-directed method, and the parallel procedure is more advantageous than the serial procedure for larger graphs.

The Novel Parameter Selection of Particle Swarm Optimization

2012 ◽  
Vol 479-481 ◽  
pp. 344-347
Author(s):  
Zhuo Li ◽  
Xue Luo Qu
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Based ◽  
Swarm Optimization ◽  
Intelligent Technique ◽  
The Social ◽  
Scale Optimization ◽  
Artificial Intelligent Technique

Particle Swarm Optimization (PSO) is a novel artificial intelligent technique proposed by Eberhart and Kennedy which is a type of Swarm Intelligence. PSO is simulated as population-based stochastic optimization influenced by the social behavior of bird flocks. In past decades, more and more researcher has been targeting to improve the original PSO for solving various problems and it has great potential to be done further. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

scholarly journals An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Feng Qian ◽  
Mohammad Reza Mahmoudi ◽  
Hamïd Parvïn ◽  
Kim-Hung Pho ◽  
Bui Anh Tuan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Global Optimum ◽  
Initial Population ◽  
Swarm Optimization ◽  
Conventional Optimization

Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.

scholarly journals An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaobing Yu ◽  
Jie Cao ◽  
Haiyan Shan ◽  
Li Zhu ◽  
Jun Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Based ◽  
Stochastic Search ◽  
Adaptive Mutation ◽  
Swarm Optimization ◽  
Local Optima

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail.

Concurrent Societies Based on Genetic Algorithm and Particle Swarm Optimization

2010 ◽  
Vol 14 (1) ◽  
pp. 110-118
Author(s):  
Hrvoje Markovic ◽  
◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Fitness Function ◽  
Particle Swarm ◽  
Absolute Error ◽  
Population Based ◽  
Test Problems ◽  
Swarm Optimization

A parallel multi-population based metaheuristic optimization framework, called Concurrent Societies, inspired by human intellectual evolution, is proposed. It uses population based metaheuristics to evolve its populations, and fitness function approximations as representations of knowledge. By utilizing iteratively refined approximations it reduces the number of required evaluations and, as a byproduct, it produces models of the fitness function. The proposed framework is implemented as two Concurrent Societies: one based on genetic algorithm and one based on particle swarm optimization both using k -nearest neighbor regression as fitness approximation. The performance is evaluated on 10 standard test problems and compared to other commonly used metaheuristics. Results show that the usage of the framework considerably increases efficiency (by a factor of 7.6 to 977) and effectiveness (absolute error reduced by more than few orders of magnitude). The proposed framework is intended for optimization problems with expensive fitness functions, such as optimization in design and interactive optimization.

scholarly journals A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Waqas Haider Bangyal ◽  
Abdul Hameed ◽  
Wael Alosaimi ◽  
Hashem Alyami
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Critical Factor ◽  
Pso Algorithm ◽  
Population Based ◽  
Test Problems ◽  
Accuracy Score ◽  
Swarm Optimization ◽  
Function Integration

Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique used to search for food with the intrinsic manner of bee swarming. PSO is widely used to solve the diverse problems of optimization. Initialization of population is a critical factor in the PSO algorithm, which considerably influences the diversity and convergence during the process of PSO. Quasirandom sequences are useful for initializing the population to improve the diversity and convergence, rather than applying the random distribution for initialization. The performance of PSO is expanded in this paper to make it appropriate for the optimization problem by introducing a new initialization technique named WELL with the help of low-discrepancy sequence. To solve the optimization problems in large-dimensional search spaces, the proposed solution is termed as WE-PSO. The suggested solution has been verified on fifteen well-known unimodal and multimodal benchmark test problems extensively used in the literature, Moreover, the performance of WE-PSO is compared with the standard PSO and two other initialization approaches Sobol-based PSO (SO-PSO) and Halton-based PSO (H-PSO). The findings indicate that WE-PSO is better than the standard multimodal problem-solving techniques. The results validate the efficacy and effectiveness of our approach. In comparison, the proposed approach is used for artificial neural network (ANN) learning and contrasted to the standard backpropagation algorithm, standard PSO, H-PSO, and SO-PSO, respectively. The results of our technique has a higher accuracy score and outperforms traditional methods. Also, the outcome of our work presents an insight on how the proposed initialization technique has a high effect on the quality of cost function, integration, and diversity aspects.

scholarly journals A Novel Multiobjective Quantum-Behaved Particle Swarm Optimization Based on the Ring Model

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Di Zhou ◽  
Yajun Li ◽  
Bin Jiang ◽  
Jun Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Population Based ◽  
Swarm Optimization ◽  
Ring Model ◽  
Global Optima ◽  
Searching Ability ◽  
Update Strategy

Due to its fast convergence and population-based nature, particle swarm optimization (PSO) has been widely applied to address the multiobjective optimization problems (MOPs). However, the classical PSO has been proved to be not a global search algorithm. Therefore, there may exist the problem of not being able to converge to global optima in the multiobjective PSO-based algorithms. In this paper, making full use of the global convergence property of quantum-behaved particle swarm optimization (QPSO), a novel multiobjective QPSO algorithm based on the ring model is proposed. Based on the ring model, the position-update strategy is improved to address MOPs. The employment of a novel communication mechanism between particles effectively slows down the descent speed of the swarm diversity. Moreover, the searching ability is further improved by adjusting the position of local attractor. Experiment results show that the proposed algorithm is highly competitive on both convergence and diversity in solving the MOPs. In addition, the advantage becomes even more obvious with the number of objectives increasing.

scholarly journals The Siting and Sizing Problem of Distributed Generation Based on a Novel Fractional Particle Swarm Optimization Algorithm

2021 ◽  
Vol 257 ◽  
pp. 01036
Author(s):  
Fengming Zhang ◽  
Lingyan Que ◽  
Xinxin Zhang ◽  
Fumian Wang ◽  
Bing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Optimization Algorithm ◽  
Capacity Planning ◽  
Large Scale ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Environmental Benefits ◽  
Swarm Optimization

Large-scale distributed generation grid-connection brings huge economic and environmental benefits, but also threatens the stability of the grid. To make the grid consume a higher proportion of distributed generation, it is necessary to optimize the location and capacity of the distributed generation connected to the grid. Firstly, the uncertainty analysis model of wind speed, illumination intensity, and load of grid is established. Secondly, a distributed generation location and capacity planning model with the lowest annual comprehensive cost as the objective function is constructed. Then, a novel fractional particle swarm optimization algorithm is proposed, and the performance of the algorithm on complex optimization problems is tested. Finally, the simulation results of the IEEE 33-bus system example verify the rationality of the established model and the effectiveness of the proposed algorithm.

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