Diversity-based Information Exchange among Multiple Swarms in Particle Swarm Optimization

2006 ◽  
Author(s):  
G.G. Yen ◽  
M. Daneshyari
Keyword(s):  
Particle Swarm Optimization ◽  
Information Exchange ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multiple Swarms

DIVERSITY-BASED INFORMATION EXCHANGE AMONG MULTIPLE SWARMS IN PARTICLE SWARM OPTIMIZATION

2008 ◽  
Vol 07 (01) ◽  
pp. 57-75 ◽  
Author(s):  
GARY G. YEN ◽  
MOAYED DANESHYARI
Keyword(s):  
Particle Swarm Optimization ◽  
Information Exchange ◽  
Particle Swarm ◽  
Evolutionary Search ◽  
Test Functions ◽  
Swarm Optimization ◽  
Local Optima ◽  
Common Interests ◽  
Multiple Swarms ◽  
Benchmark Test Functions

This paper proposes a method to exchange information among multiple swarms in particle swarm optimization (PSO) to facilitate evolutionary search. The algorithm is developed to solve problems having landscapes with a large number of local optima. Each swarm maintains two sets of particles; one set includes the particles to be shared with other swarms, while the other involves the particles to be replaced by individuals from other swarms. The proposed algorithm also provides a new design to search for neighboring swarms in order to share common interests among the swarm's neighborhood. The particle's movement is according to one variation of PSO with three basic terms, each one to lead the particles toward the best particle in the swarm, in the neighborhood, and in the whole population. Demonstrated through a suite of benchmark test functions, the proposed algorithm shows competitive performance with improved convergence speed.

scholarly journals Video Superresolution via Parameter-Optimized Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yunyi Yan ◽  
Yujie He ◽  
Yingying Hu ◽  
Baolong Guo
Keyword(s):  
Particle Swarm Optimization ◽  
High Resolution ◽  
Video Sequence ◽  
Signal To Noise Ratio ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Objective Standard ◽  
Objective Performance ◽  
Multiple Swarms

Video superresolution (VSR) aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO). We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS-PSO as an optimization method to solve the VSR problem, which overcomes the poor effect, low accuracy, and large calculation cost in other VSR algorithms. The proposed VSR method does not require exact movement estimation and does not need the computation of movement vectors. In terms of peak signal-to-noise ratio (PSNR), sharpness, and entropy, the proposed VSR method based POMS-PSO showed better objective performance. Besides objective standard, experimental results also proved the proposed method could reconstruct high-resolution video sequence with better subjective quality.

A modified multi swarm particle swarm optimization algorithm using an adaptive factor selection strategy

2021 ◽  
pp. 014233122110295
Author(s):  
Jaouher Chrouta ◽  
Fethi Farhani ◽  
Abderrahmen Zaafouri
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Information Exchange ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Selection Strategy ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Suggested Approach ◽  
Inertia Weight

In the present study, we suggest a modified version of heterogeneous multi-swarm particle swarm optimization (MSPSO) algorithm, that allows the amelioration of its performance by introducing an adaptive inertia weight approach. In order to bring about a balance between the exploration and exploitation characteristics of MSPSO allowing to promote information exchange amongst the subswarms. However, the classical MSPSO algorithm search behavior has not always been optimal in finding the optimal solution to certain problems, which results in falling into local optimum leading to premature convergence. The most advantages of the MSPSO there are easy to implement and there are few parameters to adjust. The inertia weight (w) is one of the most Particle Swarm Optimization’s (PSO) parameters. Controlling this parameter could facilitate the convergence and prevent an explosion of the swarm. To overcome the above limitations, this paper proposes a heterogeneous multi swarm PSO algorithm based on PSO number selection approach centred on the idea of particle swarm referred to as Multi-Swarm Particle Swarm Optimization algorithm with Factor selection strategy (FMSPSO). In the various process implementations of the particle swarm search, different parameter selection strategies are adopted to ameliorate the global search ability. The proposed FMSPSO is able to improve the population’s diversity and better explore the entire feature space. The statistical test and indicators that are reported in the specialized literature demonstrate that the suggested approach is superior in terms of efficiency to nine other popular PSO algorithms in solving the optimization problem of complex problems. The approach suggests that FMSPSO reaches a very promising performance for solving different types of optimization problems, leading eventually to higher solution accuracy.

A Multi-Swarm Cooperative Perturbed Particle Swarm Optimization

2011 ◽  
Vol 225-226 ◽  
pp. 619-622
Author(s):  
Xiang Jun Yang ◽  
Yi Long Zhao ◽  
Yu Chuang Chen ◽  
Xin Chao Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Information Sharing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Single Population ◽  
Swarm Optimization ◽  
Convergence And Stability ◽  
Multiple Swarms

Combined with a variety of ideas a Multi-swarm cooperative Perturbed Particle Swarm Optimization algorithm (MpPSO) is presented to improve the performance and to reduce the premature convergence of PSO. This algorithm includes the idea of multiple swarms to improve the evolution efficiency by information sharing between populations to avoid falling into local optimum caused by single population. It also includes the idea of perturbing the swarms beside the global best solution, which can escape from local optimum. Experiments show that the proposed algorithm MpPSO has better performance, better convergence and stability when comparing with the traditional and the recently improved particle swarm optimization.

A Novel Hybrid Optimization Method of Shuffled Frog Leaping Algorithm and Particle Swarm Optimization

2013 ◽  
Vol 717 ◽  
pp. 433-438 ◽  
Author(s):  
Mei Jin Lin ◽  
Fei Luo ◽  
Yu Ge Xu ◽  
Long Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Information Exchange ◽  
Search Strategy ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Hybrid Optimization ◽  
Local Optimum ◽  
Shuffled Frog Leaping Algorithm ◽  
Swarm Optimization ◽  
Shuffled Frog Leaping

Shuffled frog leaping algorithm (SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low convergence speed while solving complex optimization problems. Particle swarm optimization (PSO) is a fast searching algorithms, but easily falls into the local optimum for the diversity scarcity of particles. In the paper, a new hybrid optimization called SFLA-PSO is proposed, which introduced PSO to SFLA by combining the fast search strategy of PSO and global search strategy of SFLA. Six benchmark functions are selected to compare the performance of SFLA-PSO, basic PSO, wPSO and SFLA. The simulation results show that the proposed algorithm SFLA-PSO possesses outstanding performance in the convergence speed and the precision of the global optimum solution.

scholarly journals Utilizing the Particle Swarm Optimization Algorithm for Determining Control Parameters for Civil Structures Subject to Seismic Excitation

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 292
Author(s):  
Courtney A. Peckens ◽  
Andrea Alsgaard ◽  
Camille Fogg ◽  
Mary C. Ngoma ◽  
Clara Voskuil
Keyword(s):  
Particle Swarm Optimization ◽  
Information Exchange ◽  
Control Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Weight Matrix ◽  
Effective Control ◽  
Control Force ◽  
Control Parameters ◽  
Swarm Optimization

Structural control of civil infrastructure in response to large external loads, such as earthquakes or wind, is not widely employed due to challenges regarding information exchange and the inherent latencies in the system due to complex computations related to the control algorithm. This study employs front-end signal processing at the sensing node to alleviate computations at the control node and results in a simplistic sum of weighted inputs to determine a control force. The control law simplifies to U = WP, where U is the control force, W is a pre-determined weight matrix, and P is a deconstructed representation of the response of the structure to the applied excitation. Determining the optimal weight matrix for this calculation is non-trivial and this study uses the particle swarm optimization (PSO) algorithm with a modified homing feature to converge on a possible solution. To further streamline the control algorithm, various pruning techniques are combined with the PSO algorithm in order to optimize the number of entries in the weight matrix. These optimization techniques are applied in simulation to a five-story structure and the success of the resulting control parameters are quantified based on their ability to minimize the information exchange while maintaining control effectiveness. It is found that a magnitude-based pruning method, when paired with the PSO algorithm, is able to offer the most effective control for a structure subject to seismic base excitation.

Empirical study on rotation and information exchange in particle swarm optimization

2019 ◽  
Vol 48 ◽  
pp. 312-328 ◽  
Author(s):  
Reginaldo Santos ◽  
Gilvan Borges ◽  
Adam Santos ◽  
Moisés Silva ◽  
Claudomiro Sales ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Empirical Study ◽  
Information Exchange ◽  
Particle Swarm ◽  
Swarm Optimization

A Novel Neural Fuzzy Network Using a Hybrid Evolutionary Learning Algorithm

2010 ◽  
pp. 250-273
Author(s):  
Cheng-Jian Lin ◽  
Cheng-Hung Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Evolutionary Learning ◽  
Functional Link ◽  
Swarm Optimization ◽  
Multiple Swarms ◽  
Fuzzy Network ◽  
Neural Fuzzy Network ◽  
Neural Fuzzy

This chapter presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. This chapter uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Finally, the proposed functional-link-based neural fuzzy network with cultural cooperative particle swarm optimization (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.

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