A two Sub-swarm Exchange Particle Swarm Optimization considering exploration and exploitation

2010 ◽  
Author(s):  
Jia Zhao ◽  
Hui Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Exchange Particle

Improved Fractional-Order PSO for PID Tuning

2021 ◽  
pp. 2159016
Author(s):  
Shoubao Su ◽  
Zhaorui Zhai ◽  
Chishe Wang ◽  
Kaimeng Ding
Keyword(s):  
Particle Swarm Optimization ◽  
Fractional Order ◽  
Particle Swarm ◽  
Population Diversity ◽  
Time Factor ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Pid Tuning ◽  
Convergence Performance

The traditional fractional-order particle swarm optimization (FOPSO) algorithm depends on the fractional order [Formula: see text], and it is easy to fall into local optimum. To overcome these disadvantages, a novel perspective with PID gains tuning procedure is proposed by combining the time factor with FOPSO, i.e. a new fractional-order particle swarm optimization called TFFV-PSO, which reduces the dependence on the fractional order to enhance the ability of particles to escape from local optimums. According to its influence on the performance of the algorithm, the time factor is varied with population diversity parameters to balance the exploration and exploitation capabilities of the particle swarm, so as to adjust the convergence speed of the algorithm, then it follows that a better convergence performance will be obtained. The improved method is tested on several benchmark functions and applied to tune the PID controller parameters. The experimental results and the comparison with previous other methods show that our proposed TFFV-PSO provides an adequate velocity of convergence and a satisfying accuracy, as well as even better robustness.

Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization

2011 ◽  
Vol 2 (3) ◽  
pp. 43-69 ◽  
Author(s):  
Shi Cheng ◽  
Yuhui Shi ◽  
Quande Qin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Population Diversity ◽  
Premature Convergence ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Boundary Constraints ◽  
Constraints Handling ◽  
The Impact ◽  
Search Information

Premature convergence happens in Particle Swarm Optimization (PSO) for solving both multimodal problems and unimodal problems. With an improper boundary constraints handling method, particles may get “stuck in” the boundary. Premature convergence means that an algorithm has lost its ability of exploration. Population diversity is an effective way to monitor an algorithm’s ability of exploration and exploitation. Through the population diversity measurement, useful search information can be obtained. PSO with a different topology structure and a different boundary constraints handling strategy will have a different impact on particles’ exploration and exploitation ability. In this paper, the phenomenon of particles gets “stuck in” the boundary in PSO is experimentally studied and reported. The authors observe the position diversity time-changing curves of PSOs with different topologies and different boundary constraints handling techniques, and analyze the impact of these setting on the algorithm’s ability of exploration and exploitation. From these experimental studies, an algorithm’s ability of exploration and exploitation can be observed and the search information obtained; therefore, more effective algorithms can be designed to solve problems.

scholarly journals Improved Barebones Particle Swarm Optimization with Neighborhood Search and Its Application on Ship Design

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jingzheng Yao ◽  
Duanfeng Han
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Ship Design ◽  
Neighborhood Search ◽  
Search Process ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Real World Problem ◽  
Complex Optimization

Barebones particle swarm optimization (BPSO) is a new PSO variant, which has shown a good performance on many optimization problems. However, similar to the standard PSO, BPSO also suffers from premature convergence when solving complex optimization problems. In order to improve the performance of BPSO, this paper proposes a new BPSO variant called BPSO with neighborhood search (NSBPSO) to achieve a tradeoff between exploration and exploitation during the search process. Experiments are conducted on twelve benchmark functions and a real-world problem of ship design. Simulation results demonstrate that our approach outperforms the standard PSO, BPSO, and six other improved PSO algorithms.

scholarly journals A general framework of particle swarm optimization

2021 ◽  
Author(s):  
Loc Nguyen
Keyword(s):  
Particle Swarm Optimization ◽  
General Framework ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Target Function ◽  
Optimal Solution ◽  
Exploration And Exploitation ◽  
Global Optimal Solution ◽  
Swarm Optimization ◽  
Global Optimal

Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, I propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence.

Dynamic Small World Network Topology for Particle Swarm Optimization

2016 ◽  
Vol 30 (09) ◽  
pp. 1660009 ◽  
Author(s):  
Qingxue Liu ◽  
Barend Jacobus van Wyk ◽  
Shengzhi Du ◽  
Yanxia Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Small World ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Small World Network ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Dynamic Topology ◽  
Neighborhood Topology

A new particle optimization algorithm with dynamic topology is proposed based on small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the Particle Swarm Optimization (PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more effective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems.

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