Random walk autonomous groups of particles for particle swarm optimization

2021 ◽  
pp. 1-27
Author(s):  
Xinliang Xu ◽  
Fu Yan

Autonomous groups of particles swarm optimization (AGPSO), inspired by individual diversity in biological swarms such as insects or birds, is a modified particle swarm optimization (PSO) variant. The AGPSO method is simple to understand and easy to implement on a computer. It has achieved an impressive performance on high-dimensional optimization tasks. However, AGPSO also struggles with premature convergence, low solution accuracy and easily falls into local optimum solutions. To overcome these drawbacks, random-walk autonomous group particle swarm optimization (RW-AGPSO) is proposed. In the RW-AGPSO algorithm, Levy flights and dynamically changing weight strategies are introduced to balance exploration and exploitation. The search accuracy and optimization performance of the RW-AGPSO algorithm are verified on 23 well-known benchmark test functions. The experimental results reveal that, for almost all low- and high-dimensional unimodal and multimodal functions, the RW-AGPSO technique has superior optimization performance when compared with three AGPSO variants, four PSO approaches and other recently proposed algorithms. In addition, the performance of the RW-AGPSO has also been tested on the CEC’14 test suite and three real-world engineering problems. The results show that the RW-AGPSO is effective for solving high complexity problems.

2017 ◽  
Vol 2017 ◽  
pp. 1-22
Author(s):  
Danping Wang ◽  
Kunyuan Hu ◽  
Lianbo Ma ◽  
Maowei He ◽  
Hanning Chen

A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.


2011 ◽  
Vol 148-149 ◽  
pp. 1468-1472
Author(s):  
Ping Wang ◽  
Jun Liang Xu ◽  
Tao Lu

On the basis of superstructure of heat exchanger network (HEN), we established a particle swarm optimization (PSO) model of HEN with no splits, with the target of minimizing investment and operation cost. A typical HEN was solved via a modified particle swarm optimization (PSO). Through comparative of the optimization result, we could know that this method could reach better solution accuracy.


Kursor ◽  
2016 ◽  
Vol 8 (1) ◽  
pp. 33
Author(s):  
Alrijadjis Alrijadjis

Particle Swarm Optimization (PSO) is a popular optimization technique which is inspired by the social behavior of birds flocking or fishes schooling for finding food. It is a new metaheuristic search algorithm developed by Eberhart and Kennedy in 1995. However, the standard PSO has a shortcoming, i.e., premature convergence and easy to get stack or fall into local optimum. Inertia weight is an important parameter in PSO, which significantly affect the performance of PSO. There are many variations of inertia weight strategies have been proposed in order to overcome the shortcoming. In this paper, a new modified PSO with random activation to increase exploration ability, help trapped particles for jumping-out from local optimum and avoid premature convergence is proposed. In the proposed method, an inertia weight is decreased linearly until half of iteration, and then a random number for an inertia weight is applied until the end of iteration. To emphasis the role of this new inertia weight adjustment, the modified PSO paradigm is named Modified PSO with random activation (MPSO-RA). The experiments with three famous benchmark functions show that the accuracy and success rate of the proposed MPSO-RA increase of 43.23% and 32.95% compared with the standard PSO.


2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
J. J. Jamian ◽  
M. N. Abdullah ◽  
H. Mokhlis ◽  
M. W. Mustafa ◽  
A. H. A. Bakar

The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through sharing information of particle position between the dimensions (variables) at any iteration. The strategy can enhance the exploration capability of the GPSO algorithm to determine the optimum global solution and avoid traps at the local optimum. The proposed GPSO algorithm is validated on a 12-benchmark mathematical function and compared with three different types of PSO techniques. The performance of this algorithm is measured based on the solutions’ quality, convergence characteristics, and their robustness after 50 trials. The simulation results showed that the new updated strategy in GPSO assists in realizing a better optimum solution with the smallest standard deviation value compared to other techniques. It can be concluded that the proposed GPSO method is a superior technique for solving high dimensional numerical function optimization problems.


Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Le Chen ◽  
Ying Feng ◽  
Rui Li ◽  
Xinkai Chen ◽  
Hui Jiang

Shape memory alloy- (SMA-) based actuators are widely applied in the compliant actuating systems. However, the measured data of the SMA-based compliant actuating system reveal the input-output hysteresis behavior, and the actuating precision of the compliant actuating system could be degraded by such hysteresis nonlinearities. To characterize such nonlinearities in the SMA-based compliant actuator precisely, a Jiles-Atherton model is adopted in this paper, and a modified particle swarm optimization (MPSO) algorithm is proposed to identify the parameters in the Jiles-Atherton model, which is a combination of several differential nonlinear equations. Compared with the basic PSO identification algorithm, the designed MPSO algorithm can reduce the local optimum problem so that the Jiles-Atherton model with the identified parameters can show good agreements with the measured experimental data. The good capture ability of the proposed identification algorithm is also examined through the comparisons with Jiles-Atherton model using the basic PSO identification algorithm.


2017 ◽  
Vol 11 (3) ◽  
pp. 271-280
Author(s):  
Zhensi Lin ◽  
Qishan Zhang

A novel hybrid particle swarm optimization variant is proposed, which combines particle swarm optimization with Gaussian mutation operation based on random strategy. It applies Gaussian mutation on the positions of some randomly selected particles to enhance the search accuracy and convergence speed of swarm. The proposed algorithm can retain the diversity of population and improve the ability of global search. A suite of benchmark test functions is employed to evaluate the performance of the proposed method. The results have been compared with three state-of-the-art particle swarm optimization variants. Experimental results show that the Gaussian mutation and the random select strategy help the proposed algorithm to achieve faster convergence rate and provide better solutions in most of the problems. Further, the new algorithm has been tested on the high-dimensional problems. The results show that the proposed algorithm is not sensitive to high-dimensional problems and can even give a better performance. Moreover, the sensitivity analysis of the parameters was carried out and the setting of the parameters was given.


2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Sami Zdiri ◽  
Jaouher Chrouta ◽  
Abderrahmen Zaafouri

In this study, a modified version of multiswarm particle swarm optimization algorithm (MsPSO) is proposed. However, the classical MsPSO algorithm causes premature stagnation due to the limitation of particle diversity; as a result, it is simple to slip into a local optimum. To overcome the above feebleness, this work presents a heterogeneous multiswarm PSO algorithm based on adaptive inertia weight strategies called (A-MsPSO). The MsPSO’s main advantages are that it is simple to use and that there are few settings to alter. In the MsPSO method, the inertia weight is a key parameter affecting considerably convergence, exploration, and exploitation. In this manuscript, an adaptive inertia weight is adopted to ameliorate the global search ability of the classical MsPSO algorithm. Its performance is based on exploration, which is defined as an algorithm’s capacity to search through a variety of search spaces. It also aids in determining the best ideal capability for searching a small region and determining the candidate answer. If a swarm discovers a global best location during iterations, the inertia weight is increased, and exploration in that direction is enhanced. The standard tests and indications provided in the specialized literature are used to show the efficiency of the proposed algorithm. Furthermore, findings of comparisons between A-MsPSO and six other common PSO algorithms show that our proposal has a highly promising performance for handling various types of optimization problems, leading to both greater solution accuracy and more efficient solution times.


2014 ◽  
Vol 10 (02) ◽  
pp. 163-175
Author(s):  
Guorong Cai ◽  
Shaozi Li ◽  
Shuili Chen ◽  
Songzhi Su

This paper proposes a novel cooperative particle swarm PSO (particle swarm optimization)algorithm, which makes the use of the property of the fuzzy migratory operator to achieve the optimization performance. To avoid the drawback of the possibility of being trapped in local optimum, the proposed method uses a migrate-based strategy to control the diversity of the swarm. During the iteration aspect of the algorithm, the comprehensive fuzzy evaluation method is employed to evaluate the diversity. Furthermore, the fuzzy migratory operator is then used to remove bad particles once the diversity is far from ideal. Moreover, we have proven that the proposed migrate strategy is a mean square convergence. The experimental results conducted on three benchmark functions also proved that the proposed method is superior to that of classical PSO and conventional cooperative PSO, where the comparison has been based primarily upon the global optimality, solution accuracy and diversity value.


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