scholarly journals Adaptive Particle Swarm Optimization with Gaussian Perturbation and Mutation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Binbin Chen ◽  
Rui Zhang ◽  
Long Chen ◽  
Shengjie Long
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Adaptive Strategy ◽  
Local Optimum ◽  
Neighborhood Structure ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Single Strategy ◽  
Gaussian Perturbation ◽  
Searching Ability

The particle swarm optimization (PSO) is a wide used optimization algorithm, which yet suffers from trapping in local optimum and the premature convergence. Many studies have proposed the improvements to address the drawbacks above. Most of them have implemented a single strategy for one problem or a fixed neighborhood structure during the whole search process. To further improve the PSO performance, we introduced a simple but effective method, named adaptive particle swarm optimization with Gaussian perturbation and mutation (AGMPSO), consisting of three strategies. Gaussian perturbation and mutation are incorporated to promote the exploration and exploitation capability, while the adaptive strategy is introduced to ensure dynamic implement of the former two strategies, which guarantee the balance of the searching ability and accuracy. Comparison experiments of proposed AGMPSO and existing PSO variants in solving 29 benchmark functions of CEC 2017 test suites suggest that, despite the simplicity in architecture, the proposed AGMPSO obtains a high convergence accuracy and significant robustness which are proven by conducted Wilcoxon’s rank sum test.

Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

2013 ◽  
Vol 760-762 ◽  
pp. 2194-2198 ◽  
Author(s):  
Xue Mei Wang ◽  
Yi Zhuo Guo ◽  
Gui Jun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Clustering Quality ◽  
Mutation Operation

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

Study on Multi-Depot Vehicle Routing Problem Based on Cloud Adaptive Particle Swarm Optimization

2012 ◽  
Vol 253-255 ◽  
pp. 1369-1373
Author(s):  
Tie Jun Wang ◽  
Kai Jun Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Cloud Model ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Adaptive Particle Swarm Optimization ◽  
Searching Ability

Multi-depots vehicle routing problem (MDVRP) is a kind of NP combination problem which possesses important practical value. In order to overcome PSO’s premature convergence and slow astringe, a Cloud Adaptive Particle Swarm Optimization(CAPSO) is put forward, it uses the randomicity and stable tendentiousness characteristics of cloud model, adopts different inertia weight generating methods in different groups, the searching ability of the algorithm in local and overall situation is balanced effectively. In this paper, the algorithm is used to solve MDVRP, a kind of new particles coding method is constructed and the solution algorithm is developed. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability than GA and the PSO algorithm.

scholarly journals A Parallel Adaptive Particle Swarm Optimization Algorithm for Economic/Environmental Power Dispatch

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Jinchao Li ◽  
Jinying Li ◽  
Dongxiao Niu ◽  
Yunna Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Power Dispatch

A parallel adaptive particle swarm optimization algorithm (PAPSO) is proposed for economic/environmental power dispatch, which can overcome the premature characteristic, the slow-speed convergence in the late evolutionary phase, and lacking good direction in particles’ evolutionary process. A search population is randomly divided into several subpopulations. Then for each subpopulation, the optimal solution is searched synchronously using the proposed method, and thus parallel computing is realized. To avoid converging to a local optimum, a crossover operator is introduced to exchange the information among the subpopulations and the diversity of population is sustained simultaneously. Simulation results show that the proposed algorithm can effectively solve the economic/environmental operation problem of hydropower generating units. Performance comparisons show that the solution from the proposed method is better than those from the conventional particle swarm algorithm and other optimization algorithms.

scholarly journals An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ming Li ◽  
Wenqiang Du ◽  
Fuzhong Nian
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Complex Network ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Weighted Complex Network

The disadvantages of particle swarm optimization (PSO) algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO) is proposed. Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position. At the same time, an evolutionary mechanism of the directed dynamic network is employed to make the particles evolve into the scale-free network when the in-degree obeys power-law distribution. In the proposed method, not only the diversity of the algorithm was improved, but also particles’ falling into local optimum was avoided. The simulation results indicate that the proposed algorithm can effectively avoid the premature convergence problem. Compared with other algorithms, the convergence rate is faster.

Optimization of Maximum Entropy Model Oriented to Bayes Prior Distribution Based on PSO

2010 ◽  
Vol 37-38 ◽  
pp. 814-818
Author(s):  
Yi Dai ◽  
Zhong Min Wang ◽  
Guang Ling
Keyword(s):  
Particle Swarm Optimization ◽  
Maximum Entropy ◽  
Prior Distribution ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Entropy Model ◽  
Swarm Optimization ◽  
Set Up ◽  
Searching Ability

How to acquire prior distribution is a key to Bayes method. Firstly, a nonlinear constrained optimal model of probability density function based on the principle of maximum entropy is set up. By using Lagrange multiplier this constrained optimal problem is transformed to a non-constrained optimal one, which is solved by standard particle swarm optimization (PSO) algorithm. Secondly, a new improved particle swarm optimization (IPSO) algorithm is proposed because standard PSO is slow on convergence and easy to be trapped in local optimum. IPSO introduces the hybrid method from genetic algorithm (GA) so that the overall searching ability is enhanced; then linear decreasing inertia weight is used to optimize particles. The simulation examples show that IPSO is simple and effective and it can rapidly converge with high quality solutions.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

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