Niching particle swarm optimization with equilibrium factor for multi-modal optimization

Li, Yikai; Chen, Yongliang; Zhong, Jinghui; Huang, Zhixing

doi:10.1016/j.ins.2019.01.084

Cited by 39 publications

(11 citation statements)

References 45 publications

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“…The error goal for BP is set as 0.001 and the iteration number is 500. ARMA is a classical method for processing time series; after calculation, the order of the model is (4,8), then the autoregressive coefficient is calculated as [0.5909 0.5777 0.3926 0.5616] 1 × 4 , and the moving average coefficient is calculated as [0.2464, 0.4266, . .…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…In this study, the initial space of the particle position vector was set to C ∈ [0.1, 100], γ ∈ [0.01, 1000], ε ∈ [0.01, 100], and D ∈ [4,20]. Other parameters were set as follows: the population size was 40, and the total iteration number was 400, k-CV was 10.…”

Section: Multi-step Predictionmentioning

confidence: 99%

“…British scholars Wallbridge and Dowson defined wear as a stochastic process, determining the wear parameter distributions of some metals based on probability and mathematical statistics, and thus opening up a new research direction in wear prediction [3]. The Grey model GM (1,1) has also been used to predict the wear trends of engines [4]. With the development of artificial intelligence technology, the accuracy of wear prediction has also been improved.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO

Zheng

Gao

et al. 2021

Applied Sciences

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In order to predict aeroengine wear accurately and automatically, as a predictor, support vector regression (SVR) was optimized by means of particle swarm optimization (PSO). The guided mutation strategy of PSO (GMPSO) is presented herein to determine the proper structure parameters of an SVR, as well as the embedding dimensions of the training samples. The guided mutation strategy was able to increase the diversity of particles and improve the probability of finding the global extremum. Furthermore, single-step and multi-step prediction methods were designed to meet different accuracy requirements. A prediction comparison study on spectral analysis data was carried out, and the contrast experiments show that compared with SVR optimized by means of a traditional PSO, a neural network and an auto regressive moving average (ARMA) prediction model, the SVR optimized by means of the GMPSO approach produced prediction results not only with higher accuracy, but also with better consistency.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Multi-step Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO

Zheng

Gao

et al. 2021

Applied Sciences

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“…The multimodal optimization problem is a complex function optimization problem with one or more local extrema [ 1 ]. In practical applications, there are many multimodal optimization problems, such as parameter estimation and identification of models [ 2 , 3 ], engineering structure optimization, welded beam design [ 4 ], and medical diagnosis [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Chicken Swarm Optimization Algorithm for Solving Multimodal Optimization Problems

Liang

Wang

2022

Computational Intelligence and Neuroscience

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To solve the premature convergence problem of the standard chicken swarm optimization (CSO) algorithm in dealing with multimodal optimization problems, an improved chicken swarm optimization (ICSO) algorithm is proposed by referring to the ideas of bacterial foraging algorithm (BFA) and particle swarm optimization (PSO) algorithm. First, in order to improve the depth search ability of the algorithm, considering that the chicks have the weakest optimization ability in the whole chicken swarm, the replication operation of BFA is introduced. In the role update process of the chicken swarm, the chicks are replaced by the same number of chickens with the strongest optimization ability. Then, to maintain the population diversity, the elimination-dispersal operation is introduced to disperse the chicks that have performed the replication operation to any position in the search space according to a certain probability. Finally, the PSO algorithm is integrated to improve the global optimization ability of the algorithm. The experimental results on the CEC2014 benchmark function test suite show that the proposed algorithm has good performance in most test functions, and its optimization accuracy and convergence performance are also better than BFA, artificial fish swarm algorithm (AFSA), genetic algorithm (GA), and PSO algorithm, etc. In addition, the ICSO is also utilized to solve the welded beam design problem, and the experimental results indicate that the proposed algorithm has obvious advantages over other comparison algorithms. Its disadvantage is that it is not suitable for dealing with large-scale optimization problems.

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“…(2) Neighborhood topology-based principle: In order to enhance the individuals' information communication and maintain the population diversity, lots of novel PSO variants based on researching on the particles in a neighborhood are proposed. Niche technique [14], dynamic neighborhood [15], neighborhood information sharing [16], and so on, they are widely used strategies for neighborhood topology. The advantage of this principle can balance the exploitation and exploration ability of PSO, which makes the optimization capacity of population better.…”

Section: Introductionmentioning

confidence: 99%

Team Collaboration Particle Swarm Optimization and Its Application on Reliability Optimization

Zheng¹,

Ma²,

Zhang³

et al. 2021

IJCIS

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Particle swarm optimization (PSO) tends to be premature convergence due to easily trapping into local suboptimal areas. In order to overcome the PSO's defects, the reasons causing the defects are analyzed and summarized as population diversity deficiency, insufficient information sharing, unbalance of exploitation and exploration, and single update strategy. On this basis, inspired by human team collaboration behavior, a team collaboration particle swarm optimization (TCPSO) is proposed. Diversified updates strategies, dynamic grouping strategy, selectivity vector, and decreasing and increasing inertia weight are designed in TCPSO to solve the defects' reasons and improve the optimization performance. Eight typical test functions have been used to evaluate and compare the performance of different PSO variants, and the results have been proven that the optimal results found by TCPSO are better compared with other PSO variants, which demonstrates the rationality and effectiveness of TCPSO. Finally, a real-world problem for reliability optimization are solved by five algorithms, and the results prove the convergence rate and stable optimization performance of TCPSO, TCPSO can provide better support for reliability optimization of complex system.

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Niching particle swarm optimization with equilibrium factor for multi-modal optimization

Cited by 39 publications

References 45 publications

Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO

Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO

An Improved Chicken Swarm Optimization Algorithm for Solving Multimodal Optimization Problems

Team Collaboration Particle Swarm Optimization and Its Application on Reliability Optimization

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