Composite Particle Swarm Optimizer With Historical Memory for Function Optimization

Li, Jie; Zhang, Junqi; Jiang, Changjun; Zhou, MengChu

doi:10.1109/tcyb.2015.2424836

Cited by 154 publications

(41 citation statements)

References 43 publications

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“…The adaptive multi-kernel function of the linear combination is the most classic method [23] and is formed of a combination of an overall kernel function (polynomial) and a local kernel function (Gaussian). It can be written as:…”

Section: Multiple Kernel Rvmmentioning

confidence: 99%

“…In that way, the kernel selection problem is transformed into an optimization problem of kernel parameters and kernel weights in a multiple kernel structure. In recent years, the particle swarm optimization (PSO) algorithm [20][21][22][23] has been widely used to optimize the parameters of intelligent algorithms in many fields [24][25][26][27][28][29]. It is a classic group optimization technique that Kennedy and Eberhart [20,21] designed on the basis of the actions of birds feeding.…”

Section: Introductionmentioning

confidence: 99%

“…At present, studies of the PSO algorithm can be divided into two groups: improvement research and applied research. Most of the improvement research has concentrated on setting the inertia weight and learning factors of the PSO algorithm [22][23][24][25]. The adaptive cloud PSO (CPSO) algorithm is one of the most efficient methods in improvement research.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

Lin

Xia

Jiang

et al. 2016

IJGI

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Abstract:In this paper, we propose a multiple kernel relevance vector machine (RVM) method based on the adaptive cloud particle swarm optimization (PSO) algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the final kernel mapping. The weights and parameters of the multi-kernel function were optimized using the PSO algorithm. In addition, the convergence speed of the PSO algorithm was increased using cloud theory. To ensure the stability of the prediction model, the result of a five-fold cross-validation method was used as the fitness of the PSO algorithm. To verify the results, receiver operating characteristic curves (ROC) and landslide dot density (LDD) were used. The results show that the model that used a heterogeneous kernel (a combination of two different kernel functions) had a larger area under the ROC curve (0.7616) and a lower prediction error ratio (0.28%) than did the other types of kernel models employed in this study. In addition, both the sum of two high susceptibility zone LDDs (6.71/100 km 2 ) and the sum of two low susceptibility zone LDDs (0.82/100 km 2 ) demonstrated that the landslide susceptibility map based on the heterogeneous kernel model was closest to the historical landslide distribution. In conclusion, the results obtained in this study can provide very useful information for disaster prevention and land-use planning in the study area.

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Section: Multiple Kernel Rvmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

Lin

Xia

Jiang

et al. 2016

IJGI

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“…In [27], Qin et al has proposed an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. These scholars care more about the convergence and the learning strategy of PSO that improve the MOPSO indirectly [28][29][30]. Most of the above algorithms are based on archiving and these algorithms have been used in many fields [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

The Design of Contactors Based on the Niching Multiobjective Particle Swarm Optimization

et al. 2018

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Contactors are important components in circuits. To solve the multiobjective optimization problems (MOPs) of contactors, a niching multiobjective particle swarm optimization (NMOPSO) with the entropy weight ideal point theory is proposed in this paper. The new algorithm selecting and archiving the nondominated solutions based on the niching theory to ensure the diversity of the nondominated solutions. To avoid missing the extreme solutions of each objective during the multiobjective optimization process, extra particle swarms used to search the independent optimal solution of each objective are supplemented in this algorithm. In order to determine the best compromise solution, a method to select the compromise solution based on entropy weight ideal point theory is also proposed in this paper. Using the algorithm to optimize the characteristics of a typical direct-acting contactor, the results show that the proposed algorithm can obtain the best compromise solution in MOPs.

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“…Inspired by ecological cohabitation, chaotic multiswarm particle swarm optimization (CMS-PSO) modifies the generic PSO with the help of the chaotic sequence for multidimension unknown parameter estimation and optimization by forming multiple cooperating swarms [22]. Historical memory strategy in HMPSO [23], which estimates and preserves distribution information of particles' historical promising, is helpful in preserving the information of optimum solution space and making a comprehensive learning.…”

Section: Introductionmentioning

confidence: 99%

Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

Wang

et al. 2017

Discrete Dynamics in Nature and Society

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A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on -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.

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Composite Particle Swarm Optimizer With Historical Memory for Function Optimization

Cited by 154 publications

References 43 publications

Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

The Design of Contactors Based on the Niching Multiobjective Particle Swarm Optimization

Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

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