Hybrid Evolutionary Algorithm Based on PSO and GA Mutation

Esmin, Ahmed Ali Abdalla; Lambert-Torres,; Alvarenga, Bernardo Pinheiro de

doi:10.1109/his.2006.264940

Cited by 67 publications

(33 citation statements)

References 7 publications

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“…PSO group interaction enhances the search for an optimal solution, whereas GA has trouble finding an exact solution and is best at reaching a global region. GA and PSO hybrid strategies can be considered as a method of developing more effective and efficient searching strategies to overcome the weakness of a pure single algorithm (Esmin et al (2006)). …”

Section: Ten-degrees-of-freedom Shear-type Building Systemmentioning

confidence: 99%

Identification of Structural Systems Using Particle Swarm Optimization

Xue

Tang

Zhou

2009

Journal of Asian Architecture and Building Engineering

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Particle swarm optimization (PSO) is a new heuristic method that has yielded promising results for solving complex optimization problems. Its advantages are a simple structure, ease of use, quality of solution, and robustness. This paper utilizes the PSO algorithm for parameter estimation of structural systems, which could be formulated as a multi-modal numerical optimization problem with high dimension. Simulation results for identifying the parameters of multiple degree-of-freedom (DOF) linear and nonlinear structural systems are presented to demonstrate the effectiveness of the proposed method.

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Section: Ten-degrees-of-freedom Shear-type Building Systemmentioning

confidence: 99%

Identification of Structural Systems Using Particle Swarm Optimization

Xue

Tang

Zhou

2009

Journal of Asian Architecture and Building Engineering

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“…There are several studies in the text which have been prepared to combine Particle Swarm Optimization variant with other variants of metaheuristics such as hybrid Particle Swarm Optimization with Genetic Algorithm (PSOGA) [3,4], Particle Swarm Optimization with Differential Evolution (PSODE) [5], and Particle Swarm Optimization with Ant Colony Optimization (PSOACO) [6]. These hybrid algorithms are aimed at reducing the probability of trapping in local optimum.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

Singh

2017

Journal of Applied Mathematics

228

143

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A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer to produce both variants' strength. Some unimodal, multimodal, and fixeddimension multimodal test functions are used to check the solution quality and performance of HPSOGWO variant. The numerical and statistical solutions show that the hybrid variant outperforms significantly the PSO and GWO variants in terms of solution quality, solution stability, convergence speed, and ability to find the global optimum.

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“…Intelligent algorithms such as GA and particle swarm optimization (PSO) have shown good performances in optimization problems [7,20]. These intelligent algorithms based watermark techniques can simultaneously improve security, robustness, and image quality of the watermarked images [44].…”

Section: 2genetic Algorithms and Swarm Intelligencementioning

confidence: 99%

The Use of Computational Intelligence in Digital Watermarking: Review, Challenges, and New Trends

Darwish¹,

Abraham²

2011

NNW

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Digital Watermarking (DW) based on computational intelligence (CI) is currently attracting considerable interest from the research community. This article provides an overview of the research progress in applying CI methods to the problem of DW. The scope of this review will encompass core methods of CI, including rough sets (RS), fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithms (GA), swarm intelligence (SI), and hybrid intelligent systems. The research contributions in each field are systematically summarized and compared to highlight promising new research directions. The findings of this review should provide useful insights into the current DW literature and be a good source for anyone who is interested in the application of CI approaches to DW systems or related fields. In addition, hybrid intelligent systems are a growing research area in CI.

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Hybrid Evolutionary Algorithm Based on PSO and GA Mutation

Cited by 67 publications

References 7 publications

Identification of Structural Systems Using Particle Swarm Optimization

Identification of Structural Systems Using Particle Swarm Optimization

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

The Use of Computational Intelligence in Digital Watermarking: Review, Challenges, and New Trends

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