Journal of Global Optimization
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Population set-based global optimization algorithms: some modifications and numerical studies
Computers and Operations Research
Expert Systems with Applications: An International Journal
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
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This paper presents a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for quantification analysis of the lateral flow immunoassay (LFIA), which represents the first attempt to estimate the concentration of target analyte based on the well-established state-space model. A new switching local evolutionary PSO (SLEPSO) is developed and analyzed. The velocity updating equation jumps from one mode to another based on the non-homogeneous Markov chain, where the probability transition matrix is updated by calculating the diversity and current optimal solution. Furthermore, DE mutation and crossover operations are implemented to improve local best particles searching in PSO. Compared with some well-known PSO algorithms, the experiments results show the superiority of proposed SLEPSO. Finally, the new SLEPSO is successfully exploited to quantification analysis of the LFIA system, which is essentially nonlinear and dynamic. Therefore, this can provide a new method for the area of quantitative interpretation of LFIA system.