2022
DOI: 10.2174/1574893616666210727164226
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Improved Hybrid Particle Swarm Optimizer with Sine-Cosine Acceleration Coefficients for Transient Electromagnetic Inversion

Abstract: Background: Recently, particle swarm optimization (PSO) has been increasingly used in geophysics due to its simple operation and fast convergence. Objective: However, PSO lacks population diversity and may fall to local optima. Hence, an improved hybrid particle swarm optimizer with sine-cosine acceleration coefficients (IH-PSO-SCAC) is proposed and successfully applied to test functions and in transient electromagnetic (TEM) nonlinear inversion. Method: A reverse learning strategy is applied to optimize p… Show more

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Cited by 7 publications
(6 citation statements)
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“…By referring to the solutions of other heuristic optimization algorithms to solve engineering problems, and improving the way of evolution of heuristic algorithms, an applicable multi-threading technology can be developed based on the characteristics of the optimizer [31], and the search efficiency can be improved by optimizing the population of candidate solutions [32][33][34]. The binary scheme selector [35], the optimization strategy selector [36][37][38], and the dynamic parameter selector [36,37,39] can be used.…”
Section: Machine Learning Inversionmentioning
confidence: 99%
“…By referring to the solutions of other heuristic optimization algorithms to solve engineering problems, and improving the way of evolution of heuristic algorithms, an applicable multi-threading technology can be developed based on the characteristics of the optimizer [31], and the search efficiency can be improved by optimizing the population of candidate solutions [32][33][34]. The binary scheme selector [35], the optimization strategy selector [36][37][38], and the dynamic parameter selector [36,37,39] can be used.…”
Section: Machine Learning Inversionmentioning
confidence: 99%
“…In the early stages, the focus should be placed on individual cognition, while in the later stages, emphasis should be placed more on population cognition. Inspired by the sine-cosine algorithm (SCA) mathematical model, this study dynamically adjusted the learning factor by leveraging its volatility and periodicity [43].…”
Section: Dynamic Adjustment Of Model Parametersmentioning
confidence: 99%
“…The constriction coefficient introduced into the PSO algorithm enhance the convergence speed and improves the balance between global exploration and local exploitation [29]. The sinecosine acceleration coefficients are introduced into the particle swarm optimizer in [30]. A proportional factor based on the Nash equilibrium is incorporated in the PSO in [31].…”
Section: Evolutionary Algorithms For the Optimization Of Stochastic G...mentioning
confidence: 99%