Differential evolution with subpopulations for high‐dimensional seismic inversion

Abstract

Seismic inversion has drawn the attention of researchers due to its capability of building an accurate earth model. Such a model will need to be discretised finely, and the dimensions of the inversion problem will be very high. In this paper, we propose an efficient differential evolution algorithm and apply it to high‐dimensional seismic inversion. Our method takes into account the differences among individuals, which are disregarded in conventional differential evolution methods, resulting to a better balance between exploration and exploitation. We divide the entire population into three subpopulations and propose a novel mutation strategy with two phases. Furthermore, we optimise the crossover operator by applying the components having the best objective function values into the crossover operator. We embed this strategy into a cooperative coevolutionary differential evolution and propose a new differential evolution algorithm referred to as a differential evolution with subpopulations. Then, we apply our scheme to both synthetic and field data; the results of high‐dimensional seismic inversion have shown that the proposed differential evolution with subpopulations achieves faster convergence and a higher‐quality solution for seismic inversion.

SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy

“…In MPEDE, (Wu et al 2016) proposed an integrated DE that uses three different mutation strategies to form multiple subpopulations for simultaneous evolution. In SpDE, (Pan et al 2018) divided the entire population into three subpopulations and proposed a new two-stage mutation strategy. The difference between individuals is considered in the evolution process.…”

“…Therefore, in order to solve the above problem, we summarize a large number of enhanced DE variants proposed by researchers in recent years, such as FADE (with fuzzy logic self-adaptive strategy) (Liu and Lampinen 2005), TDE (with trigonometric mutation operation) (Fan and Lampinen 2003), EDA (with distributed estimation strategy) (Sun et al 2005), jDE (with self-adaptive parameters) (Brest et al 2006), AnDE (with new mutation and selection operation and combining simulated annealing ideal) (Das et al 2007), JADE (with "current-to-pbest/1" mutation operation and self-adaptive parameters) (Zhang and Sanderson 2009), SaDE (with self-adaptive mutation strategies and parameters) (Qin et al 2009b), CoDE (with composite generation strategies and control parameters) (Wang et al 2011), SspDE (with self-adaptive strategies and control parameters) (Pan et al 2011), ESADE (with "current-to-pbest/1" mutation operation and self-adaptive control parameters) (Guo et al 2014), DMPSADE (with self-adaptive discrete mutation control parameters) (Fan and Yan 2015), MPEDE (with multiple mutation strategies and self-adaptive parameters) (Wu et al 2016), DEMPSO (combining particle swarm optimization ideal) (Mao et al 2017), SpDE (with multiple subpopulations and phase-mutations strategy) (Pan et al 2018), HyGADE (combining Genetic Algorithm ideal) (Chaudhary et al 2019), and SAMDE (with self-adaptive multipopulation strategy) (Zhu et al 2020). Through much experimental research and theoretical analysis, the efficiency and performance of DE variants in solving problems have been greatly improved.…”

An Enhancing Differential Evolution Algorithm with a Rank-Up Selection: RUSDE

“…The Grashof condition and the Sequence condition are introduced as the penalty functions. At last, the objective function of the optimal problem can be expressed as Equation (13) (13) where h 1 (x), h 2 (x) are the Grashof and Sequence condition, respectively. If the condition is satisfied, then the value is equal to 0, otherwise, it is set to 1; M 1 , M 2 are high values to penalize the objective function, and they are set to 10 4 .…”

“…Among them, the differential evolution (DE) algorithm and its variants showed excellent performance in competitions held under the IEEE Congress on Evolutionary Computation (CEC) conference series [11,12]. In recent years, DE has been widely applied in diverse fields, such as geography [13], chemistry [14], engineering design [15][16][17][18], image processing [19,20], and software development [21]. Additionally, there are many successful applications of the DE in these fields [22][23][24].…”