A stochastic iterative evolution CT reconstruction algorithm for limited-angle sparse projection data

Stochastic local search: a state-of-the-art review

“…Luo [27] proposed a stochastic iterative evolution CT reconstruction algorithm for limited-angle sparse projection data. The working principle of this algorithm is as follow: a stochastic approach is applied for searching, and Markov Chain is used to predict iterative evolution model and accelerate the proposed algorithm's convergence.…”

“…Another significant contribution to the field includes a paper by Boughaci et al, [22] with regard to the application of SLS for image steganography, Wang et al, [23] on estimation of the distribution algorithm with a SLS for uncertain capacitated arc routing problems, followed by Rezoug and Boughaci [24] dealing with integration of self-adaptive harmony search with a SLS for tackling knapsack problem. Furthermore, in the last two years there are several other studies conducted with regard to the SLS application, including Putikhin and Kascheev [25], they used SLS for solving SAT problems by extending continuous Boolean formulas, Chu et al, [26] presented neighboring variables based configuration checking in SLS for weighted partial maximum satisfiability, Luo [27], who applied stochastic iterative evolution CT reconstruction algorithm for limited-angle sparse projection data, Yu et al, [28] introduced the Thompson sampling for optimizing SLS. Oliveira et al, [29] studied analysis of the ACO algorithm for solving TSP, Paquete and Stützle [30] presented a review of SLS Algorithms for multi objective combinatorial optimization, Niu et al, [31] introduced a new SLS approach for computing preferred extensions of abstract argumentation, Santos et al, [32], who performed analysis of SLS methods for the unrelated parallel machine scheduling problem and Weise et al, [33], who showed an improved generic BET-AND-RUN strategy with performance prediction for SLS.…”