Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization

Pareto dominance-based multiobjective optimization has been successfully applied to constrained evolutionary optimization during the last two decades. However, as another famous multiobjective optimization framework, decomposition-based multiobjective optimization has not received sufficient attention from constrained evolutionary optimization. In this paper, we make use of decomposition-based multiobjective optimization to solve constrained optimization problems (COPs). In our method, first of all, a COP is transformed into a biobjective optimization problem (BOP). Afterward, the transformed BOP is decomposed into a number of scalar optimization subproblems. After generating an offspring for each subproblem by differential evolution, the weighted sum method is utilized for selection. In addition, to make decomposition-based multiobjective optimization suit the characteristics of constrained evolutionary optimization, weight vectors are elaborately adjusted. Moreover, for some extremely complicated COPs, a restart strategy is introduced to help the population jump out of a local optimum in the infeasible region. Extensive experiments on three sets of benchmark test functions, namely, 24 test functions from IEEE CEC2006, 36 test functions from IEEE CEC2010, and 56 test functions from IEEE CEC2017, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods

Regularity Model for Noisy Multiobjective Optimization

Regularity models have been used in dealing with noise-free multiobjective optimization problems. This paper studies the behavior of a regularity model in noisy environments and argues that it is very suitable for noisy multiobjective optimization. We propose to embed the regularity model in an existing multiobjective evolutionary algorithm for tackling noises. The proposed algorithm works well in terms of both convergence and diversity. In our experimental studies, we have compared several state-of-the-art of algorithms with our proposed algorithm on benchmark problems with different levels of noises. The experimental results showed the effectiveness of the regularity model on noisy problems, but a degenerated performance on some noisy-free problems

On the use of two reference points in decomposition based multiobjective evolutionary algorithms

Decomposition based multiobjective evolutionary algorithms approximate the Pareto front of a multiobjective optimization problem by optimizing a set of subproblems in a collaborative manner. Often, each subproblem is associated with a direction vector and a reference point. The settings of these parameters have a very critical impact on convergence and diversity of the algorithm. Some work has been done to study how to set and adjust direction vectors to enhance algorithm performance for particular problems. In contrast, little effort has been made to study how to use reference points for controlling diversity in decomposition based algorithms. In this paper, we first study the impact of the reference point setting on selection in decomposition based algorithms. To balance the diversity and convergence, a new variant of the multiobjective evolutionary algorithm based on decomposition with both the ideal point and the nadir point is then proposed. This new variant also employs an improved global replacement strategy for performance enhancement. Comparison of our proposed algorithm with some other state-of-the-art algorithms is conducted on a set of multiobjective test problems. Experimental results show that our proposed algorithm is promising

hsa-miR-125a-5p Enhances Invasion Ability in Non-Small Lung Carcinoma Cell Lines

Background and objective MicroRNAs (miRNAs) are short non-coding RNAs that posttranscriptionally regulate gene expression by partially binding complementary to target sites in mRNAs. Although some impaired miRNA regulations have been observed in many human cancers, the functions of miR-125a are still unclear. The aim of this study is to investigate the expression of hsa-miR-125a-5p in NSCLC cell lines and the relationship between hsa-miR-125a-5p and the invasion of lung cancer cells. Methods The expression of hsa-miR-125a-5p and the effectiveness for a given period time after being transfected sense hsa-miR-125a-5p 2’-O-methyl oligonucleotide, which were 24 h, 36 h, 48 h, 60 h and 72 h, were examined by realtime PCR. Meanwhile, we investigated the modification of invasive ability in A549 and NCI-H460 cells by transwell. Results Real-time PCR showed that hsa-miR-125a-5p was poorly-expressed in 6 lung cancer cell lines, especially in LH7, NCI-H460, SPC-A-1 and A549. The highest expression of hsa-miR-125a-5p occurred in the cells transfected with sense hsa-miR-125a-5p 2’-O-methyl oligonucleotide 36 h. Furthermore, the invasive abilities of A549 and NCI-H46O were enhanced by up-regulating hsa-miR-125a-5p. Conclusion hsa-miR-125a-5p was poorly-expressed in lung cancer cells and it could enhance lung cancer cell invasion by up-regulating hsa-miR-125a-5p

Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization

Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by experts. However, most constructive NCO methods cannot solve problems with large-scale instance sizes, which significantly diminishes their usefulness for real-world applications. In this work, we propose a novel Light Encoder and Heavy Decoder (LEHD) model with a strong generalization ability to address this critical issue. The LEHD model can learn to dynamically capture the relationships between all available nodes of varying sizes, which is beneficial for model generalization to problems of various scales. Moreover, we develop a data-efficient training scheme and a flexible solution construction mechanism for the proposed LEHD model. By training on small-scale problem instances, the LEHD model can generate nearly optimal solutions for the Travelling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP) with up to 1000 nodes, and also generalizes well to solve real-world TSPLib and CVRPLib problems. These results confirm our proposed LEHD model can significantly improve the state-of-the-art performance for constructive NCO. The code is available at https://github.com/CIAM-Group/NCO_code/tree/main/single_objective/LEHD.Comment: Accepted at NeurIPS 202

Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes

Numerical reasoning is an essential ability for NLP systems to handle numeric information. Recent research indicates that fine-tuning a small-scale model to learn generating reasoning processes alongside answers can significantly enhance performance. However, current methods have the limitation that most methods generate reasoning processes with large language models (LLMs), which are "unreliable" since such processes could contain information unrelated to the answer. To address this limitation, we introduce Enhancing NumeriCal reasOning with Reliable procEsses (Encore), which derives the reliable reasoning process by decomposing the answer formula, ensuring which fully supports the answer. Nevertheless, models could lack enough data to learn the reasoning process generation adequately, since our method generates only one single reasoning process for one formula. To overcome this difficulty, we present a series of pre-training tasks to help models learn the reasoning process generation with synthesized data. The experiments show that Encore yields improvement on all five experimental datasets with an average of 1.8%, proving the effectiveness of our method

Capillary and viscous forces during CO2 flooding in tight reservoirs

In this study, the multiphase multicomponent Shan-Chen lattice Boltzmann method is employed to analyze the impact of capillary force on oil-CO2-water  fluid flow and enhanced oil recovery. Various sizes of the single throat are designed to  simulate the interaction between displacing and displaced phases as well as their mechanical  equilibrium. Several sensitivities are taken into account, such as wettability, miscibility,  interfacial tension, and pore aperture. Based on the objective reservoir conditions,  supercritical CO2 as an injection fluid is adopted to study the influence of different displacement  patterns on the mechanical equilibrium in both homogenous and heterogeneous porous media, in  which enhanced oil recovery is also quantitatively estimated. The results show that the  water-alternating-gas injection pattern reduces the moving speed of the leading edge by  increasing the swept area of the residual oil, and inhibits the breakthrough effect of the  gas, making it the optimal displacement method in terms of the degree of oil production. Compared with the results of different displacement patterns, the enhanced oil recovery  of water-alternatinggas injection is the highest, followed by supercritical CO2 flooding after  water flooding, and lastly, continuous supercritical CO2 flooding.Cited as: Zhang, C., Zhang, Q., Wang, W., Xie, Q., Su, Y., Zafar, A. Capillary and viscous forces during CO2 flooding in tight reservoirs. Capillarity, 2022, 5(6): 105-114. https://doi.org/10.46690/capi.2022.06.0