Evolutionary multitasking (EMT) is a newly emerging research topic in the community of evolutionary computation, which aims to improve the convergence characteristic across multiple distinct optimization tasks simultaneously by triggering knowledge transfer among them. Unfortunately, most of the existing EMT algorithms are only capable of boosting the optimization performance for homogeneous problems which explicitly share the same (or similar) fitness landscapes. Seldom efforts have been devoted to generalize the EMT for solving heterogeneous problems. A few preliminary studies employ domain adaptation techniques to enhance the transferability between two distinct tasks. However, almost all of these methods encounter a severe issue which is the so-called degradation of intertask mapping. Keeping this in mind, a novel rank loss function for acquiring a superior intertask mapping is proposed in this article. In particular, with an evolutionary-path-based representation model for optimization instance, an analytical solution of affine transformation for bridging the gap between two distinct problems is mathematically derived from the proposed rank loss function. It is worth mentioning that the proposed mapping-based transferability enhancement technique can be seamlessly embedded into an EMT paradigm. Finally, the efficacy of our proposed method against several state-of-the-art EMTs is verified experimentally on a number of synthetic multitasking and many-tasking benchmark problems, as well as a practical case study.

Sparse multiobjective optimization problems (MOPs) have become increasingly important in many applications in recent years, e.g., the search for lightweight deep neural networks and high-dimensional feature selection. However, little attention has been paid to sparse large-scale MOPs, whose Pareto-optimal sets are sparse, i.e., with many decision variables equal to zero. To address this issue, this article proposes an enhanced competitive swarm optimization algorithm assisted by a strongly convex sparse operator (SCSparse). A tricompetition mechanism is introduced into competitive swarm optimization, aiming to strike a better balance between exploration and exploitation. In addition, the SCSparse is embedded in the position updating of the particles to generate sparse solutions. Our simulation results show that the proposed algorithm outperforms the state-of-the-art methods on both sparse test problems and application examples.

Limestone pore structure strongly influences dissolution and associated reactive transport. These effects are critical in limestone diagenesis and but also in engineering operations such as carbon capture and storage (CCS). However, detailed studies on how CO₂‐enriched (acidic) brine changes this pore structure at relevant reservoir storage conditions are very limited. Thus, to provide further quantitative information and more fundamental understanding about these key processes, we studied the dissolution patterns of a homogeneous, a fractured, and a vuggy limestone when flooded with CO₂‐saturated brine at representative storage conditions. The pore structured of these limestones showed drastically different responses to the acidic brine flood. As such, preferential channels surrounded by branched channels were formed in the homogeneous sample, while fractures became the main flow path in the fractured sample. In contrast, only one dominant channel formed in the vuggy sample, which resulted in a sharp permeability increase. These dissolution patterns reflect the associated Damköhler number, which significantly lower in the homogeneous, representing uniform dissolution. However, after injecting sufficient reactive fluid (1,000 PV), this uniform dissolution pattern transformed into a single preferential channel growth. Moreover, we conclude that increasing complexity of the pore geometry leads to more nonuniform dissolution. These dissolution patterns indicate the effect of initial pore structure on preferential channel growth and reaction transport. Our work provides key fundamental data for further quantifying limestone dissolution patterns in CCS, indicating that the CO₂ injection may cause the reactivation of geological faults and damage around wellbore, thus aids in the implementation of industrial‐scale CCS.

A major limitation of fuzzy or neuro-fuzzy systems is their failure to deal with high-dimensional datasets. This happens primarily due to the use of T-norm, particularly, product or minimum (or a softer version of it). Thus, there are hardly any work dealing with datasets having features more than hundred or so. Here, we propose a neuro-fuzzy framework that can handle datasets with even more than 7000 features! In this context, we propose an adaptive softmin (Ada-softmin) which effectively overcomes the drawbacks of “numeric underflow” and “fake minimum” that arise for existing fuzzy systems while dealing with high-dimensional problems. We call it an Adaptive Takagi-Sugeno-Kang (AdaTSK) fuzzy system. We then equip the AdaTSK system to perform feature selection and rule extraction in an integrated manner. In this context, a novel gate function is introduced and embedded only in the consequent parts, which can determine the useful features and rules, in two successive phases of learning. Unlike conventional fuzzy rule bases, we design an enhanced fuzzy rule base (En-FRB), which maintains adequate rules but does not grow the number of rules exponentially with features that typically happens for fuzzy neural networks. The integrated Feature Selection and Rule Extraction AdaTSK (FSRE-AdaTSK) system consists of three sequential phases: (i) feature selection, (ii) rule extraction, and (iii) fine tuning. The effectiveness of the FSRE-AdaTSK is demonstrated on 19 datasets of which five are in more than 2000 dimension including two with more than 7000 features. This may be the first attempt to develop fuzzy rule based classifiers that can directly deal with more than 7000 features without requiring separate selection of features or any other dimensionality reduction method.