Two-stage Evolution Research Articles

AS2LS: Adaptive Anatomical Structure-based Two-layer Level Set Framework for Medical Image Segmentation.

Medical images often exhibit intricate structures, inhomogeneous intensity, significant noise and blurred edges, presenting challenges for medical image segmentation. Several segmentation algorithms grounded in mathematics, computer science, and medical domains have been proposed to address this matter; nevertheless, there is still considerable scope for improvement. This paper proposes a novel adaptive anatomical structure-based two-layer level set framework (AS2LS) for segmenting organs with concentric structures, such as the left ventricle and the fundus. By adaptive fitting region and edge intensity information, the AS2LS achieves high accuracy in segmenting complex medical images characterized by inhomogeneous intensity, blurred boundaries and interference from surrounding organs. Moreover, we introduce a novel two-layer level set representation based on anatomical structures, coupled with a two-stage level set evolution algorithm. Experimental results demonstrate the superior accuracy of AS2LS in comparison to representative level set methods and deep learning methods.

Paleozoic tectono-thermal history of the amalgamation of the Tarim–North-China and Mongolian collages

In the Mongolian Collage, metamorphic pressure – temperature ( P–T ) and timing reveal a one-stage evolution defined by a duality of late Neoproterozoic–Ordovician subduction-related low T/P metamorphism and supra-subduction high T/P metamorphism recorded in the Mongolia-Manchuria and Baikal-Sayan belts. This was followed by gradual prevalence of supra-subduction high T/P metamorphism towards the late Paleozoic corresponding to the Altai and South Altai cycles. In the Tarim–North-China Collage, metamorphic P–T and timing reveal a two-stage evolution, from dominant intermediate T / P metamorphism possibly resulting from Ordovician–Devonian amalgamation and Andean-type evolution of the collage, to dual low and high T / P metamorphism in the Carboniferous–Permian reflecting subduction-collision processes along the South Tianshan suture in the west and a supra-subduction evolution along the Solonker suture in the east. Altogether, the Paleozoic tectono-metamorphic evolution of the two collages in the Central Asian Orogenic Belt show remarkable differences, with the Mongolian Collage displaying features typical of peripheral accretionary style reflecting recurrent tectonic switches that can be regarded as a single orogenic system, and a two-stage evolution of the Tarim–North-China Collage with features of both peripheral–accretionary and interior-collisional orogenic cycles, but mostly related to recurrent subductions of interior oceans.

Two-stage differential evolution with dynamic population assignment for constrained multi-objective optimization

Using infeasible information to balance objective optimization and constraint satisfaction is a very promising research direction to address constrained multi-objective problems (CMOPs) via evolutionary algorithms (EAs). The existing constrained multi-objective evolutionary algorithms (CMOEAs) still face the issue of striking a good balance when solving CMOPs with diverse characteristics. To alleviate this issue, in this paper we develop a two-stage different evolution with a dynamic population assignment strategy for CMOPs. In this approach, two cooperative populations are used to provide feasible driving forces and infeasible guiding knowledge. To adequately utilize the infeasibility information, a dynamic population assignment model is employed to determine the primary population, which is used as the parents to generate offspring. The entire search process is divided into two stages, in which the two populations work in weak and strong cooperative ways, respectively. Furthermore, multistrategy-based differential evolution operators are adopted to create aggressive offspring. The superior exploration and exploitation ability of the proposed algorithm is validated via some state-of-the-art CMOEAs over artificial benchmarks and real-world problems. The experimental results show that our proposed algorithm gained a better, or more competitive, performance than the other competitors, and it is an effective approach to balancing objective optimization and constraint satisfaction.

Two-stage evolution of mammalian adipose tissue thermogenesis.

Brown adipose tissue (BAT) is a heater organ that expresses thermogenic uncoupling protein 1 (UCP1) to maintain high body temperatures during cold stress. BAT thermogenesis is considered an overarching mammalian trait, but its evolutionary origin is unknown. We show that adipose tissue of marsupials, which diverged from eutherian mammals ~150 million years ago, expresses a nonthermogenic UCP1 variant governed by a partial transcriptomic BAT signature similar to that found in eutherian beige adipose tissue. We found that the reconstructed UCP1 sequence of the common eutherian ancestor displayed typical thermogenic activity, whereas therian ancestor UCP1 is nonthermogenic. Thus, mammalian adipose tissue thermogenesis may have evolved in two distinct stages, with a prethermogenic stage in the common therian ancestor linking UCP1 expression to adipose tissue and thermal stress. We propose that in a second stage, UCP1 acquired its thermogenic function specifically in eutherians, such that the onset of mammalian BAT thermogenesis occurred only after the divergence from marsupials.

Two-Stage Information Spreading Evolution on the Control Role of Announcements

Two-Stage Information Spreading Evolution on the Control Role of Announcements

An enhanced sparrow search swarm optimizer via multi-strategies for high-dimensional optimization problems

With the development of science and technology, high-dimensional global optimization problems have become increasingly prevalent for scientific research and engineering, such as gene recognition, vehicle routing, job scheduling, and network topology. These problems are typically characterized by enormous and complex search spaces and numerous local minima, making it challenging to find the global optimal solution with limited computing resources. This paper introduces an enhanced sparrow search swarm optimizer (ESSSO) based on a bio-mimetic method. The ESSSO employs an adaptive sinusoidal walk strategy based on the von Mises distribution, a learning strategy utilizing roulette wheel selection, a two-stage evolution strategy, and a selection mutation strategy to address these issues. The proposed sinusoidal walk strategy, grounded in the von Mises distribution, supports a balanced evolutionary search. This mechanism disperses the individuals in a swarm in various directions based on a circular normal distribution. It then leads the search and adaptively adjusts their step sizes according to the size of the search domain during each generation of evolution. The learning strategy, based on roulette wheel selection, enhances the diversity of the population and improves the global search capability of the algorithm during the initial iterations. The two-stage evolution strategy involves a sine-learning mechanism based on the von Mises distribution and an adaptive mutation mechanism. The former is designed to boost the convergence speed of ESSSO, while the latter prevents ESSSO from getting trapped in a local optimum. Additionally, the selection mutation strategy further enhances convergence speed while maintaining population diversity. These strategies promote exploration in the early stages of evolution and exploitation in the later stages, enabling a well-balanced search for optimal solutions. We conducted comprehensive experiments two standard benchmark sets (i.e., CEC2010 and CEC2013), antenna array optimization, feature selection, and four engineering design problems. The results indicate that ESSSO outperforms ten comparison algorithms, especially in scenarios with smaller population sizes. This confirms its effectiveness in high-dimensional global optimization tasks and demonstrates that it can achieve better results with less computational resource consumption.

The penetration phenomenon of the expiratory airflow from thermal plume of human body in the microenvironment around people

Understanding the influence of the human body's thermal plume on exhaled airflow is crucial for accurately assessing indoor infection risks. This study employs numerical simulations and a jet integral modeling method to investigate the airflow dispersion patterns of a single thermal manikin during sustained expiration. It was found that the thermal plume formed a two-stage evolution in exhaled airflow. Initially, the airflow was weakened by the thermal plume in the horizontal dispersion, and subsequently it was bent due to the entrainment of the surrounding air in the upward dispersion of the exhaled airflow. The thermal plume changes the trajectory and lateral dispersion distance of exhaled airflow. When the exhalation velocity is 1 m/s, the airflow is fully deflected towards the head plume region. When the exhalation velocity is between 1.5 and 2 m/s, the airflow is partially deflected, with only part of the upper airflow deflected towards the head plume region. In addition, it is interesting to note that the plume changes the dispersion trajectory of the exhaled airflow, while still adhering to the distributions of buoyant jet flow. This study suggests that the thermal effect of the human body is a significant factor in characterizing the dispersion of exhalation airflow and aerosols. Considering the thermal plume in indoor infection risk assessment is expected to yield more reliable data for theoretical modeling of respiratory airflow transport.

The Time-Dependent Interfacial Adhesion between Artificial Rock and Fresh Mortar Modified by Nanoclay.

The time-dependent interfacial adhesion between rock and fresh mortar is key for printing concrete linings in mountain tunnels. However, a scientific deficit exists in the time-dependent evolution of the interfacial adhesion, which can cause adhesion failure when printing tunnel lining. Nanoclay has the potential to increase the interfacial adhesion and eliminate the adhesion failure. Before the actual printing of tunnel linings, the time-dependent interfacial adhesion between artificial rock and fresh mortar modified by nanoclay should be understood. This paper studied the time-dependent interfacial adhesion based on fast tack tests, fast shear tests, and isothermal calorimetry tests. With the addition of nanoclay, the maximum tensile stress and the maximum shear stress increased. Compared with a reference series, the maximum interfacial tensile stress in a 0.3% nanoclay series increased by 106% (resting time 1 min) and increased by 209% (resting time 32 min). A two-stage evolution of the interfacial adhesion was found with the addition of nanoclay. In the first stage, the time-dependent interfacial adhesion increased rapidly. A 0.3% NC series showed an increase rate six times higher than that of the reference series. As the matrices aged, the increase rate slowed down and followed a linear pattern of increase, still higher than that of the reference series. The stiffening of fresh matrices resulted in the interface failure mode transition from a ductile failure to a brittle failure. The effect of nanoclay on flocculation and on accelerating the hydration contributed to the time-dependent interfacial adhesion between artificial rock and fresh mortar.

Study on the Failure Process and Acoustic Emission Characteristics of Freeze–Thawed Sandstone under Cyclic Loading and Unloading

In order to investigate freeze–thawed red sandstone failure processes under cyclic loading and unloading conditions, real-time acoustic emission (AE) and scanning electron microscopy (SEM) techniques were used to reveal the fracture process of the saturated red sandstone after cyclic loading and unloading tests using uniaxial compression. The results show that the stress–strain curves of the freeze–thawed sandstones show signs of hysteresis and exhibit a two-stage evolution of “sparse → dense”. In the cyclic loading and unloading process, the modulus of elasticity in the loading process is always larger than that in the unloading process, while the Poisson’s ratio is the opposite, and the radial irreversible strain and cumulative irreversible strain are larger than those in the axial direction. As the number of freeze–thaw cycles increases, the rock specimens need more cycles of loading and unloading to make the crack volume compressive strain Δεcv+ reach the maximum value and tend to stabilize, while the crack volume extensional strain Δεcv− tends to decrease gradually. This study also shows that the growth phase of the cyclic loading and unloading process has more ringing counts and a shorter duration, while the slow degradation phase has more ringing counts with loading and less with unloading. In addition, the F-T cycle gradually changes the internal microcracks of the red sandstone from shear damage, which is dominated by shear cracks, to tensile damage, which is dominated by tensile cracks. This study’s findings contribute to our knowledge of the mechanical characteristics and sandstone’s degradation process following F-T treatment, and also serve as a guide for engineering stability analyses conducted in the presence of multiphysical field coupling.

Bunker Museology

After the Cold War ended in 1991, many former nuclear bunkers were discarded by the British state and left as field monuments. This article examines the two-stage evolution of bunker museology in Scotland, wherein these archaeological remains have been converted into museums, initially by private collector-enthusiasts and latterly by community groups. My case study of the Gairloch Museum in the Wester Ross documents the reincarnation of a Cold War monument as a community museum, illustrating the positive potential of transforming unofficial into official heritage with the participation of local people. The Gairloch Museum is considered alongside two other local bunker restoration projects in Edinburgh and Dundee, advocating the need for public bodies to give greater priority to the preservation of Cold War heritage in Scotland and the United Kingdom as a whole.

Fluvial deposits of the Patlanoaya Group in southern Mexico: the stratigraphic record of an Early Mississippian supradetachment basin formed along the margin of Gondwana during the closure of the Rheic Ocean

ABSTRACT The Rheic Ocean closure stratigraphic record in southern Mexico is largely exposed in the Paleozoic Acatlán Complex. One of the most distinctive characteristics of the Acatlán Complex is the presence of high-pressure rocks, interpreted as remnants of the Rheic Ocean and the adjacent continental margin of Gondwana, which were subducted and then exhumed to the surface. Despite its importance in reconstructing the dynamics of the westernmost Rheic Ocean closure, the timing and mechanisms of exhumation of these high-pressure rocks remain debated. Some authors have tentatively interpreted the late Paleozoic, fluvial to marine succession of the Patlanoaya Group in southern Mexico as the sedimentary response to the exhumation of high-pressure rocks of the Acatlán Complex. Therefore, these sedimentary rocks may be particularly helpful in understanding the timing and evolution of high-pressure rocks exhumation during the Rheic Ocean closure. In this work, the tectono-sedimentary evolution of the continental deposits of the Patlanoaya Group was reconstructed, integrating sedimentologic, petrographic, and U-Pb detrital geochronological data to test the different scenarios proposed for the exhumation of the Acatlán Complex high-pressure rocks. Our data suggest that the lower Patlanoaya Group was deposited in an Early Mississippian basin with a two-stage evolution: a phase characterized by debris flow deposits derived from small-scale reliefs composed of low-grade metamorphic rocks and a subsequent phase marked by an NNE-trending fluvial system deposits sourced from low-metamorphic grade rocks of a mylonitic zone. Therefore, the lower Patlanoaya Group deposition was controlled by a major NNE-trending mylonitic shear zone associated with the development of a supradetachment basin linked to the progressive exhumation of high-pressure rocks during an Early Mississippian major extensional event. This work highlights the importance of sedimentary deposits in reconstructing the late Paleozoic history of the Acatlán Complex during the Rheic Ocean closure and the final stage of the Pangea assembly.

Imaging and spectroscopic observations of a confined solar filament eruption with two-stage evolution

ABSTRACT Solar filament eruptions are often characterized by stepwise evolution due to the involvement of multiple mechanisms, such as magnetohydrodynamic instabilities and magnetic reconnection. In this article, we investigated a confined filament eruption with a distinct two-stage evolution by using the imaging and spectroscopic observations from the Interface Region Imaging Spectrograph and the Solar Dynamics Observatory. The eruption originated from a kinked filament thread that separated from an active region filament. In the first stage, the filament thread rose slowly and was obstructed due to flux pile-up in its front. This obstruction brought the filament thread into reconnection with a nearby loop-like structure, which enlarged the flux rope and changed its connectivity through the foot-point migration. The newly formed flux rope became more kink unstable and drove the rapid eruption in the second stage. It ascended into the upper atmosphere and initiated the reconnection with the overlying field. Finally, the flux rope was totally disintegrated, producing several solar jets along the overlying field. These observations demonstrate that the external reconnection between the flux rope and overlying field can destroy the flux rope, thus playing a crucial role in confining the solar eruptions.

Two-Stage Multiobjective Evolution Strategy for Constrained Multiobjective Optimization

For the past many years, several constrained multiobjective evolutionary algorithms (CMOEAs) have been designed for solving constrained multi-objective optimization problems (CMOPs). In these CMOEAs, some constraint-handling techniques (CHTs) were proposed to balance the convergence and constrained satisfaction, however, they still face some serious challenges, such as premature convergence to the local optimal region and labor-intensive tuning of parameters for a specific CMOP. Furthermore, most of the existing CHTs are derived by solving constrained single-objective optimization. The information hidden from the feasible non-dominated set (FNDS) has not been fully utilized. This study proposed a novel parameter-less constraint handling technique, which divides the entire population into three mutually exclusive subsets dynamically: FNDS, the subset dominated by FNDS, and the subset not dominated by FNDS. According to the proposed division of labor, it is not necessary to balance the convergence and constrained satisfaction in each subset. To avoid being entrapped in local optima, the proposed algorithm adopts a two-stage strategy to solve CMOPs. In the first stage, the proposed algorithm focuses solely on converging toward the unconstrained Pareto front without considering the constrained satisfaction. In the second stage, the FNDS constraint handling technique is adopted to guide the population converging toward constrained Pareto front effectively. The performance of the proposed algorithm was compared to that of nine state-of-the-art CMOEAs, and the comparison results show that the proposed algorithm performs significantly better on the CF, MW, and LIRCMOP test suites.

Differential Evolution Framework for Budget Optimization in Marketing Models with Saturation and Adstock Effects

Marketing has proven to be an important asset to every business, and the growth of digital platforms has made it even more crucial, which makes the use of tools to measure and guide advertising strategies almost mandatory. In this context, Marketing Mix Modeling (MMM) has been seen as a powerful tool for backing up this process. However, the sole use of MMM does not help with complex budget allocation decisions, and some of the most used solutions for this modeling lacks on effective or intuitive solutions for this kind of problem. That said, the subject of this article is the proposal of a budget-allocation method consisting of a two-stage differential evolution optimization algorithm that considers the carryover and saturation effects for media channels in the calculations. The model was evaluated on a counterfactual scenario, aiming at the improvement of the Return on Ad Spend over a real scenario of an open database and showed positive results with a significant hypothetical increase in the financial result of the given scenario, proving to be a viable tool for this use.

Magnetic multi-fabrics as tools for understanding ignimbrite emplacement processes: An example from late-Variscan Tharandter Wald Caldera, Bohemian Massif

Felsic collapse calderas pose a hazard to our society, thus detailed knowledge of collapse mechanisms and behavior of eruptive products could help us to better understand the active calderas. We present a model of the eruption and collapse of Late Carboniferous Tharandter Wald Caldera (Variscan Bohemian Massif) based on field mapping and a magnetic multi-fabric approach. We investigate intra-caldera rhyolitic ignimbrites using a combination of conventional in-phase and relatively new out-of-phase anisotropy of magnetic susceptibility, along with the anisotropy of anhysteretic remanent magnetization. A two-stage caldera evolution model is proposed. (1) The high-energetic and high-temperature ignimbrites filled the paleo-valleys to form a thick ignimbrite sheet with welded to rheomorphosed central portion. Concurrently, eruptions emptied the source magma chamber triggering the piston caldera collapse. (2) The subsidence caused a monoclinal ductile bending of the still-hot and ignimbrite sheet around the caldera rim towards its center. Caldera activity was terminated by the emplacement of subvolcanic ring and radial dikes associated with hydrothermal activity. The latter likely induced abundant magneto-mineralogical alteration and formed composite fabrics. Hence, the application of the multi-fabric approach may serve as a useful tool to extrapolate reliable geodynamic proxies from the magnetic fabric of old, eroded, or altered caldera ignimbrites.

Two-stage evolution of a bimodal reservoir: The case of Holocene lavas of the Lanín composite volcano, Southern Volcanic Zone, Chile

Lanín is a composite volcano (∼39.38°S; 71.30°W) located in the Central Southern Volcanic Zone of the Andes. It is the southernmost member of the NW-SE volcanic alignment with the Villarrica and Quetrupillán composite volcanoes. During the Holocene, Lanín volcano erupted many lava flows, which we classify into three compositional groups: BAT (basalt, andesite, and basaltic trachyandesite), intermediate, and trachyte samples. The main recognised phenocrysts in the Lanín lavas are plagioclase, clinopyroxene, orthopyroxene, Fe–Ti oxides, and apatite. These mineral phases allow us to constrain the pre-eruptive (from the magma reservoir) conditions: temperature (985–1089 °C), pressure (1.8–4.1 kbar; equivalent to 6.5–15 km depth), and oxygen fugacity patch (∼QFM+0.4). In addition, textural features combined with pre-eruptive conditions suggest the existence of a crystal mush within the reservoir, which developed in two stages. The first stage considers the emplacement, crystal mush development, and eruption related to a melt of BAT composition. The residual melt evolved from BAT to trachytic primarily through fractional crystallisation. The second stage involves an evolved trachytic melt in the reservoir and the intrusion of a new, more mafic magma (presumably of BAT composition), which generates heating of the trachytic residual melt and crystal mush disaggregation. The fractional crystallisation also gives rise to scarce products of intermediate composition. This observation is consistent with the chemical constituents plots (TAS and Harker) and thermodynamic modelling.

Transferable Adaptive Differential Evolution for Many-Task Optimization.

The evolutionary multitask optimization (EMTO) algorithm is a promising approach to solve many-task optimization problems (MaTOPs), in which similarity measurement and knowledge transfer (KT) are two key issues. Many existing EMTO algorithms estimate the similarity of population distribution to select a set of similar tasks and then perform KT by simply mixing individuals among the selected tasks. However, these methods may be less effective when the global optima of the tasks greatly differ from each other. Therefore, this article proposes to consider a new kind of similarity, namely, shift invariance, between tasks. The shift invariance is defined that the two tasks are similar after linear shift transformation on both the search space and the objective space. To identify and utilize the shift invariance between tasks, a two-stage transferable adaptive differential evolution (TRADE) algorithm is proposed. In the first evolution stage, a task representation strategy is proposed to represent each task by a vector that embeds the evolution information. Then, a task grouping strategy is proposed to group the similar (i.e., shift invariant) tasks into the same group while the dissimilar tasks into different groups. In the second evolution stage, a novel successful evolution experience transfer method is proposed to adaptively utilize the suitable parameters by transferring successful parameters among similar tasks within the same group. Comprehensive experiments are carried out on two representative MaTOP benchmarks with a total of 16 instances and a real-world application. The comparative results show that the proposed TRADE is superior to some state-of-the-art EMTO algorithms and single-task optimization algorithms.

A guided genetic programming with attribute node activation encoding for resource constrained project scheduling problem

The large-scale characteristic and complex logic between activities have made priority rules (PRs) are more favoured in actual project scheduling, resulting in the increasing attention of genetic programming (GP) with automatically generating more effective PRs. However, the limitations of encoding and numerous random search operators in existing GPs not only affect the effectiveness of evolved PRs, but also reduce their interpretability. This paper proposes a novel Hyper-Heuristic based Guided Genetic Programming with Attribute Node Activation Encoding for resource constrained project scheduling problem. Uniquely, the proposed method transforms existing single class feature activation encoding into attribute node activation encoding for independently controlling each attribute node, and develops an attribute importance calculation method based on the frequency of attribute occurrence and activation. Based on the importance of subtrees and attributes, four guided and two random local search operators are designed to obtain more characteristic PRs. In addition, a two-stage evolution framework that automatically switches stages through iteration number is constructed to achieve performance sampling and guided generation of PRs. Based on the PSPLIB benchmark, although with fewer attribute inputs, the proposed method can generate more effective PRs with significantly better results compared to 12 existing PRs and PRs evolved from the two latest GPs in all test subsets.

Formation and evolution of a loess sinkhole in the southern Chinese Loess Plateau

Sinkholes are formed by the collapse of the overlying strata or the subsidence of the ground from cracks or holes in the underlying strata, which are widely developed in the karst and loess regions over southern and northern China. These sinkholes have been explored intensively to infer their climatic and ecological significances, as well as the management of geological disasters. In this study we investigated the Sanyuan (SY) sinkhole in the southern Chinese Loess Plateau to understand how it was formed and evolved. Two 30-m cores were retrieved from the SY sinkhole and a detailed chronological framework was established by applying radionuclides dating for the top 1-m sediments as well as 14C dating for the two 30-m cores. These dating results show that SY sinkhole was formed 23.7 m below the sinkhole surface at ca. 6.0 kyr BP (before 1950 CE). Changes in lithology, sedimentation rate, magnetic susceptibility, lightness, and mean grain-size of these two cores reveal that the sinkhole has undergone two-stage evolution. The first stage was characterized by a relatively slow deposition of loess sediments during 6.0–1.9 kyr BP, and the second stage was a rapid accumulating period since 1.9 kyr BP likely affected by human activities. Our results suggest that SY sinkhole can well document the combined influence of climate change and human activities, offering an ideal archive to decipher the natural and anthropogenic imprints since the mid-Holocene.

Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping

With satellite quantity and quality development in recent years, remote sensing products in vast areas are becoming widely used in more and more fields. The acquisition of large regional images requires the scientific and efficient utilization of satellite resources through imaging satellite task planning technology. However, for imaging satellite task planning in a vast area, a large number of decision variables are introduced into the imaging satellite task planning model, making it difficult for existing optimization algorithms to obtain reliable solutions. This is because the search space of the solution increases the exponential growth with the increase in the number of decision variables, which causes the search performance of optimization algorithms to decrease significantly. This paper proposes a large-scale multi-objective optimization algorithm based on efficient competition learning and improved non-dominated sorting (ECL-INS-LMOA) to efficiently obtain satellite imaging schemes for large areas. ECL-INS-LMOA adopted the idea of two-stage evolution to meet the different needs in different evolutionary stages. In the early stage, the proposed efficient competitive learning particle update strategy (ECLUS) and the improved NSGA-II were run alternately. In the later stage, only the improved NSGA-II was run. The proposed ECLUS guarantees the rapid convergence of ECL-INS-LMOA in the early evolution by accelerating particle update, introducing flight time, and proposing a binary competitive swarm optimizer BCSO. The results of the simulation imaging experiments on five large areas with different scales of decision variables show that ECL-INS-LMOA can always obtain the imaging satellite mission planning scheme with the highest regional coverage and the lowest satellite resource consumption within the limited evaluation times. The experiments verify the excellent performance of ECL-INS-LMOA in solving vast area mapping planning problems.