Evolution Strategy Research Articles

Evolutionary stability of developmental commitment

Evolution of unicellular to multicellular organisms must resolve conflicts in reproductive interests between individual cells and the group. The social amoeba Dictyostelium discoideum is a soil-living eukaryote with facultative sociality. While cells grow in the presence of nutrients, cells aggregate under starvation to form fruiting bodies containing spores and altruistic stalk cells. Once cells socially committed, they complete formation of fruiting bodies, even if a new source of nutrients becomes available. The persistence of this social commitment raises questions as it inhibits individual cells from swiftly returning to solitary growth. I hypothesize that traits enabling premature de-commitment are hindered from being selected. Recent work has revealed outcomes of the premature de-commitment through forced refeeding; The de-committed cells take an altruistic prestalk-like position due to their reduced cohesiveness through interactions with socially committed cells. I constructed an evolutionary model assuming their division of labor. The results revealed a valley in the fitness landscape that prevented invasion of de-committing mutants, indicating evolutionary stability of the social commitment. The findings provide a general scheme that maintains multicellularity by evolving a specific division of labor, in which less cohesive individuals become altruists.

Meta-model structural monitoring with cutting-edge AAE-VMD fusion alongside optimized machine learning methods

Transfer learning significantly enhances machine learning by leveraging knowledge from one dataset to boost performance on another, which is particularly beneficial when labelled data ares limited or costly. Sensor fusion, an essential technique in structural health monitoring, improves the effectiveness of transfer learning by combining data from multiple sensors to extract more informative features. This article proposes a novel meta-model structural monitoring using a new data fusion method named the adversarial autoencoder (AAE)–variational mode decomposition (VMD) algorithm, which integrates optimization algorithms, transfer learning, and machine learning techniques for damage detection tasks. The proposed meta-model combines transfer learning with an optimized machine learning framework for training and employs pretrained models for testing across diverse datasets. First, the tensors are fused using the AAE approach into 300 data points, reducing noise and outliers, performing dimensionality reduction, and enriching the training and test datasets. Then, a preprocessing step involves decomposing and denoising tensors using the VMD algorithm, followed by selecting the most informative intrinsic mode functions (IMFs) based on their variance. These selected IMFs are concatenated, and 13 statistical features are extracted from them and used as inputs for machine learning models. Various optimization algorithms are employed to fine-tune the hyperparameters of the machine learning models for optimal classification results. Validation of this meta-model utilizes the dataset collected from a steel grandstand structure, available in benchmark dataset format. For decomposed fused tensors, both particle swarm optimization and covariance matrix adaptation evolution strategy emerge as equally effective optimization techniques for K-nearest neighbours (KNN), consistently achieving the highest mean test accuracy and F1 score of 99.5%. Conversely, KNN stands out as a robust and reliable classification algorithm for denoised fused tensors in transfer learning classification problems, consistently demonstrating perfect accuracy and an F1 score of 1.0 ± 0.0 across all optimization techniques.

Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context.

Surrogate modeling has become a valuable technique for black-box optimization tasks with expensive evaluation of the objective function. In this paper, we investigate the relationships between the predictive accuracy of surrogate models, their settings, and features of the black-box function landscape during evolutionary optimization by the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) state-of-the-art optimizer for expensive continuous black-box tasks. This study aims to establish the foundation for specific rules and automated methods for selecting and tuning surrogate models by exploring relationships between landscape features and model errors, focusing on the behavior of a specific model within each generation in contrast to selecting a specific algorithm at the outset. We perform a feature analysis process, identifying a significant number of non-robust features and clustering similar landscape features, resulting in the selection of 14 features out of 384, varying with input data selection methods. Our analysis explores the error dependencies of four models across 39 settings, utilizing three methods for input data selection, drawn from surrogate-assisted CMA-ES runs on noiseless benchmarks within the Comparing Continuous Optimizers framework.

Analysis of low-carbon technology transfer strategies based on a quadrilateral evolutionary game

Promoting low-carbon technology transfer is crucial for reducing global greenhouse gas emissions, combating climate change, and fostering sustainable development. Based on the “efficient market” and “active government” concepts, a “government-intermediary-receiver-sender” game model was constructed, identifying evolutionary stable strategies and key factors affecting strategy selection. It was found that (1) receiver and sender needs and government policy incentives increase intermediaries' willingness to participate, reducing input costs and shifting government support from incentives to compensatory regulations; (2) optimal strategies emerge when incentives for low-carbon technology senders, receivers, and intermediaries, and penalties for serious defaults by senders and receivers exceed a threshold, fostering high participation willingness and leading to government implementation of compensatory regulations; (3) differentiated penalties are necessary to avoid a sender-receiver prisoner's dilemma but only ensure one side's willingness to participate, while government incentive-based regulation can overcome negative constraints and promote active participation from both sender and receiver. Compensatory regulations encourage sustained technology transfer costs and reduce incentive costs, with optimization focusing on lowering government publicity costs and increasing economic benefits for intermediaries.

Unlocking the potential: advancements and future horizons in ROR1-targeted cancer therapies.

While receptor tyrosine kinase-like orphan receptor 1 (ROR1) is typically expressed at low levels or absent in normal tissues, its expression is notably elevated in various malignant tumors and conditions, including chronic lymphocytic leukemia (CLL), breast cancer, ovarian cancer, melanoma, and lung adenocarcinoma. This distinctive feature positions ROR1 as an attractive target for tumor-specific treatments. Currently, several targeted drugs directed at ROR1 are undergoing clinical development, including monoclonal antibodies, antibody-drug conjugates (ADCs), and chimeric antigen receptor T-cell therapy (CAR-T). Additionally, there are four small molecule inhibitors designed to bind to ROR1, presenting promising avenues for the development of PROTAC degraders targeting ROR1. This review offers updated insights into ROR1's structural and functional characteristics, embryonic development implications, cell survival signaling pathways, and evolutionary targeting strategies, all of which have the potential to advance the treatment of malignant tumors.

Multi-AUVs cooperative path planning in 3D underwater terrain and vortex environments based on improved multi-objective particle swarm optimization algorithm

This paper presents an improved multi-objective particle swarm optimization (IMOPSO) algorithm for cooperative path planning of multiple autonomous underwater vehicles (multi-AUVs) in 3D underwater terrain and vortex environment. Traditional PSO algorithms often struggle with local optima in high-dimensional, complex search spaces. To address this, we propose enhancements including Sine double-sequence perturbation interpolation for initial particle population generation, adaptive differential evolution strategies, iterative local search, and traction operations to enhance global search capability and avoid local optima. Additionally, cubic spline interpolation is employed to smooth the planned paths. The objective function is tailored to the ocean currents environment, considering path length, smoothness, deflection angle, and current impact, with a penalty function method to handle multi-AUVs cooperation and environmental constraints optimization for optimal energy consumption. Simulation results demonstrate that the proposed algorithm effectively plans short-range, high-security global paths for multi-AUVs, verifying its effectiveness and practicality in meeting the navigation requirements in ocean currents environments.

Uncovering the genomic basis of symbiotic interactions and niche adaptations in freshwater picocyanobacteria

BackgroundPicocyanobacteria from the genera Prochlorococcus, Synechococcus, and Cyanobium are the most widespread photosynthetic organisms in aquatic ecosystems. However, their freshwater populations remain poorly explored, due to uneven and insufficient sampling across diverse inland waterbodies.ResultsIn this study, we present 170 high-quality genomes of freshwater picocyanobacteria from non-axenic cultures collected across Central Europe. In addition, we recovered 33 genomes of their potential symbiotic partners affiliated with four genera, Pseudomonas, Mesorhizobium, Acidovorax, and Hydrogenophaga. The genomic basis of symbiotic interactions involved heterotrophs benefiting from picocyanobacteria-derived nutrients while providing detoxification of ROS. The global abundance patterns of picocyanobacteria revealed ecologically significant ecotypes, associated with trophic status, temperature, and pH as key environmental factors. The adaptation of picocyanobacteria in (hyper-)eutrophic waterbodies could be attributed to their colonial lifestyles and CRISPR-Cas systems. The prevailing CRISPR-Cas subtypes in picocyanobacteria were I-G and I-E, which appear to have been acquired through horizontal gene transfer from other bacterial phyla.ConclusionsOur findings provide novel insights into the population diversity, ecology, and evolutionary strategies of the most widespread photoautotrophs within freshwater ecosystems.62j11owDS1FAqTqkQkf7rCVideo

A novel evolutionary strategy optimization algorithm for reliability redundancy allocation problem with heterogeneous components

The reliability-redundancy allocation problem (RRAP) is an optimization problem that maximizes system reliability under some constraints. In most studies on the RRAP, either active redundant components or cold standby components are used in a subsystem. This paper presents a new model for the RRAP of a system with a mixed redundancy strategy, in which all components can be heterogeneous. This formulation leads to a more precise solution for the problem; however, RRAP is an np-hard problem, and the new mixed heterogeneous model will be more complicated to solve. After formulating the issue, a novel design of an evolutionary strategy optimization algorithm is proposed to solve that. The problem consists of discrete and continuous variables, and different mutation strategies are designed for each. The new formulation of the problem and the new method for solving it lead to better results than those reported in other recent papers. We implement the new suggested heterogeneous model with the PSO and SPSO algorithms to better compare the proposed algorithm. Results show improvement in both system reliability and fitness evaluation count.

Tripartite evolutionary game and simulation analysis of electric bus charging facility sharing under the governmental reward and punishment mechanism

Electric vehicles (EVs) are environmentally friendly with the features of low noise levels and zero tailpipe emissions. However, limited charging facilities have restricted the large-scale development of EVs, and electric bus charging facility sharing is a promising solution to this issue. While reducing charging time and improving facility utilization, this measure involves the interests of the government, bus operators, and electric car owners, requiring a systemic analysis of their interactions. Therefore, this study constructs a tripartite evolutionary game model under the governmental reward and punishment mechanism. The model examines the strategic decision-making process, influential factors, and evolutionary stability of three players, followed by simulation analysis. The results show that: (1) The evolutionary system may exhibit three stable states: (0,0,0), (1,0,0) and (1,1,1), and enhance the initial ratio of the three stakeholders will help evolve towards the ideal state. (2) Increasing rewards and penalties can both be meaningful, when penalties exceed rewards, the measures can be more effective. (3) Bus operators are more sensitive to incentive and penalty measures than electric car owners, and governments should pay more attention to them in the implementation process. The findings can provide references for the policy formulation on electric bus charging facility sharing.

Low-Carbon Supply Chain Decision-Making and CSR Strategy Evolution Analysis Considering Heterogeneous Consumer Preferences

Decision-making regarding the low-carbon supply chain, considering corporate social responsibility (CSR) and the heterogeneous preferences of consumers, has become an urgent topic to be explored. This paper explores the decision-making problem of a low-carbon supply chain considering the heterogeneous preferences of consumers under different CSR situations, analyzes the influence of important parameters on each equilibrium solution, compares the size relationship of each equilibrium solution under different CSR situations, and verifies the conclusions obtained through numerical simulation. Then, based on the obtained equilibrium solution, a CSR evolutionary game model of the low-carbon supply chain is constructed, and the evolutionary stability strategies of the two sides on the CSR game are solved. Finally, the evolutionary trajectory of the game system is intuitively presented using a simulation method, and the influence of the main parameters on the evolutionary trends of the two sides is analyzed. The findings are as follows: (1) When both manufacturers and retailers undertake CSR, the retail price and wholesale price are their lowest, while carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility are the highest. (2) When a company undertakes CSR, carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility all increase with the increase in the CSR degree of the company and the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers. (3) The evolutionary stability strategy for both manufacturers and retailers is to undertake CSR. In addition, the initial proportion of manufacturers and retailers that undertake CSR, the low-carbon preference of low-carbon consumers, and the increase in the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers can encourage both members of the supply chain to undertake CSR.

A New Student Performance Prediction Method Based on Belief Rule Base with Automated Construction

Student performance prediction (SPP) is a pivotal task in educational analytics, enabling proactive interventions and optimized resource allocation by educators. Traditional SPP models are often hindered by their complexity and lack of interpretability. This study introduces a novel SPP framework, the Belief Rule Base with automated construction (Auto–BRB), designed to address these issues. Firstly, reference values are derived through data mining techniques. The model employs an IF–THEN rule-based system integrated with evidential reasoning to ensure both transparency and interpretability. Secondly, parameter optimization is achieved using the Projected Covariance Matrix Adaptive Evolution Strategy (P–CMA–ES), significantly enhancing model accuracy. Moreover, the Akaike Information Criterion (AIC) is then applied to fine-tune the balance between model accuracy and complexity. Finally, case studies on SPP have shown that the Auto–BRB model has an advantage over traditional models in terms of accuracy, while maintaining good interpretability. Therefore, Auto–BRB has excellent application effects in educational data analysis.

Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks.

Modular algorithm frameworks not only allow for combinations never tested in manually selected algorithm portfolios, but they also provide a structured approach to assess which algorithmic ideas are crucial for the observed performance of algorithms. In this study, we propose a methodology for analyzing the impact of the different modules on the overall performance. We consider modular frameworks for two widely used families of derivative-free black-box optimization algorithms, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and differential evolution (DE). More specifically, we use performance data of 324 modCMA-ES and 576 modDE algorithm variants (with each variant corresponding to a specific configuration of modules) obtained on the 24 BBOB problems for 6 different runtime budgets in 2 dimensions. Our analysis of these data reveals that the impact of individual modules on overall algorithm performance varies significantly. Notably, among the examined modules, the elitism module in CMA-ES and the linear population size reduction module in DE exhibit the most significant impact on performance. Furthermore, our exploratory data analysis of problem landscape data suggests that the most relevant landscape features remain consistent regardless of the configuration of individual modules, but the influence that these features have on regression accuracy varies. In addition, we apply classifiers that exploit feature importance with respect to the trained models for performance prediction and performance data, to predict the modular configurations of CMA-ES and DE algorithm variants. The results show that the predicted configurations do not exhibit a statistically significant difference in performance compared to the true configurations, with the percentage varying depending on the setup (from 49.1% to 95.5% for mod-CMA and 21.7% to 77.1% for DE).

Effects of climatic and time‐related variables on dung beetle communities: A case study in Central Spain

AbstractDung beetles are considered a key element in ecosystems as they are involved in many ecological processes, being one of the main decomposers of organic matter in the landscape. They can be classified into 3 subfamilies: Aphodiinae, Geotrupinae and Scarabaeinae, with each subfamily exhibiting specific adaptations and evolutionary strategies that have developed over time. The global patterns of dung beetle diversity are influenced by ecological factors, such as climatic (temperature, humidity, atmospheric pressure) and time‐related variables. Thus, the aim of the study is to see how these variables affect a dung beetle community and whether there are different responses among the subfamilies. The study was carried out in Mataelpino, a town located in Central Spain (Madrid, Spain). Monthly sampling was conducted from May 2018 to February 2020. According to the results the variables considered exert an effect on dung beetles, with differences observed among the subfamilies. To our knowledge, atmospheric pressure has been considered for the first time in a study of this type, with a greater effect being observed in the Aphodiinae subfamily than in the rest of the dung beetle groups. Regarding the other subfamilies, it has been observed that temperature is a determining factor for Scarabaeinae species, whereas humidity seems to have a greater effect on Geotrupinae species.

Positive selection analyses identify a single WWE domain residue that shapes ZAP into a more potent restriction factor against alphaviruses.

The host interferon pathway upregulates intrinsic restriction factors in response to viral infection. Many of them block a diverse range of viruses, suggesting that their antiviral functions might have been shaped by multiple viral families during evolution. Host-virus conflicts have led to the rapid adaptation of host and viral proteins at their interaction hotspots. Hence, we can use evolutionary genetic analyses to elucidate antiviral mechanisms and domain functions of restriction factors. Zinc finger antiviral protein (ZAP) is a restriction factor against RNA viruses such as alphaviruses, in addition to other RNA, retro-, and DNA viruses, yet its precise antiviral mechanism is not fully characterized. Previously, an analysis of 13 primate ZAP orthologs identified three positively selected residues in the poly(ADP-ribose) polymerase-like domain. However, selective pressure from ancient alphaviruses and others likely drove ZAP adaptation in a wider representation of mammals. We performed positive selection analyses in 261 mammalian ZAP using more robust methods with complementary strengths and identified seven positively selected sites in all domains of the protein. We generated ZAP inducible cell lines in which the positively selected residues of ZAP are mutated and tested their effects on alphavirus replication and known ZAP activities. Interestingly, the mutant in the second WWE domain of ZAP (N658A) is dramatically better than wild-type ZAP at blocking replication of Sindbis virus and other ZAP-sensitive alphaviruses due to enhanced viral translation inhibition. The N658A mutant is adjacent to the previously reported poly(ADP-ribose) (PAR) binding pocket, but surprisingly has reduced binding to PAR. In summary, the second WWE domain is critical for engineering a more potent ZAP and fluctuations in PAR binding modulate ZAP antiviral activity. Our study has the potential to unravel the role of ADP-ribosylation in the host innate immune defense and viral evolutionary strategies that antagonize this post-translational modification.

Evolutionary Strategy Dynamics (ESD): A Conceptual Framework for the Analysis of Dynamic Strategy

Evolutionary Strategy Dynamics (ESD): A Conceptual Framework for the Analysis of Dynamic Strategy

Combining physical modeling and machine learning for micro-scale modeling of a fuel cell electrode

Abstract Microscale modeling plays a critical role in fuel cell development, offering deep insights into the microscale transport phenomena and electrochemical reactions. This level of detail is essential for optimizing the performance of a single fuel cell, enabling the precise design and improvement of materials and structures at the microscale and consequently enhancing the overall efficiency of a stack. Here, we show a comprehensive transition from white-box models, characterized by their reliance on physical laws, to black-box models exemplified by neural networks, which excel in pattern recognition from provided data without necessitating a clear understanding of the underlying processes. This spectrum encompasses the inherent challenges and merits of both methodologies. While white-box models are recognized for their reliability due to their foundation in mathematical equations that describe physical phenomena, they often require the integration of empirical parameters and are susceptible to experimental errors, much like their black-box counterparts. The core novelty in this study lies in the synergistic integration of these two paradigms, specifically tailored for enhancing the predictive accuracy in solid oxide fuel cell modeling. In this approach, the neural network is employed to replace different parts of the mathematical model, from refining empirical parameters in the electrochemical model to replacing the entire electrochemical model. The adjustment of parameters is conducted by an evolutionary strategy based on the outputs of the mathematical model. The results underscore the superiority of the gray box in achieving higher prediction accuracy and in minimizing the requisite data volume for network training. This presented approach not only bridges the gap between the deterministic clarity of white-box models and the data-driven insights of black-box models but also strategically distributes the computational load between them, thereby offering a promising solution to the prevalent challenges in solid oxide fuel cell modeling.

Simultaneous improvement of fructophilicity and ethanol tolerance of Saccharomyces cerevisiae strains through a single Adaptive Laboratory Evolution Strategy

Saccharomyces cerevisiae is the main yeast used in the winemaking industry. Its innate glucophilicity provokes a discrepancy in glucose and fructose consumption during alcoholic fermentation of grape must, which, combined with the inhibitory effect of ethanol accumulated in the fermentation broth, might lead to stuck or sluggish fermentations. In the present study, we realized an Adaptive Laboratory Evolution strategy, where an alcoholic fermentation of a 20 g/L fructose broth was followed by cell selection in a high ethanol concentration environment, employed in two different S. cerevisiae strains named CFB and BLR. The evolved populations originated from each strain after 100 generations of evolution exhibited diverse fermentative abilities. One evolved population, originated from CFB strain, fermented a synthetic broth of 100 g/L glucose and 100 g/L fructose to dryness in 170 h, whereas the parental strain did not complete the fermentation even after 1000 h of incubation. The parameters of growth of the parental and evolved populations of the present study, as well as of the ethanol tolerant populations acquired in a previous study, when grown in a synthetic broth of 100 g/L glucose and 100 g/L fructose, were calculated through a kinetic model, and were compared to each other in order to identify the effect of evolution on the biochemical behavior of the strains. Finally, in a 200 g/L fructose synthetic broth fermentation, only the evolved population derived from CFB strain showed improved fermentative behavior than its parental strain.

Comparative genome wise analysis of codon usage of Staphylococcus Genus.

The genus Staphylococcus encompasses a diverse array of bacteria with significant implications for human health, including disreputable pathogens such as Staphylococcus aureus and Staphylococcus epidermidis. Understanding the genetic composition and codon usage patterns of Staphylococcus species is crucial for unraveling their evolutionary dynamics, adaptive strategies, and pathogenic potential. In this study, we conducted a comprehensive analysis of codon usage patterns across 48 species within the Staphylococcus genus. Our findings uncovered variations in genomic G-C content across Staphylococcus species, impacting codon usage preferences, with a notable preference for A/T-rich codons observed in pathogenic strains. This preference for A/T-rich codons suggests an energy-saving strategy in pathogenic organisms. Analysis of dinucleotide pair expression patterns unveiled insights into genomic dynamics, with overrepresented codon pairs reflecting trends in dinucleotide expression across genomes. Additionally, a significant correlation between CAI and genomic G-C content underscored the intricate relationship between codon usage patterns and gene expression strategies. Amino acid usage analysis highlighted preferences for energetically cheaper amino acids, suggesting adaptive strategies promoting energy efficiency. This comprehensive analysis sheds light on the evolutionary dynamics and adaptive mechanisms employed by Staphylococcus species, providing valuable insights into their pathogenic potential and clinical implications. Understanding these genomic features is crucial for devising strategies to combat staphylococcal infections and improve public health outcomes.

Optimizing [formula omitted]-coverage in energy-saving wireless sensor networks based on the Elite Global Growth Optimizer

For energy-constrained wireless sensor networks, enhancing network fault tolerance and extending lifetime are of paramount importance. In this study, we focus on addressing the Minimum Power k-Coverage (MinPKC) problem. The objective of MinPKC is to minimize and balance the transmission (Tx) power by sensor nodes (SNs), thus extending network lifetime. Furthermore, MinPKC requires satisfying the constraints of k-coverage for the targets to enhance network fault tolerance. The problem is non-convex, multimodal, and NP-hard. To address this problem, we propose a novel metaheuristic algorithm named Elite Global Growth Optimizer (EGGO), which incorporates efficient global search and elite evolution strategies into the GO and utilizes time-varying multi-strategies for population updates. This study extensively compares EGGO with ten popular algorithms on 20 classic benchmark test functions using the Friedman and Wilcoxon rank-sum tests. The results indicate that EGGO significantly outperforms all the compared algorithms. Ablative experiments confirm the crucial role of the proposed mechanisms in EGGO for improving algorithm performance. Meanwhile, the study implements ten MinPKC methods based on comparative algorithms and compares them with the EGGO-based MinPKC method in terms of total Tx power, network lifetime, and simulation time. The experimental results indicate that the EGGO-based MinPKC method significantly increases the network lifetime by approximately 14.2%, 38.2%, and 1.9% compared to the second-best algorithm, and by 57%, 451%, and 84.8% compared to the GO, while balancing the Tx power among SNs and reducing the total Tx power by approximately 28%, 12.7%, and 0.5% compared to the second-best algorithm, and by 47.1%, 72%, and 30% compared to the GO, respectively, when k=1,2,3. Additionally, a detailed analysis is conducted on the impact of the number of SNs, the maximum target coverage capacity of each SN, as well as the effects of obstacles and their distribution on signal strength attenuation in reducing Tx power. The source code is available at https://github.com/iNet-WZU/EGGO.

Evolutionary Game Analysis of Government–Enterprise Collaboration in Coping with Natech Risks

The synergistic interaction between emergency management departments and enterprises constitutes a fundamental mechanism for mitigating the risks of technological accidents caused by natural disasters (Natech). The efficacy of this collaborative approach is impacted by comprehensive risk analyses and the game between government and enterprise. Predicated on these premises, the evolutionary game analysis of government–enterprise collaboration in coping with Natech risk was carried out. Firstly, an evolutionary game model of government–enterprise collaboration in coping with Natech risk was constructed. Secondly, the evolutionary stability strategy (ESS) was developed. Finally, these strategies were substantiated through numerical simulations. The findings revealed that at lower levels of Natech risk, enterprises will choose low investment in coping capacity building, and emergency management departments will choose delayed disposal. Under moderate Natech risk, enterprises will increase their investments if emergency management departments persist with delayed strategies, and emergency management departments will react proactively if enterprises persist with low investment. Under a high Natech risk, a pattern of strategic misalignment emerges between the two entities. This study contributes a theoretical basis for the optimization of government–enterprise collaboration in coping with Natech risks.