Evolution Strategy Research Articles

Far field reconstruction of antennas via single‐surface phaseless spherical near‐field scans: A novel approach based on dipole equivalence

AbstractA novel approach is proposed in this paper to reconstruct the far‐field radiation pattern from the phaseless electric field of an antenna scanned on a single near‐field sphere. It adopts the dipole equivalence approach to project the near‐field electric field into a spherically distributed array of electric dipoles. A term representing the error from the linearly correlated portion in the least‐square problem associated with the dipole equivalence, namely the linear correlation error, is introduced. It is demonstrated that by iterative search to minimize the linear correlation error using the covariance matrix adaptation evolution strategy, near‐field phase distributions can be found efficiently from the magnitude‐only near‐field, and the far‐field radiation pattern can be computed. Two representative case studies are given here to validate the proposed method. Results demonstrate good agreements between computations and simulations.

Global Marine Ranching Research: Progress and Trends through Bibliometric Analysis

With the increasing prominence of pollution issues in offshore waters, marine ranching plays a crucial role in restoring marine ecosystems and ensuring the sustainable use of fishery resources. This paper provides a comprehensive bibliometric analysis of global marine ranching research. The study indicates that policy is the primary driver of the industry's development. It also reveals that the low level of interconnectivity between China and other major developing countries, as well as cooperation between governmental and research institutions in the area of marine ranching. In light of these considerations, prospective strategies for the future evolution of marine ranching can be formulated with a view to optimising and enhancing the seedling production system, and to forecasting changes in marine ranching in conjunction with the application of modern computing techniques and the field of remote sensing.

CMA-ES with Learning Rate Adaptation

The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful methods for solving continuous black-box optimization problems. A practically useful aspect of CMA-ES is that it can be used without hyperparameter tuning. However, the hyperparameter settings still have a considerable impact on performance, especially for difficult tasks, such as solving multimodal or noisy problems. This study comprehensively explores the impact of learning rate on CMA-ES performance and demonstrates the necessity of a small learning rate by considering ordinary differential equations. Thereafter, it discusses the setting of an ideal learning rate. Based on these discussions, we develop a novel learning rate adaptation mechanism for CMA-ES that maintains a constant signal-to-noise ratio. Additionally, we investigate the behavior of CMA-ES with the proposed learning rate adaptation mechanism through numerical experiments and compare the results with those obtained for CMA-ES with a fixed learning rate and with population size adaptation. The results show that CMA-ES with the proposed learning rate adaptation works well for multimodal and/or noisy problems without extremely expensive learning rate tuning.

An Evolutionary Analysis of Enterprise Innovation Behavior Based on Organizational Routines

Routine innovation can help enterprises eliminate old routines that do not adapt to the environment, introduce new routines with high efficiency, and improve the adaptability of enterprises and the environment. Based on this, this paper constructs an evolutionary game model between enterprises and employees, explores the game relationship between enterprise innovation incentives and employee practice innovation, and uses simulation to analyze the influence of different parameters on the evolution path of both parties in the game. The results show that there are two kinds of evolutionary stability strategies, and the final result depends on the parameter values in the payoff matrix. The relationship between income and cost is the decisive factor affecting enterprises and employees' strategy choices. One effective measure to promote enterprise innovation is the third-party reward. Establishing reasonable revenue distribution mechanisms is conducive to improving employees' innovation initiatives. This paper contributes to the advancement of research in the field of routine innovation and broadens the use of organizational routines in corporate governance.

Dynamic airspace sectorization with machine learning enhanced workload prediction and clustering

Addressing the complexities of modern Air Traffic Management (ATM), this paper introduces a novel framework for dynamic airspace sectorization, tailored to enhance efficiency and safety in congested airspaces. Central to this framework is the WP-ConvLSTM model, an innovative deep learning approach equipped with attention mechanisms. This model excels in accurately predicting workload dynamics, a critical factor in managing air traffic flow. To implement sectorization, we adopt a constrained K-means clustering technique for spatial division, followed by a refinement process involving Support Vector Machine (SVM) algorithms for precise boundary generation. Further optimization of sector boundaries is achieved through an evolutionary algorithm, ensuring both flexibility and stability in airspace divisions. Our methodology was thoroughly evaluated using real-world data from one of the busiest airspaces, demonstrating significant improvements in workload prediction accuracy and airspace sector management. The findings highlight the model’s robustness in practical scenarios, offering a scalable solution for ATM challenges. We conclude with a recognition of the study’s limitations and propose avenues for future research to build upon our findings, particularly in enhancing real-time data integration and adapting to evolving air traffic patterns.

The cadDX operon contributes to cadmium resistance, oxidative stress resistance, and virulence in zoonotic streptococci

Mobile genetic elements (MGEs) enable bacteria to acquire novel genes and traits. However, the functions of cargo genes within MGEs remain poorly understood. The cadmium resistance operon cadDX is present in many gram-positive bacteria. Although cadDX has been reported to be involved in metal detoxification, its regulatory mechanisms and functions in bacterial pathogenesis are poorly understood. This study revealed that cadDX contributes to cadmium resistance, oxidative stress resistance, and virulence in Streptococcus suis, an important zoonotic pathogen in pigs and humans. CadX represses cadD expression by binding to the cadDX promoter. Notably, cadX responds to H2O2 stress through an additional promoter within the cadDX operon, mitigating the harmful effect of excessive cadD expression during oxidative stress. cadDX resides within an 11 K integrative and mobilizable element that can autonomously form circular structures. Moreover, cadDX is found in diverse MGEs, accounting for its widespread distribution across various bacteria, especially among pathogenic streptococci. Transferring cadDX into another zoonotic pathogen, Streptococcus agalactiae, results in similar phenotypes, including resistance to cadmium and oxidative stresses and increased virulence of S. agalactiae in mice. The new functions and regulatory mechanisms of cadDX shed light on the importance of the cadDX system in driving evolutionary adaptations and survival strategies across diverse gram-positive bacteria.

The tripartite evolutionary game of enterprises' green production strategy with government supervision and people participation

Government supervision and people participation play vital roles in enterprises' green production strategy, but few studies have considered the strategy choices behind government supervision and people's participation. This study contended that whether an enterprise adopts the green production strategy is influenced not only by its responsible attitude facing social responsibility but also by the following two factors, the intensity of government supervision and whether people choose to participate in supervision or not when facing the enterprise adopting the green production strategy or not. We constructed a tripartite evolutionary game model to explore the behavioral evolution rules and evolutionary stability strategies of government supervision, people participation, and enterprises' green production. Then we employed numerical simulation to analyze how various factors influence the strategy selection of the government, people, and enterprises. The results show that if the utility values of government supervision and people participation are greater than 0, the enterprises will adopt the green production strategy. The system stability is affected by a synergistic relationship between participation cost, reputation benefit, and government subsidies, and an incremental relationship between enterprises' green production benefit, government subsidies, people's reported bonus, and enterprises' green production cost. This study provides a new theoretical perspective for research on government supervision, people participation, and enterprises' green production strategy, and the results provide important references for improving the enterprises' green production and urban environmental management.

Better than linear strength scaling of multifunctional ceramic truss lattice materials

Driven by the goal of creating exceptionally strong and lightweight thermal insulators to enable the operation of a vacuum airship on Venus, conditions where ceramic truss lattice materials provide better than linear scaling of strength with respect to variations in relative density have been found. This enhanced scaling relationship is a consequence of the pressure sensitive shear strength of ceramic materials. A new Gibson-Ashby type scaling relationship is developed between strength and relative density. Elementary analysis is used to formulate theoretical limits for the compressive strength, minimum density, and minimum thermal conductivity for truss lattice materials subjected to hydrostatic pressure loads. Shape optimization using the covariance matrix adopted evolutionary strategy (CMA-ES) and highly resolved finite element models is conducted on silicon carbide Kelvin cells with variable cross-section axisymmetric struts considering three failure modes: buckling, tensile rupture, and shear failure. The optimized designs closely adhere to and validate the newly developed analytical scaling relationship with better than linear strength scaling. These optimized designs are found to withstand the extreme loading conditions on Venus while providing up to 43 kgm3 of buoyancy. The thermal conductivity of the optimized designs are computed and found to be less than 0.5 WmK, with one design outperforming silica aerogels at elevated temperature.

Investigation into the Production of Melanin from By-Products of Huangjiu Brewing.

Melanin is a high value bioproduct generated through the fermentation of Aureobasidium pullulans, playing a crucial role in various fields, including food, medicine, environmental protection, and materials science. However, its high production costs and low synthetic yields significantly limit its applications. Therefore, it is essential to identify high-yield strains, reduce production costs, and optimize fermentation strategies. In this study, a high melanin-yielding Aureobasidium pullulans 53LC7 was screened and identified, and the fermentation process was optimized based on melanin yield, color value, and pullulan yield. The results indicated that the melanin yield peaked at an initial pH of 6.0, temperature of 27 °C, fermentation time of 6.5 d, and inoculation quantity of 2.5%, achieving a melanin yield of 16.33 g/L. Subsequently, huangjiu lees, a byproduct of huangjiu production, was incorporated into the fermentation medium, resulting in a melanin yield of 5.91 g/L. This suggests that the Aureobasidium pullulans was not effectively utilizing huangjiu lees. To address this, we employed an adaptive evolution strategy, which increased the melanin yield to 8.72 g/L. The enhanced production was correlated with the expression of key genes, including FKS, PKS, and Cmr1. Finally, cellulase was utilized to convert the crude fibers in huangjiu lees, which were difficult to utilize, into usable substrates, while pullulanase was employed to minimize byproduct formation in the fermentation system, resulting in a melanin yield of 19.07 g/L. This study not only provides promising strains for further research but also offers valuable insights for resource production technologies.

An evolutionary game analysis of the prefabricated building supply side under dynamic government reward and punishment mechanisms

From the standpoint of the supply side of prefabricated buildings, an evolutionary game model between the government and developers is constructed under the static and dynamic reward and punishment mechanisms in order to promote the development of prefabricated buildings and realize the transformation and upgrading of the construction industry. The simulation study of the example is conducted, and the impact of policy cost, subsidy upper limit, and tax upper limit on the system’s evolutionary stability strategy is examined. The findings demonstrate that when the government chooses static subsidy and static tax policies, there is no evolutionary stable strategy in the game system. The flaws of the static reward and punishment system can be adequately compensated for by the dynamic subsidy and dynamic tax policy, leading to an evolutionary stable state. The likelihood of developers producing prefabricated structures is negatively connected with policy cost and subsidy upper limits and favorably correlated with tax upper limits, according to the dynamic reward and punishment mechanism. The findings of the study have significant implications for government policy that will encourage the widespread use of prefabricated structures in China.

Stochastic configuration networks for short-term power load forecasting

The accuracy of power load forecasting can enhance the operational efficacy and economic efficiency of the power system. This paper utilizes the stochastic configuration network (SCN) and improved marine predator algorithm (IMPA) for power systems short-term load forecasting. First, the daily load data is clustered using the kernel fuzzy C-means (KFCM), and the clustering results are utilized as training input data for the load forecasting model. Then, load forecasting is performed using SCN. Next, IMPA is developed by integrating the opposite-based differential evolution (ODE) and normal mutation perturbation strategy for the purpose of preventing the algorithm from converging to local minima. Finally, IMPA is employed to optimize the critical parameters of the load forecasting model. The experimental results clearly demonstrate that the proposed algorithm performs favorably in terms of the prediction accuracy compared to other modeling algorithms.

(Amoralny) familizm: propozycja rekonceptualizacji z wykorzystaniem psychologii ewolucyjnej i międzykulturowej

In his 1958 book Moral Basis of a Backward Society, Edward Banfield introduced the term amoral familism to describe social relations and values in a poor rural community in southern Italy. In my analysis, I propose to abandon the value-laden epithet "amoral" and consider familism as an evolutionary adaptive strategy activated in times of scarcity or crises. I base the thesis of the evolutionary roots of familism on the inclusive fitness theory (Hamilton 1964) and the theory of kin and reciprocal altruism (Trivers 1971). The perspective of evolutionary psychology makes it possible to see the positive functions of familism and to propose a new biological-cultural understanding of familism. Familism has so far been diagnosed primarily "indirectly" in sociological studies of bonding (and bridging) capital and in research on social trust conducted as part of cyclical social surveys such as the World Values Survey and the European Social Survey. In the search for accurate measures of biological-cultural familism we can additionally turn to the research of cross-cultural psychologists conducted for international management. I argue that the dimension of particularism on the 7-dimensional cultural map of Fons Trompenaars and Charles Hampden-Turner (2002) and the dimension of in-group collectivism on the 9-dimensional cultural map of the GLOBE project (House et al. 2004), can valuably complement the measures used in sociological sciences and make the diagnosis of familism more reliable. Finally, I call for in-depth research on Polish familism aimed at testing whether it is possible to build bridging capital on the basis of familist bonding capital.

Competitive swarm optimizer with dynamic multi-competitions and convergence accelerator for large-scale optimization problems

Large-scale optimizations (LSOPs) with high dimensional decision variables have become one of the most challenging problems in engineering optimization. High dimensional information causes serious interference to the algorithm optimization performance. The optimization performance of the algorithms will be seriously degraded. Competitive swarm optimizer (CSO) is a robust algorithm to tackle LSOPs. However, CSO randomly selects two particles to compare, then generates the winner and the loser. Although this search mechanism can enhance the diversity of the swarm, a single comparison is difficult to guarantee the quality of winners and losers. Therefore, there exists a risk of producing unqualified solutions. In order to enhance the quality of solution, a novel CSO with dynamic multi-competitions and convergence accelerator, namely DMCACSO, is designed in this paper. In the DMCACSO, a dynamic multi-competitions based evolutionary information is designed to pick out the losers more efficiently and improve the quality of winners. In addition, a convergence accelerator with hybrid evolutionary strategy is developed to speed up the particle search when the algorithm is a state of stagnation. The experiment results in solving large-scale benchmark functions from CEC2010 and CEC2013 indicate that the DMCACSO has competitive optimization performance by comparing with some state-of-the-art algorithms. Finally, the DMCACSO is effective in terms of quality in solving an actual feature selection problem.

Hybrid motility mechanism of sperm at viscoelastic fluid-solid interface

To fertilize eggs, sperm must pass through narrow, complex channels filled with viscoelastic fluids in the female reproductive tract. While it is known that the topography of the surfaces plays a role in guiding sperm movement, sperm have been thought of as swimmers, i.e., their motility comes solely from sperm interaction with the surrounding fluid, and therefore, the surfaces have no direct role in the motility mechanism itself. Here, we examined the role of solid surfaces in the movement of sperm in a highly viscoelastic medium. By visualizing the flagellum interaction with surfaces in a microfluidic device, we found that the flagellum stays close to the surface while the kinetic friction between the flagellum and the surface is in the direction of sperm movement, providing thrust. Additionally, the flow field generated by sperm suggests slippage between the viscoelastic fluid and the solid surface, deviating from the no-slip boundary typically used in standard fluid dynamics models. These observations point to hybrid motility mechanisms in sperm involving direct flagellum-surface interaction in addition to flagellum pushing the fluid. This finding signifies an evolutionary strategy of mammalian sperm crucial for their efficient migration through narrow, mucus-filled passages of the female reproductive tract.

Discussion on the Aesthetic Evolution and Optimization Strategies of Neo-Ming Style Chair Design

This paper explores the balanced development of Neo-Ming style chair design in both traditional and modern contexts, focusing on its design evolution, aesthetic characteristics, and future optimization strategies. By analyzing the cultural background, aesthetic form, material craftsmanship, and structural functions of Neo-Ming style chair, the paper highlights the importance of maintaining a fusion between traditional aesthetics and modern demands in contemporary design. It proposes future optimization directions such as personalization trends, cross-disciplinary integration, and the application of intelligent technology, aiming to provide theoretical support and practical references for the design innovation of Neo-Ming style chair.

Urban community security system model from the perspective of resilience: a study on Chinese cities

To enhance urban governance and bolster community disaster resilience, it is urgent to innovate and develop an urban community security system to cope with the complex and volatile social security situation. From the perspective of resilience, the concept, layout, and structure of the security system are organized. Considering the current situation and development trend of the social security system, and based on the constructed cost-capability model, the cost and benefit of the two modes are analyzed and compared. Subsequently, from the perspective of multi-agent participation, a game model between governments and enterprises is constructed. The evolutionary stability strategies of enterprises are analyzed, and simulation analysis is conducted on the factors influencing enterprises. Finally, to propose targeted improvement measures based on the community security capabilities of different communities, a grey fuzzy comprehensive evaluation method is employed. The community security comprehensive capabilities of the top 10 cities in China are quantitatively analyzed and evaluated. The results indicate that the urban community security system from a resilience perspective is more comprehensive compared to the traditional security system. Adopting mode 2 is relatively effective to improving government emergency management capability. In a certain range, reducing C, increasing F, reducing R2, and increasing h can achieve the effect of promoting enterprise to participate in construction. The urban community security capability is within the upper-medium level in general, close to the level of good. The urban community security system constructed in this paper can provide a theoretical basis for the government to formulate policies of the urban community security system and serve as a reference for strengthening the comprehensive capability of the urban community security.

Directed evolution and metabolic engineering generate an Escherichia coli cell factory for de novo production of 4-hydroxymandelate

4-hydroxymandelate is a high-value aromatic compound used in the medicine, cosmetics, food, and chemical industry. However, existing natural extraction and chemical synthesis methods are costly and lead to environmental pollution. This study employed metabolic engineering and directed evolution strategies for de novo 4-hydroxymandelate biosynthesis. Two key challenges were addressed: insufficient precursor supply and limited activity of crucial enzymes. Through gene overexpression and multi-level gene interference using CRISPRi, An Escherichia coli chassis capable of producing the key precursor 4-hydroxyphenylpyruvate and the titer reached 5.05 mM (0.91 g/L). A mutant clone was obtained, HmaSV152G, which showed a 5.13-fold improvement in the catalytic rate. During fermentation, a high production of 194.87 mM (32.768 g/L) 4-hydroxymandelate was achieved in 76 h with a batch supply of glucose in a 5-L bioreactor. This study demonstrated the great potential of biosensors in protein engineering and provides a reference for large-scale production of other high-value aromatic compounds.

Driving factors on corporate green investments behaviors: From the strategic intersection of governments regulation and public participation

As the global community confronts the challenges of climate change, businesses face increasing pressure to adopt sustainable practices. This study develops a tripartite game model to investigate the impact of green investments on corporate performance, considering the dynamic interplay between governments regulations and public participation in shaping strategic initiatives. First, the evolutionary stability strategy (ESS) is identified by solving replicator dynamic equations and performing stability analysis of equilibrium points. Next, the practicability and rationality of the evolutionary game model are assessed by analyzing ESSs under various corporate green investment scenarios. Finally, a case-based example is provided to validate the theoretical findings and support the following arguments: there are eight equilibrium points and four potential ESSs in the game model; the selection of each ESS is primarily determined by the trade-off between costs and revenues for each stakeholder; increased governmental regulatory costs prompt a strategic shift, incentivizing corporations to enhance green investments; while rising penalties drive a preference for green options; and corporations recognizing compensatory responsibilities are steered towards sustainable pathways.

Coordination of Renewable Energy Integration and Peak Shaving through Evolutionary Game Theory

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid.

Research on low-carbon supply chain emission reduction strategies based on blockchain technology

The difficulty of regulating carbon trading due to information asymmetry and low consumer trust in low-carbon products are key factors hindering companies from reducing emissions. This paper examines a manufacturer-led secondary low-carbon supply chain consisting of a single supplier and a retailer, focusing on the impact of blockchain technology on carbon transaction costs and consumers' low-carbon preferences. Utilizing Stackelberg game theory, the paper constructs a supply chain decision model for emission reduction, determining the payment matrix and analyzing the stable strategy for blockchain adoption through evolutionary game theory. The findings indicate that retailers' adoption of blockchain technology significantly promotes emission reduction within the supply chain, whereas manufacturers' adoption has minimal impact. Additionally, the study reveals that variations in blockchain adoption costs and carbon quotas result in multiple evolutionary stable strategies. Specifically, when blockchain adoption costs and carbon quotas are below certain thresholds, the system reaches a unique equilibrium where both parties adopt blockchain technology.