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Faced with the problems of low production efficiency and unreasonable industrial organization exist in the development of agriculture, this paper studied the multi-objective optimization of sustainable agricultural industrial structure based on genetic algorithm in order to optimize the allocation of agricultural industrial structure. First of all, this paper expounded the current agricultural development background, and then put forward the research status of multi-objective optimization algorithm, based on which points out the advantages of genetic algorithm. Then, establish the model system that is based on the optimal allocation of industrial structure. Use the improved genetic algorithm to solve the problem, and modify the operator design to obtain the optimal algorithm for the sustainable agricultural optimization sequence. The results of the algorithm evaluation show that the optimized industrial structure benefit value and sustainability both have been improved. [ABSTRACT FROM AUTHOR]

In the context of global climate change and increasing population, transitioning to resource-efficient, environmentally friendly agricultural technologies is essential for sustainability. In this context, the present study is anchored in the theories of information asymmetry and bounded rationality. It constructs a tripartite evolutionary game model under market mechanisms and government guidance, facing the challenges of promoting green agricultural technologies among multiple actors. This model explores the strategic choices and dynamic evolution of these actors under economic incentives and risk perception. The objective of the research is to reveal the dynamic evolutionary trends of stakeholder behavior under different policy intervention conditions and how governments can propel the promotion of green agricultural production technologies through policy instruments. To encompass the impact of historical decisions on the evolutionary process of green technology dissemination, this study employs a Delay Differential Equations (DDE) algorithm that considers historical time lags in its numerical simulations. Through stability analysis and DDE numerical simulations, the research identifies factors influencing the promotion of green agricultural production technologies. The findings indicate that under market mechanisms, there is a risk of "market failure" in the dissemination model of green agricultural technologies. Government participation can provide external incentive support, effectively enhancing the stability and efficiency of the evolutionary game system for promoting green technologies. Simulation results further suggest that government intervention can significantly improve the stability and efficiency of technology promotion. Economies of scale, initial willingness, risk perception, and subsidy intensity are decisive factors in technology adoption. Consequently, policymakers are advised to formulate precise and multifaceted subsidy strategies based on the varying needs of different actors, to stimulate engagement, reduce risk, and encourage widespread adoption of green technologies. This study lays a theoretical groundwork for smallholders and family farms to overcome technology adoption barriers, crucial for sustainable agricultural development. It provides a framework for understanding and addressing these challenges, guiding policymakers in crafting effective green agriculture policies to improve farming efficiency and sustainability. [Display omitted] • This research investigates the impact of policy interventions on the adoption of green agricultural technologies. • We employ a tripartite game model and the DDE algorithm to analyze strategy evolution. • The study demonstrates that government involvement enhances the promotion of green technologies. • Implement a variety of subsidies to promote green technologies and support sustainable agriculture. [ABSTRACT FROM AUTHOR]