Urban projects planning by multi-objective ant colony optimization algorithm

This paper presents a mathematical framework for algorithmic governance within the context of the Real Estate and Transferable Development Rights (TDR) Exchange of India, addressing the challenges of urban infrastructure development. The model incorporates key components such as dynamic pricing, compliance enforcement, and optimal resource allocation, which are designed to regulate TDR transactions efficiently, transparently, and sustainably. By developing a dynamic pricing mechanism that adapts to market demand, supply, compliance with regulations, and environmental sustainability factors, this framework ensures that TDRs are allocated to areas with the highest redevelopment potential.The framework integrates regulatory enforcement mechanisms that monitor adherence to urban planning regulations and impose dynamic penalties on non-compliance. The pricing model adjusts based on the demand-supply gap and local environmental constraints, encouraging sustainable urban growth. The paper also explores a multi-objective optimization approach to TDR allocation, ensuring that redevelopment potential is maximized while maintaining environmental thresholds. The proposed algorithmic model provides a scalable solution to urban infrastructure challenges, promoting balanced growth and sustainable resource utilization in Indian cities.

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Urban projects planning by multi-objective ant colony optimization algorithm

Cited by 2 publications

References 13 publications

Multi-objective Ant Colony Optimization: An Updated Review of Approaches and Applications

Multi-objective Ant Colony Optimization: An Updated Review of Approaches and Applications

Algorithmic Governance in the Real Estate and TDR Exchange of India: A Statistical Framework for Regulating Urban Infrastructure Development

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