Author(s)

MingYu Wang^*, ZiHeng Ji

Affiliation(s)

School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034, Liaoning, China.

Corresponding Author

MingYu Wang

ABSTRACT

This study presents an innovative computational framework addressing the complex optimization challenges in historic urban renewal through block-by-block relocation strategies. This study develop a hybrid simulated annealing-genetic algorithm (SA-GA) that synergistically combines the global search capability of genetic algorithms with simulated annealing's local optima avoidance mechanism. The model incorporates three critical optimization objectives: (1) maximization of contiguous cleared blocks (demonstrating 28.3% improvement), (2) minimization of resident displacement (achieving 19.7% reduction), and (3) preservation of neighborhood spatial integrity. This study’s comprehensive compensation scheme accounts for multiple architectural factors including housing orientation, unit area differentials, spatial configuration metrics, daylight access preservation, and structural renovation requirements. Computational experiments reveal the SA-GA hybrid's superior performance, showing 24.1% better solution quality and 17.3% faster convergence compared to conventional methods. The framework features: A cost-benefit analysis module identifying optimal ROI thresholds, Resident satisfaction metrics (85.2% acceptance rate in simulations)Implementation cost optimization(23.4% savings potential), Decision-support software implementation  This research contributes both theoretically and practically by: Establishing the first application of SA-GA in urban building relocation, Developing quantifiable fairness assessment metrics, Providing actionable tools for large-scale renewal projects.

KEYWORDS

Urban renewal; Building relocation optimization; Hybrid metaheuristics; Multi-objective decision making; Computational urban planning; SA-GA algorithm

CITE THIS PAPER

MingYu Wang, ZiHeng Ji. Optimizing block-by-block relocation in historic urban renewal: a hybrid simulated annealing-genetic algorithm approach. Journal of Computer Science and Electrical Engineering. 2025, 7(5): 59-64. DOI: https://doi.org/10.61784/jcsee3078.

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