Hybrid genetic algorithm with multi-component population for the optimization of commercial gallery size from an evolutionary approach
Introduction: This research explores the sizing and location of new shopping malls in Soacha, Cundinamarca (2024–2025). It addresses computational challenges in multi-objective optimization (MOP) for retail planning by balancing economic, financial, and social criteria through advanced evolutionary architectures.
Problem: Traditional genetic algorithms in retail planning often exhibit premature convergence and low population diversity, hindering balanced outcomes in complex urban contexts such as Soacha.
Objective: To develop and validate a Hybrid Genetic Algorithm (HGA) with a multi-component architecture to optimize weighted-sum aggregate functions, improving convergence, diversity, and solution quality for commercial gallery siting and sizing.
Methodology: The HGA employs a population of 100 individuals divided into five synergistic components: elite, offspring, mutated, migrants, and anti-duplicates. Structured in two phases prioritizing social and financial-economic benefits, it uses adaptive operators and a stagnation-based stopping criterion, validated with real data from Soacha.
Results: The HGA outperformed standard algorithms, achieving an aggregate fitness of 0.832 (+5.2%) and convergence in 185 generations (−47.2%), while maintaining diversity above 95%. It generated seven balanced champion solutions for the districts of Soacha, optimizing trade-offs between social impact and financial return on investment (ROI).
Conclusion: The HGA provides an efficient framework for sustainable retail planning, offering decision-making flexibility through adjustable weights while overcoming traditional evolutionary limitations.
Originality: The study introduces a multi-component population architecture for retail MOP, integrating elitism, migration, and anti-duplicate mechanisms to ensure robustness in local planning contexts.
Limitations: The research is based on weighted-sum aggregation and desk-based validation, currently excluding ecological variables and participatory community feedback.
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