<![CDATA[Supermarket location selection is a critical challenge in strategic planning for modern retail businesses. Incorrect location decisions can lead to low customer visits and high operational costs. This study aims to determine the optimal supermarket location based on six key criteria: population, income, accessibility, distance to competitors, land rental costs, and traffic flow. To address this issue, this study implements the Penguin Search Optimization Algorithm (PeSOA), a metaheuristic inspired by the group hunting behavior of penguins in search of the best resources. A dataset of 50 location alternatives was processed using Python, and each criterion was normalized using the min-max method to standardize the scoring scale. The results show that the best location is at index 33 with a maximum fitness score of 4.3669. PeSOA achieved optimal convergence within 50 out of 100 iterations. The main advantage of PeSOA lies in its ability to explore solution space effectively with minimal parameters and deliver stable outcomes. This study confirms that PeSOA is an effective decision-support tool for retail location planning. These findings can be used as a foundation for developing intelligent algorithm-based decision support systems in the retail sector.]]>
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