This study proposes a metaheuristic algorithm for restaurant exploration and food search, which simulates the methods and strategies of human exploration and food search, aiming to comprehensively consider the multi-dimensional needs of users and provide personalized recommendation services. Two different recommendation algorithms are systematically compared: a metaheuristic algorithm for restaurant exploration and a simple scoring algorithm. The metaheuristic algorithm for restaurant exploration and food search assigns different weights to each dimension according to the user’s preferences (e.g., dishes, services, prices, atmosphere, etc.) to provide personalized recommendations. For example, restaurant A has a low overall score, but it is recommended as the best choice because it meets the user’s preferences for Chinese food and atmosphere. In contrast, a simple scoring algorithm makes recommendations based only on the overall score of the restaurant. For example, although restaurant B has a high score, it may not meet the user’s needs because its Italian cuisine does not meet the user’s preferences. Through comparative analysis, restaurant recommendations based on metaheuristic algorithms can more accurately meet the personalized needs of users, while simple scoring algorithms rely too much on the comprehensive score of restaurants and fail to fully consider the personalized needs of users. By exploring restaurants and finding delicious food through this meta-heuristic algorithm, we believe that we can help users find the most suitable options among massive amounts of information, whether it is a restaurant, product, service, or other personalized needs.