TITLE:
A New Metaheuristic Optimizer Inspired by Shopping Behavior
AUTHORS:
Mohamed El-Dosuky, Athir Al-Bagmi, Ghaida Al-Muhaidib, Najla Al-Manna
KEYWORDS:
Metaheuristic, Exploitation, Exploration, Shopping Behavior, Optimization
JOURNAL NAME:
Journal of Computer and Communications,
Vol.14 No.9,
September
17,
2026
ABSTRACT: This paper introduces a new metaheuristic inspired by the differing shopping behavior of men and women, called the Shopping Metaheuristic. It models optimization as a collaborative process between two agents: a man, who focuses on intensification (exploitation), and a woman, who prioritizes diversification (exploration). The goal is to enhance the balance between global exploration and local exploitation, improving convergence speed and avoiding local optima. To achieve this, the metaheuristic employs various strategies, including random jumps for exploration, adaptive step sizes, social influence mechanisms, communication between agents, and periodic intensification. These techniques help maintain a balance between broad search and fine-tuning solutions. The paper analyses its convergence behavior and explores a case study in smart agriculture. Finally, its performance is compared with traditional methods like Artificial Bee Colony, Genetic Algorithm, and Particle Swarm Optimization, as well as modern approaches like Spotted Hyena Optimizer and Dragonfly Optimization. The key observation is that both the Shopping Metaheuristic and Spotted Hyena Optimizer achieved the highest accuracy (0.993), outperforming the Dragonfly Optimization and Salp Swarm Algorithm, with the latter showing the lowest accuracy.