Market Game Optimization Algorithm: A Metaheuristic Inspired by Symmetric Competitive Behavior of Merchants and Consumers

This paper introduces a market-inspired meta-heuristic named the Market Game Optimization Algorithm (MGOA), which models the symmetric competitive interactions between merchants and customers. MGOA simulates the dual dynamics of attraction and collaboration among merchants and customers, integrating a global search strategy that reflects how merchants attract customers. The optimization process includes two distinct stages, each employed with a 50% probability through a merchant cooperation mechanism designed to balance exploration and exploitation. Prior to performance evaluation, a qualitative analysis and parameter sensitivity study were conducted to clarify parameter selection and optimization characteristics. The algorithm was tested on 42 benchmark functions from the CEC2017 and CEC2022 test suites and compared against 10 well-established and 10 recently proposed meta-heuristic algorithms. Results indicate that MGOA achieves the highest overall ranking. Furthermore, its application to 11 real-world engineering design problems demonstrates high effectiveness and solution quality. All experimental findings confirm that MGOA is a robust and competitive meta-heuristic capable of solving complex real-world optimization problems.