Enhanced Chameleon Swarm Algorithm Based on Differential Strategies for Solving the Uncapacitated Facility Location Problem

The Uncapacitated Facility Location Problem (UFLP) is a well-known NP-hard combinatorial optimization problem. In real-world applications, the profitability of many industries is closely linked to geographic location, making facility location a problem of significant practical value. Evolutionary algorithms, with their self-organizing, self-learning, and selfadaptive characteristics, have shown strong competitiveness in solving such complex problems. To efficiently solve the UFLP, this paper proposes an Enhanced Discrete Chameleon Swarm Algorithm (enDCSA). First, the population is initialized randomly. Then, the continuous evolutionary algorithm is discretized using a taper-shaped transfer function. Next, a differential evolution (DE) strategy is incorporated into the prey-searching phase of the Chameleon Swarm Algorithm (CSA) to enhance its exploration and exploitation capabilities. Furthermore, a redundancychecking strategy is employed to optimize facilities location decisions and enhance the quality of the obtained solutions. Finally, comparative experiments and visualizations are conducted to demonstrate the effectiveness and feasibility of the proposed enDCSA in solving the UFLP.