Multiobjective Hybrid Evolutionary Multitasking Algorithm for PCB Assembly Optimization in Beam-Head Placement Machines

Operational efficiency of placement machines constrains the overall production capacity of printed circuit board (PCB) assembly lines. Existing state-of-the-art algorithms face challenges, such as conflicts between multiple objectives and coupling within different problems. This article proposes a multiobjective hybrid evolutionary multitasking algorithm (MOHEMTA) to address PCB assembly optimization in beam-head placement machines. The algorithm divides the problem into pickup and placement tasks, leveraging implicit parallelism to enhance solution efficiency. A nozzle block encoding method and heuristic decoding strategies with domain knowledge are introduced to reduce encoding complexity and accelerate algorithm convergence. MOHEMTA enhances offspring population diversity and quality through an elitist strategy, evolutionary operators, and knowledge transfer mechanisms, while incorporating safeguards against negative transfer. Experiments demonstrate that the multiobjective solution performance and practical results of MOHEMTA are better than those of other state-of-the-art algorithms.