For resource reutilization and environmental protection, remanufacturing gets more and more attention in many countries. Disassembly is a critical part of traditional manufacturing industry, but the traditional disassembly operation is mainly done by workers, which is low-efficiency. Now the use of robots can improve production efficiency a lot, which involves the problem of disassembly line balancing. Due to the constraints such as product complexity and precedence relationship between tasks, when the number of tasks increases, the combination scheme between tasks increases geometrically, and conventional algorithms are difficult to solve the problems, the Disassembly Line Balancing Problem (DLBP) is generally necessary to optimize multiple objectives. In this research, the author selects a variety of intelligent optimization algorithms to resolve the complex disassembly line balancing problem in different dimensional objective space. Four representative algorithms are selected from three angles to be compared through three performance indicators. It is concluded that these algorithms have different search capabilities for different specifications and objective space. Researchers should carefully select the algorithm according to the specific disassembly problem. The appropriate algorithm should be selected according to the scale of the disassembly line problem and the number of optimization objectives in actual production practice.

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