Abstract
Assembly sequence optimization aims to find the optimal or near-optimal assembly sequences under multiple assembly constraints. Since it is NP-hard for complex assemblies, the heuristic algorithms are widely used to find the optimal or near-optimal assembly sequences in an acceptable computation time. Considering the multiple assembly constraints, an assembly model is presented for assembly sequence optimization. Then, the hybrid symbiotic organisms search and ant colony optimization is used to find the optimal or near-optimal assembly sequences. The symbiotic organisms search has a relatively strong global optimization capability but weak local optimization capability. On the other hand, the ant colony optimization has the relatively strong local optimization capability for assembly sequence optimization even though the parameters are not optimized. The hybrid symbiotic organisms search and ant colony optimization take advantages of their capacities for assembly sequence optimization. The case study demonstrates that the hybrid symbiotic organisms search and ant colony optimization finds the better assembly sequences within less iteration than the individual ant colony optimization and symbiotic organisms search in most experiments under the same preconditions.
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