A novel hybrid immune clonal selection algorithm for the constrained corridor allocation problem

Aiming at the lack of relevant research on relationship constraints between facilities in the corridor allocation problem (CAP). In this paper, fixed position constraints and ordering constraints are considered in CAP, and the logistics cost is minimized. Considering that the existing search technology is complicated and time-consuming in dealing with such constrained CAP (cCAP), and immune clone selection algorithm with variable neighborhood operation (ICSAVNS) is provided for solving this problem. Two approaches to initial solution generation are designed to improve the quality of the initial population. A variable neighborhood search operator is embedded to improve the accuracy of the local search. A threshold is set in the mutation operation of the ICSAVNS to achieve population expansion better. A double index of sequences consisting of affinity values and constrained facility index values is used to select and reselect, achieving population compression in the clonal selection part. Finally, by exactly solving the model, the rationality of the model is verified. The hybrid clone selection algorithm is used to solve the cCAP and cbCAP benchmark instances of different sizes, and compared with the state-of-the-art optimization algorithms. The results show that the proposed algorithm exhibits better performance.

Automated summary

This paper addresses the lack of research on relationship constraints between facilities in the corridor allocation problem (CAP). It proposes an immune clone selection algorithm with variable neighborhood operation (ICSAVNS) to solve this problem. The algorithm improves the quality of initial solutions, accuracy of local search, and achieves population compression through the use of a double index sequence. Experimental results demonstrate the superior performance of the proposed algorithm in solving this problem.