A Scalable Parallel Coevolutionary Algorithm With Overlapping Cooperation for Large-Scale Network-Based Combinatorial Optimization

Many real-world combinatorial optimization problems are defined on networks, such as road networks and social networks, etc. Due to the connectivity nature of networks, decision variables in such problems are usually coupled with each other, and the variables are also closely related to the characteristics of the local subnetwork to which they belong. These features pose new challenges to the design of cooperative coevolutionary (CC) algorithms for the large-scale network-based optimization. To improve the scalability, efficiency, and effectiveness of CC, we propose a new approach called parallel cooperative coevolution with overlapping decomposition and local evaluation (CCOL) for large-scale network-based combinatorial optimization. First, CCOL devises the overlapping decomposition to divide a large-scale network-based problem into some overlapping subproblems with lower dimensions. Second, subproblems are optimized in parallel since they can be evaluated by defined local objectives without the context of other subproblems. Meanwhile, an overlapping cooperation strategy is employed to achieve consensus toward the global objective. Finally, as different subproblems may have different scales, a Huffman-tree-based resources assignment strategy is devised. This strategy is able to utilize computing resources in a better way and thus further improve the scalability of the algorithm. To better demonstrate the proposed CCOL, we implement a set-based particle swarm optimization CCOL (CCOL-SPSO) to solve the multidepot vehicle routing problem with time windows as an example. Experimental results in medium and large scale benchmark problems indicate that CCOL is efficient and promising.