Evolutionary Computation with Spatial Receding Horizon Control to Minimize Network Coding Resources

Abstract

The minimization of network coding resources, such as coding nodes and links, is a challenging task, not only because it is a NP-hard problem, but also because the problem scale is huge; for example, networks in real world may have thousands or even millions of nodes and links. Genetic algorithms (GAs) have a good potential of resolving NP-hard problems like the network coding problem (NCP), but as a population-based algorithm, serious scalability and applicability problems are often confronted when GAs are applied to large- or huge-scale systems. Inspired by the temporal receding horizon control in control engineering, this paper proposes a novel spatial receding horizon control (SRHC) strategy as a network partitioning technology, and then designs an efficient GA to tackle the NCP. Traditional network partitioning methods can be viewed as a special case of the proposed SRHC, that is, one-step-wide SRHC, whilst the method in this paper is a generalized N-step-wide SRHC, which can make a better use of global information of network topologies. Besides the SRHC strategy, some useful designs are also reported in this paper. The advantages of the proposed SRHC and GA for the NCP are illustrated by extensive experiments, and they have a good potential of being extended to other large-scale complex problems.

A Modified Ant Colony Optimization Algorithm for Network Coding Resource Minimization

“…To be specific, each link is associated with a coefficient which represents how the information is combined according to the combination of flows from the upstream links. Hu et al invented this encoding approach and adapt several GAs, e.g., the ripple-spreading GA (RSGA) [36] and the spatial receding horizon control GA (SRHCGA) [37], for the problem in large-scale or complex networks. Meanwhile, a chemical reaction optimization (CRO) algorithm was studied for addressing the problem, with the operating principle inspired from chemical reactions [38].…”

“…We evaluate the overall performance of NCRM-ACO by comparing it with the eleven state-of-the-art EAs, including 5 BLS-based (BLSGA [10], QEA1 [27], QEA2 [26], PBIL [28] and cGA [30]), 2 BTS-based (BTSGA [10] and FA-ENCA [34]), 3 relative-encoding-based (RGA [35], SRHCGA [37] and CRO [38]), and 1 path-oriented (pEA [32]). The algorithms for performance comparison are listed as follows.…”

“…For BTSGA, we have p c =0.8 and p m = 0.012. For the rest of the algorithms, we adopt their best parameter settings [26], [27], [29], [30], [32], [34], [35], [37], [38] . For the fixed, random and real-world networks, the stopping criteria is either an optimal solution is obtained or the maximum number of generations is reached.…”

Genetic algorithm with spatial receding horizon control for the optimization of facility locations

“…A major cause of losing the global optimality is the independent/isolated way of resolving each sub-problem. Inspired by the temporal receding horizon control (TRHC) strategy in the area of control engineering [14], [15], we have recently proposed a novel spatial receding horizon control (SRHC) strategy to partition large-scale network coding problems in [16]. In the SRHC problem partitioning strategy, a large-scale problem is divided into many sub-problems, which compose a problem space, a spatial horizon is then defined which covers some sub-problems each time and will recede in the problem space.…”

“…This paper particularly attempts to apply the SRHC strategy proposed in [16], and develops an effective GA to resolve the FLOP. As a new application study, the FLOP in this paper will further demonstrate the practicability of the SRHC in [16].…”