The performance of swarm intelligent algorithms in solving large-scale permutation and combinatorial optimization problems is influenced by the large search space, so a Solution Space Dynamic Compression (SSDC) strategy was proposed to cut down the search space of the algorithms dynamically. In the proposed strategy, two times of initial solutions of the the permutation and combination optimization problem were obtained by the intelligent algorithm firstly. Then the repetitive segments of the two solutions were recognized and merged together. And the new points after merging were taken into the original solution space to perform the compression and update of the solution space. In the next intelligent algorithm solving process, the search was carried out in the compressed feasible space, so as to improve the searching ability of the individuals in the limited space and reduce the searching time cost. Based on five high-dimensional benchmark Travel Salesmen Problems (TSP) and two Vehicle Routing Problems (VRP), the performances of several swarm intelligent algorithms combined with the solution space dynamic compression strategy were tested. The results show that the swarm intelligent algorithms combined with the proposed strategy are superior to the corresponding original algorithms in the search accuracy and stability. It is proved that the solution space dynamic compression strategy can effectively improve the performance of swarm algorithms.

Particle Swarm Optimization (PSO) algorithm is easy to be premature and drop into the local optimum and cannot jump out when solving complex multimodal functions. Related researches show that changing the topological structure among particles and adjusting the updating mechanism are helpful to improve the diversity of the population and the optimization ability of the algorithm. Therefore, a Hybrid PSO with Topological time-varying and Search disturbance (HPSO-TS) was proposed. In the algorithm, a K-medoids clustering algorithm was adapted to cluster the particle swarm dynamically for forming several heterogeneous subgroups, so as to facilitate the information flow among the particles in the subgroups. In the speed updating, by adding the guide of the optimal particle of the swarm and introducing the disturbance of nonlinear changing extreme, the particles were able to search more areas. Then, the transformation probability of the Flower Pollination Algorithm (FPA) was introduced into the position updating process, so the particles were able to transform their states between the global search and the local search. In the global search, a lioness foraging mechanism in the lion swarm optimization algorithm was introduced to update the positions of the particles; while in the local search, a sinusoidal disturbance factor was applied to help particles jump out of the local optimum. The experimental results show that the proposed algorithm is superior to FPA, PSO, Improved PSO (IPSO) algorithm and PSO algorithm with Topology (PSO-T) in the accuracy and robustness. With the increase of testing dimension and times, these advantages are more and more obvious. The topological time-varying strategy and search disturbance mechanism introduced by this algorithm can effectively improve the diversity of population and the activity of particles, so as to improve the optimization ability.