This paper proposes a Quantum-Inspired wolf pack algorithm (QWPA) based on quantum encoding to enhance the performance of the wolf pack algorithm (WPA) to solve the 0-1 knapsack problems. There are two important operations in QWPA: quantum rotation and quantum collapse. The first step enables the population to move to the global optima and the second step helps to avoid the trapping of individuals into local optima. Ten classical and four high-dimensional knapsack problems are employed to test the proposed algorithm, and the results are compared with other typical algorithms. The statistical results demonstrate the effectiveness and global search capability for knapsack problems, especially for high-level cases.

Mechanical properties, such as residual stress, micro-hardness and fatigue performance, of the Ti-5Al-4Mo-4Cr-2Sn-2Zr titanium alloy were improved via the laser peening without coating (LPwC) with a water-penetrable wavelength of 532 nm and pulse duration of 10 ns. In this paper, three kinds of laser energy, namely 85, 110 and 160 mJ were used to process the samples. The titanium alloy samples were also peened with different impact times (1, 3 or 5 impacts) at the energy of 85 mJ. The micro-hardness and residual stress distribution results provided that LPwC can introduce compressive residual stress (CRS) and also induce hardening of the target materials. Further, micro-hardness and CRS showed the increasing trends when the laser impact times increased. However, the CRS and micro-hardness decreased while the laser energy increased from 110 to 160 mJ, which was attributed to the dynamic equilibrium between the thermal and mechanical effects of LPwC. High cycle fatigue strength of the titanium alloy was significantly improved from 360 to 490.3 MPa after three impacts LPwC. The strengthening mechanism of fatigue strength subjected to LPwC was a combined effect between the laser-induced CRS and the high-density dislocations.

Flower pollination algorithm is a new type of heuristic algorithm, which uses Lévy random walk as the key element for high efficiency of global searching. In order to explore the search performance of pollination algorithms under different random walk models, three random walk models are taken into account, including levy random walk model used by original flower pollination algorithm and two new random walk models based on McCulloch algorithm introduced in this paper. The analysis of searching performance and adaptive of Flower Pollination Algorithm with three random walks from two aspects of the model structure and the numerical simulation is given. The result shows that Cauchy random has a great competitive advantage for the low dimensional searching problem, and the Gauss random is more suitable for dealing with the multi-dimension unimodal case, while the Lévy random is able to provide better performance of solving the multi-dimension multimodal case. Simulation results and analysis will have a significant impact on the design of the randomness mechanism of the meta-heuristics algorithm and the improvement of Classical algorithms.