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Aiming point optimization refers to the selection of the aiming point of a missile so that the strike effect on the target is at or near the best. As an example, a modified genetic algorithm is used to study the aiming point optimization problem of a multi-bullet missile striking the same target with different damage effects. Combined with the target vulnerability of the substation, suitable damage indicators and corresponding types of missile weapons are selected to establish the optimal aiming point solution model. In order to solve the defects of traditional genetic algorithm, an experience pool is established to store the dominant individuals to avoid the loss of dominant individuals and to retain the individuals with different genotypes to enhance the population diversity and avoid premature convergence of the algorithm. After calculations, the algorithm can effectively solve the problem of multi-bullet hybrid aiming point optimization, and the overall optimization result is better than the traditional algorithm, and has good convergence, which is of high application value.

Different from the classical job shop scheduling, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) should deal with job sequence, machine assignment and worker assignment all together. In this paper, a knowledge-guided fruit fly optimisation algorithm (KGFOA) with a new encoding scheme is proposed to solve the DRCFJSP with makespan minimisation criterion. In the KGFOA, two types of permutation-based search operators are used to perform the smell-based search for job sequence and resource (machine and worker) assignment, respectively. To enhance the search capability, a knowledge-guided search stage is incorporated into the KGFOA with two new search operators particularly designed for adjusting the operation sequence and the resource assignment, respectively. Due to the combination of the knowledge-guided search and the smell-based search, global exploration and local exploitation can be balanced. Besides, the effect of parameter setting of the KGFOA is investigated and numerical tests are carried out using two sets of instances. The comparative results show that the KGFOA is more effective than the existing algorithms in solving the DRCFJSP. [ABSTRACT FROM PUBLISHER]

This article addresses the distributed two-stage assembly flow-shop scheduling problem (DTSAFSP) with makespan minimisation criterion. A mixed integer linear programming model is presented, and a competitive memetic algorithm (CMA) is proposed. When designing the CMA, a simple encoding scheme is proposed to represent the factory assignment and the job processing sequence; and a ring-based neighbourhood structure is designed for competition and information sharing. Moreover, some knowledge-based local search operators are developed to enhance the exploitation ability. The influence of parameter setting on the CMA is investigated using the analysis of variance method. Extensive computational tests and comparisons are carried out, which demonstrate the effectiveness of the proposed CMA in solving the DTSAFSP. [ABSTRACT FROM AUTHOR]

BACKGROUND Plantar pressure analysis is widely used in the study of knee osteoarthritis (KOA). The present study aimed to investigate the static and dynamic plantar pressure distribution in patients with different stages of unilateral KOA using the Footscan® platform system. MATERIAL AND METHODS We recruited 94 patients aged 61.75±7.23 years old with different stages of unilateral KOA for static and dynamic analysis using the Footscan® platform system. The static pressure (%) of the left, right, anterior, posterior, and the pelvic rotation (°) was assessed. The peak pressure (PP, kPa) was investigated in 10 areas of the foot: medial heel (MH), lateral heel (LH), midfoot (MF), first to fifth metatarsals (M1-M5), hallux (T1), and toes 2-5 (T2-5). The correlation between KOA stages and plantar pressure distributions was investigated. RESULTS The results revealed that static pressure on the unaffected side and pelvic rotation were positively correlated with KOA stages. In addition, there was a positive correlation between KOA stages and PP of M5, MF, and LH zones on the affected side and PP of M2, M3, and M4 zones on the unaffected side, and a negative correlation between KOA stages and PP of T1 and T2-5 zones on the affected side. CONCLUSIONS With the progression of KOA, static plantar pressure tends to distributed on the unaffected side, and the dynamic plantar pressure tends to be distributed laterally on both feet. The plantar pressure distributions in unilateral KOA patients are abnormal and are closely related to the severity of KOA.