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The multiple knapsack problem（MKP） is a special knapsack problem with great difficulty. In order to solve MKP by differential evolution（DE）, this paper first established the integer programming model of MKP. Based on a simple and efficient new transfer function based on modulo operation, it proposed a novel discrete differential evolution algorithm MODDE. Then, the method used an efficient algorithm GROA to eliminate the unfeasible solution of MKP by greedy strategy. Therefrom, this paper proposed a new method for solving MKP based on MODDE. Finally, it used MODDE to solve 30 international instances of MKP. Comparing with 4 representative evolution algorithms shows that MODDE not only has better calculation results; but also has stronger stability. It is indeed an efficient algorithm for solving MKP [ABSTRACT FROM AUTHOR]

In order to provide decision support for vehicle scheduling of logistics companies, this paper investigated the routing problem with time windows and dynamic demands considering a mixed fleet of electric and conventional vehicles, and proposed a two-stage integer programming model to minimize the total distribution cost. This paper designed an improved adaptive large-scale neighborhood search algorithm(IALNS), proposed the new deletion and repair operators and acceleration strategy in the dynamic stages. It conducted the extensive large-scale computational experiments with both static and dynamic demands to examine the performance of proposed IALNS. The results show that, compared to IALNS-ND, IALNS performs better in term of the minimum and average values in 75% of the static problems ( 9 out of 12 cases) . In 95% (57 out of 60 examples) of the dynamic cases, IALNS works better than IALNS-ND in terms of the cost and computation time . Moreover, compared to ALNS, LNS and VNS, IALNS performs best in term of the best minimum and average values of the total costs for all static cases. In 58% (35 out of 60 examples) of the dynamic case, the IALNS can achieve a better solution in 1.5 times or even 10 times less computation time than the rest algorithms. Also the larger the degree of dynamism of a experiment is, the better the obtained solution obtained by IALNS in a shorter time. Thus IALNS performs best in solving the time-sensitive dynamic demand vehicle routing problem. [ABSTRACT FROM AUTHOR]

Cluster assignment is a compiler optimization that plays an important role in performance of programs. However, the existing algorithms only work on straight-line program area, and is difficult to be optimal. Aiming at these problems, this paper proposed a unified cluster assignment and instruction scheduling method based on integer linear programming. This method used zero-one decision variables to represent cluster assignment, local instruction scheduling and global scheduling of inter-cluster transfer instructions, and formulated dependency and processor resources competition between instructions into linear constraints, eventually got an integer linear programming model whose objective is to minimize the estimated execution time of the function. Experimental results show that the cluster assignment and scheduling scheme from solving the model significantly outperforms the existing algorithms on accelerating programs, and the time required to solve the model is acceptable. [ABSTRACT FROM AUTHOR]

The uncertainty factors operation scheduling in the lead to inability to obtain accurate time of operation and ends under emergency rescue situation. In order to solve the problem, this paper proposed a grey scheduling model and a hybrid grey cuckoo algorithm. The model used three parameters interval grey number and the four parameters interval grey number to describe the uncertain time of operation and the uncertain ends. Then, it defined the possibility measure and necessity measure, proposed the index of delay confidence, which was used to estimate the probability of operation tardiness. After that, this paper established a gray mixed integer programming model with minimize average credibility of operation tardiness, and presented a solution method of the mixed grey cuckoo search algorithm. A classic example of size 6 ( 3) X 3 verified the proposed algorithm. The results show that the proposed algorithm can solve the problem very well, and the performance is better than the basic cuckoo algorithm. [ABSTRACT FROM AUTHOR]

For the software-defined networking environment, the existing service function chain deployment method failed to fully consider the resource utilization of the entire network, this paper proposed a service chain optimal deployment mechanism based on the efficient heuristic algorithm. Firstly, this mechanism gave the overall structure of the service chaining deployment, and introduced an integer linear programming model. Secondly, this paper proposed an efficient heuristic algorithm, which ran in first sort after greed manner and effectively used network resources and balanced load under the constraints of resource and delay. The obtained simulation results show that compared to other deployment algorithms, the proposed algorithm reduces the load balancing and time complexity while improving the request acceptance rate. [ABSTRACT FROM AUTHOR]

While conducting bandwidth allocation over elastic optical data center network EODCN, it is important to balance the revenue of cloud service providers and the connection performance of tenants. This paper proposed a bandwidth allocation algorithms to effectively assign optical bandwidth for multiple connection requests based on subcarriers so as to maximize revenue. Firstly, on the basis of a revenue model and a concept of virtual subcarrier, it constructed an integer linear programming formulation of the studied bandwidth allocation problem. Then, it proposed a dynamic programming-based algorithm and a heuristics-based algorithm correspondingly, which respectively focus on achieving maximum net revenue and gaining better time efficiency. Finally, it conducted simulations to validate the efficiency of the designed algorithms. The simulation results show that both of the proposed algorithms are superior to the existed methods in system revenue obtaining in bandwidth allocation over EODCN. [ABSTRACT FROM AUTHOR]

This paper evaluated the integral properties of BORON cryptography algorithm with SPN structure and Khudra cryptography algorithm with Feistel structure. It estahlished MILP model based on bit-based division according to the structure of linear layer and nonlinear layer. It generated the corresponding objective function according to the number of target rounds in the final search, and utilized the optimizer of Gurobi to solve the model and searched the distinguisher. Finally, it obtained the 6-round integral distinguisher of BORON and the 7-round integral distinguisher of Khudra, which were the longest distinguishers at present. By using these distinguishers, more rounds of assessment of integral properties can be carried out [ABSTRACT FROM AUTHOR]

For the parallel machine scheduling with controllable processing times( PMS-CPT), this paper proposed a scheduling problem for optimizing tardiness and energy cost. First, this article developed an integer linear programming( ILP) model for the proposed scheduling problem in order to facilitate the CPLEX solver. Second, it established a hybrid teaching-learningbased optimization( HTLBO) algorithm to quickly obtain satisfactory solutions. This algorithm adopted a new coding and decoding method according to the nature of the proposed parallel machine scheduling problem, which overcame the weakness of standard teaching-learning-based optimization algorithm that could not be directly applied to discrete problems. Meanwhile, it also proposed a local optimizer based on variable neighborhood search( VNS), which aimed to enhance the performance of the hybrid algorithm. Finally, it conducted the simulations to solve instances of PMS-CPT. The experimental results demonstrate the feasibility and validity of the proposed ILP model and hybrid algorithm. [ABSTRACT FROM AUTHOR]