In the rapid development of the computer industry, the development of big data technology is getting faster and faster, and the requirements for data processing in corporate accounting and financial management are becoming more and more stringent. Therefore, the effective integration of computer data processing technology and corporate financial management is conducive to improving the overall efficiency of accounting work and improving the integrity of accounting information. Accounting informationization is the main development trend that must be faced in the process of financial management of Chinese enterprises. In the development of accounting informatization, we need to effectively apply computer technology and computer internal management procedures to realize the automation of financial work and intelligent operation and management. This will help improve the overall level of financial management and promote the healthy and stable development of my country’s accounting industry. However, there are still some problems when we promote the application of accounting management on computers. In consequence, we need to study and analyze these problems and propose effective improvement measures. Only in this way can the corporate accounting management work be effectively combined with computer technology and the level of accounting informationization can be improved.
In many fields, including management, computer, and communication, Large-Scale Global Optimization (LSGO) plays a critical role. It has been applied to various applications and domains. At the same time, it is one of the most challenging optimization problems. This paper proposes a novel memetic algorithm (called MPCE & SSALS) based on multiparent evolution and adaptive local search to address the LSGO problems. In MPCE & SSALS, a multiparent crossover operation is used for global exploration, while a step-size adaptive local search is utilized for local exploitation. A new offspring is generated by recombining four parents. In the early stage of the algorithm execution, global search and local search are performed alternately, and the population size gradually decreases to 1. In the later stage, only local searches are performed for the last individual. Experiments were conducted on 15 benchmark functions of the CEC′2013 benchmark suite for LSGO. The results were compared with four state-of-the-art algorithms, demonstrating that the proposed MPCE & SSALS algorithm is more effective.
The pickup and delivery problem with time windows and last-in-first-out (LIFO) loading (PDPTWL) is a combinational optimization problem extended from the well-known vehicle routing problem (VRP), in which the type of customer point is no longer single and the loading order of the requests must meet the LIFO constraint. Due to its NP-hard nature, it is difficult for exact algorithms and heuristics with a linear structure to solve a large-scale problem in a reasonable time. In this paper, we propose a fast decomposition and reconstruction framework (D&R) to solve the PDPTWL with high quality in a relatively short time. An angle-based sweep method is used to decompose a complete solution into multiple subsolutions, each of which is assigned to a tabu search for optimization. To speed up the whole process, the optimization procedure of sub-solutions is performed by different processors of multi-core CPU in parallel. Three neighborhood operators and three strategies to reduce the number of vehicles are designed to cope with the tabu search for further improvement. Moreover, the adaptive memory mechanism is added to provide a better start when the optimization procedure falls into the local optima. We compare our framework against the best known solutions on 119 instances with up to 300 requests, the results show that our framework is able to improve over 85% (107 out of 119) of the best known solutions. More specifically, the number of vehicles is optimized by about 60% (74 out of 119) and the driving distance by about 50% (59 out of 119). In addition on instances with the largest size of requests, the computational time of our framework can be 1/50 of the comparative results, confirming its efficiency.INDEX TERMS Pickup and delivery problem with time windows, LIFO loading, tabu search, adaptive memory.
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