# Research on Fish Slicing Method Based on Simulated Annealing Algorithm
## 1 Introduction  
In the field of cut processing, workers usually rely on their own experience to cut products. Due to the different operating proficiencies of workers, many errors occur, which reduces the production efficiency of qualified products. With the continuous improvement in the industrial automation level, a variety of processing machinery and control algorithms have been designed to replace manual labor [1, 2]. However, traditional mechanical algorithms cannot cut and optimize complex targets such as the human brain and cannot meet the ever-changing production requirements. For example, in the field of aquatic product processing, there are many single-objective optimization problems. Workers can use traditional machinery to perform fixed-weight cutting with the optimization goal of obtaining the same segment weights from a fish body [3, 4, 5]. For the goal of obtaining the same segment lengths, fixed-length segmentation of the fish body is performed. These studies have been widely used in the processing flow of factories. With the continuous improvements in production requirements, some multiobjective optimization problems have appeared in the field of aquatic product processing. Such problems cannot be solved by traditional machinery alone, and there are few related studies. The optimization problem of large squid slices to be solved in this paper is a multiobjective optimization problem. The multiobjective optimization problem in this paper can be described as follows: After preprocessing a large squid (such as the giant squid D. gigas [6]), the processed raw materials in Figure 0(a) (length \(\times\) width approximately 400 \(\times\) 160 mm) are obtained. According to the cutting plan shown in Figure 1b, the raw materials are cut to obtain the finished product in Figure 1c. After removing the first and last waste materials, each piece of the finished product is a small piece that achieves the target index. As shown in Figure 1d, the weight is 40 \(\pm\) 2 g and the side diagonal connection length is 40 \(\pm\) 2 mm. The diagonal connection is shown in Figure 1e. If the weight index or diagonal index of a small piece exceeds the allowable range, the small piece is judged as unqualified. The unqualified rate of the cut product will determine the ultimate economic benefits. Therefore, the raw material cutting problem is to determine the starting and ending cutting positions and angles for each small piece of raw material so that the above two goals can be optimized at the same time. Workers usually rely on their own experience and adopt different cutting strategies according to different squid shapes to cut raw materials to meet the above two parameters at the same time, but this process depends heavily on the experience of the workers. Even the most experienced workers have a failure rate of more than 20% for their cut products. To solve this problem, researchers have developed a device that can perform fixed weight cutting, but it cannot meet the diagonal size requirements at the same time. The research goal of this article is to further solve this multiobjective optimization problem on this basis. In many industries (such as paper, cloth, metal plates, and wood), there are multi-objective cutting optimization problems, all of which aim to obtain small products that meet customer needs from large raw materials under the conditions of meeting multiple optimization objectives [7, 8]. For example, in the textile industry, the fabric cutting position needs to be determined according to customer quality requirements. The flaws in the fabric itself will affect the cutting results. To minimize the impact of the flaws on the quality of the cloth and to make the continuous length of the cloth as large as possible, [PERSON] [9] used the simulated annealing algorithm to optimize these two goals. The convergence of the algorithm was sped up by maintaining an appropriate mutation rate.