Hybrid strategy improved crayfish optimization algorithm

Crayfish Optimization Algorithm (COA) is a newly proposed meta-heuristic optimization algorithm, which seeks the optimum by simulating crayfish’s summer avoidance, competition and foraging. However, COA also suffers from the defects of insufficient population diversity, easy to fall into local optimization, and insufficient global search ability. In order to solve these problems, this study proposes a crayfish optimization algorithm based on hybrid strategy improvement (HSICOA). First, the population position is initialized by introducing the center of gravity reverse learning mechanism to improve the diversity of the population; second, in the summer avoidance phase, a population diversity feedback mechanism is introduced to adjust the parameters and dynamically adjust the variation strength. Then, in the foraging stage, the optimization accuracy of the algorithm is further improved by accumulating differential feeding strategies, which fully consider the difference in dimensional information between individuals and food. In the experimental analysis phase, the HSICOA algorithm was verified using some test functions of the CEC2005 test set, and the same algorithm was verified on the pressure vessel design problem, and the HSICOA algorithm achieved better experimental results.