Chaotic adaptive sailfish optimizer with genetic characteristics for global optimization.

The sailfish optimizer (SFO) is a new metaheuristic swarm intelligence optimization algorithm based on the hunting behavior of biological groups, simulating the elite strategy of the population, and the strategy of alternating sailfish attacking the sardines. It has the advantages of strong search ability, easy implementation and good robustness, and has better performance than popular metaheuristic algorithms. However, the classical SFO suffers from insufficient solution accuracy, slow convergence speed, premature convergence, and insufficient balance between global search and local search capabilities. This paper proposes a chaotic adaptive sailfish optimizer with genetic characteristics (CASFO). The CASFO algorithm first introduces the Tent chaos strategy to initialize the positions of sailfish and sardines to increase the diversity of the population. Secondly, the adaptive t-distribution is introduced to mutate individual sardines to balance and improve the exploration and exploitation capabilities of algorithms. Finally, genetic characteristics are introduced to carry out natural inheritance of sailfish and sardines to improve the solution accuracy and convergence speed of the algorithm. CASFO is tested with 20 mathematical optimization problems and 3 classical engineering optimization problems. The numerical results and comparisons among several algorithms show that the performance and efficiency of the CASFO algorithm are significantly improved. [ABSTRACT FROM AUTHOR]