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From collective intelligence to evolutionary computation and machine learning, symmetry can be leveraged to enhance algorithm performance, streamline computational procedures, and elevate solution quality. Grasping and leveraging symmetry can give rise to more resilient, scalable, and understandable algorithms. In view of the flaws of the original Sailfish Optimization Algorithm (SFO), such as low convergence precision and a propensity to get stuck in local optima, this paper puts forward an Osprey and Cauchy Mutation Integrated Sailfish Optimization Algorithm (OCSFO). The enhancements are mainly carried out in three aspects: (1) Using the Logistic map to initialize the sailfish and sardine populations. (2) In the first stage of the local development phase of sailfish individual position update, adopting the global exploration strategy of the Osprey Optimization Algorithm to boost the algorithm’s global search capability. (3) Introducing Cauchy mutation to activate the sailfish and sardine populations during the prey capture stage. Through the comparative analysis of OCSFO and seven other swarm intelligence optimization algorithms in the optimization of 23 classic benchmark test functions, as well as the Wilcoxon rank-sum test, it is evident that the optimization speed and convergence precision of OCSFO have been notably improved. To confirm the practicality and viability of the OCSFO algorithm, it is applied to solve the optimization problems of piston rods, three-bar trusses, cantilever beams, and topology. Through experimental analysis, it can be concluded that the OCSFO algorithm has certain advantages in solving practical optimization problems.
Cao et al. (Thu,) studied this question.