Research On Multi-strategy Sailfish Optimizer And Its Application

The Sailfish Optimizer(SFO)is a meta-heuristic intelligent algorithm that simulates the hunting behavior of sailfish groups in the ocean.After the algorithm was proposed,it has been widely used in many fields such as science and industry.However,with the deepening of research,SFO has the disadvantages of low convergence accuracy and easily fall into local optimal solution when solving actual complex and large-scale optimization problems.Therefore,in order to improve its optimization ability and broaden the scope of application,this paper analyzes and improves the shortcomings of the Sailfish optimizer.The main work of this paper is as follows:(1)Aiming at the problem that the accuracy of the Sailfish optimizer is not high,the chaos initialization strategy,reverse learning and Cauchy mutation operator are added to the Sailfish optimization algorithm,and an improved form of the Sailfish optimizer is proposed.In order to evaluate the performance of the improved algorithm,based on six benchmark functions and three different scales of twodimensional Sylvester problem-solving experiments,the improved Sailfish optimizer was compared with three meta-heuristic algorithms for performance analysis.The results show that the improved Sailfish optimizer has higher solution accuracy and faster calculation speed,and is highly competitive in solution quality.(2)To further enhance the global optimization ability of the sailfish optimization algorithm,improve the premature convergence of the algorithm,and easily fall into the problem of local optimum,this paper proposes a chaotic adaptive sailfish optimizer with genetic characteristics(CASFO).Based on 20 benchmark test numbers and three engineering design optimization problems,the performance analysis and comparison of CASFO and six meta-heuristic intelligent algorithms are carried out.The experimental results show that the global optimization ability of the CASFO algorithm is significantly improved,and it has good robustness.(3)To broaden the application range of the Sailfish optimization algorithm and aim at the dynamic optimization problem in the chemical industry,a modified Sailfish optimizer combined with CVP equal separation dynamic optimization technology is proposed.It is applied to the solution of six dynamic optimization problems in chemical fields,and the experimental results are compared with the results of the methods given in the relevant literature.The experimental results show that the method proposed in this paper shows superior search performance and can obtain high-quality control trajectory and performance indicators.(4)Using equal discretization to solve dynamic optimization problems requires setting as many discrete grids as possible to obtain the optimal control trajectory,but this will increase the computational cost of the problem.Therefore,this paper proposes an adaptive non-uniform CVP based on the enhanced Sailfish optimizer.The experimental results of three chemical dynamic optimization problems show that the proposed method can adaptively allocate discrete grid balance calculation costs and performance indicators,and obtain more competitive control trajectories.