Research On Sine Cosine Algorithm And Application

The population-based stochastic optimization algorithm is one of the importan,t methods to solve the optimization problem,and it is an effective method to solve the global optimization problem,which has attracted the attention of many scholars at home and abroad.The sine cosine algorithm is a new population-based stochastic optimization method,which uses sine and cosine functions to fluctuate the solution run to the global optimal solution.This paper proposes three improved algorithms after learning the algorithm in detail,the specific research contents are as follows.1.An alternating sine cosine algorithm based on the elite chaotic search strategy is proposed,the new algorithm uses the nonlinear adjustment strategy based on logarithmic curve to modify the control parameters,uses the elite individuals's chaotic search strategy to enhance the exploitation ability of the algorithm.The new algorithm based on this strategy and the opposition-based learning algorithm are alternately implemented to enhance the exploration ability,reduce the time complexity and improve the convergence speed of the algorithm.The new algorithm has been tested by 23 benchmark test functions,and compared with the improved SCA and the state-of-the-art heuristic algorithm.The results analysis show the effectiveness and superiority of the proposed algorithm.2.A hybrid sine cosine algorithm based on the optimal neighborhood and quadratic interp olation is proposed.The new algorithm uses an optimal neighborhood update strategy to overcome the defect that the population is updated by the global optimal individual in the sine cosine algorithm.and it adopts a quadratic interpolation curve for individual updates.In addition,QISCA incorporates quasi-opposition learning strategies to enhance the population's global exploration capabilities,and improves the convergence speed and accuracy.The simulation experiments of 23 benchmark functions show that the new algorithm can better coordinate the exploration and exploitation capabilities and improve the global optimization ability,compared with the improved sine cosine algorithm and the representative stochastic optimization algorithm.3.Riesz fractional derivative sine cosine algorithm based on the Riesz fractional derivative mutation strategy is proposed.The new algorithm uses quasi-opposition learning to initialize the population and increase the diversity of the population.Based on the approximate formula of Riesz fractional derivative with second-order accuracy,we construct a new mutation strategy to update the optimal individual and improve the calculation accuracy of the algorithm,the proposed method is integrated into probabilistic quasi-opposition learning strategy and opposition-based learning strategy to enhance the ability of global exploration of the population and speed up the convcergence of the algorithm.The new algorithm was tested in two sets of test sets(standard benchmark of 23 problems and standard IEEE CEC 2017).The simulation experiments demonstrate that the proposed algorithm significantly outperforms the latest heuristic-based algorithms in both exploration,exploitation and solution quality.Two engineering questions are applied to confirm the superior performance and ability of proposed algorithm.