Applications Of Improved Whale Optimization Algorithm In Function Optimization

Swarm intelligence algorithms are widely used in various optimization problems because of their simple ideas and good versatility.They have achieved success in various fields such as engineering,economics,scientific research,and medicine.With the continuous development of science and technology,a variety of complex optimization problems are emerging.One is the improvement of the dimension of the optimization problem,which leads to an exponential increase in the search range.The other is that optimization often involves multiple goals at the same time.In order to solve the optimization problem more efficiently,this paper improves the basic whale algorithm and applies it to high-dimensional function optimization and multi-objective function optimization problems.The major job in this article is shown as following:(1)High-dimensional function optimization and multi-objective function optimization are introduced,and the limitations and dilemmas of these two types of problems are analyzed.Then it describes the three position updating measures of the whale algorithm,and analyzes the original algorithm from the aspect of exploration and development.(2)In order to solve the high-dimensional function optimization problem,this paper improves the whale optimization algorithm,and gives two improved algorithms,namely the improved whale optimization algorithm(IWOA)and the whale optimization algorithm based on quadratic interpolation(QIWOA).The algorithm IWOA introduces Levy flight into the original algorithm.Its special step size enhances the search performance of the algorithm,and at the same time helps the algorithm to jump out of the local optimum,and improves the convergence speed and diversity of the algorithm.To improve,on the one hand,new step parameters are introduced,so that the algorithm can more effectively explore the entire search space;on the other hand,a quadratic interpolation algorithm is introduced,which enhances the development ability of the original algorithm and improves the accuracy of the obtained solution.The results of simulation experiments show that these two algorithms not only perform well in the accuracy of the obtained optimal solution,but also perform well in converging to the optimal speed,and can effectively implement high-dimensional function optimization.(3)Combining existing methods and strategies to improve the whale algorithm,a multi-objective whale optimization algorithm(EMOWOA)with enhanced performance for multi-objective optimization is proposed.On the basis of the Pareto advantage,the algorithm first introduces anti-learning ideas into the initialization process of the algorithm,incorporates solutions in the opposite direction into the search range,and improves the diversity of the algorithm.Second,it introduces a dual leader selection mechanism.In this mechanism,the two mechanisms of congestion distance and novel potential energy ranking are used to select the leader of the whale optimization algorithm,in order to ensure the diversity and convergence of the algorithm.Finally,when updating the external file,the Pareto advantage and the congestion distance are used.Two strategies to ensure the quality of non-dominated solutions in external files.The simulation results show that compared with the comparison algorithm,the algorithm is successful in most test functions used.The obtained non-dominated solution shows a good balance in convergence and diversity,which is a solution to multi-objectives.In general,the algorithm is an effective method for solving multi-objective optimization.