Research And Application Of Multi-objective Optimization Algorithm Based On Coevolution

In engineering practice and scientific research,we often encounter a number of multi-objective optimization problems,which will have some defects and disadvantages if we adopt traditional methods to solve these problems.The use of evolutionary algorithms to solve multi-objective optimization problem has been proved to be an effective method,but the evolutionary multi-objective optimization algorithm is not perfect,there are still shortcomings of these evolutionary algorithm solution set distribution is not uniform,premature convergence and low precision.Co-evolutionary algorithm is a new method to solve the multi-objective optimization problem in recent years.Compared with the traditional evolutionary multi-objective optimization algorithm,it can improve the global convergence and avoid the premature convergence to some extent.However,at present,the multi-objective evolutionary optimization algorithm is used to solve the multi-objective problem.It is concluded that the distribution and diversity of non-dominated solutions are not ideal.In this paper,aiming at the shortcomings of co-evolutionary algorithm put forward the corresponding strategies and measures for improving the coordination of multi-objective optimization evolutionary algorithms and evolutionary mechanism of integration and improvement,the co-evolutionary multi-objective optimization algorithm is more effective,and this method is effectively applied to solve the multi-objective robot path planning problem,the main research work is as follows:(1)In order to solve the problem that the selection of representative individuals is not strong enough,a packet ranking evaluation of cooperative co-evolutionary algorithm is proposed.In order to make the selection of the representative combination more oriented,we will choose the best combination of the individual representatives of the new generation through the continuous evaluation of the new generation of new species.The proposed algorithm is compared with other evolutionary algorithms using typical test functions.The results show that the improved cooperative evolutionary algorithm has faster convergence speed and stronger global convergence ability.The proposed scheduling evaluation strategy of cooperation based on cooperative evolutionary algorithm is applied to optimize the control parameters of the complicated system of PID,the experimental results show that the algorithm can efficiently search the optimal parameters of PID combination of requirement of the given performance index,and has better application prospect.(2)Aiming at multi-objective optimization algorithm of non-dominated solution of uneven spatial distribution and convergence precision,the multi swarm cooperative theory,combined with the fast non dominated sorting method and strategy of elite external archive,this paper presents an optimization algorithm for cooperative multi-objective multi-population cooperation.Optimization of contrast test,the algorithm and the NSGA-II algorithm proposed the multi-objective problem using the standard test function.The results show that the multi-objective optimization algorithm proposed by multi group cooperative,can get a more uniform and more accurate set of non-dominated solutions to Pareto front better.(3)In this paper,the method of multi objective cooperative multi objective optimization algorithm is proposed,and the method and its implementation for solving multi objective path planning problem are studied.The multi objective path planning task is modeled,and a multi-objective optimization model is proposed,which includes several performance indexes.The simulation results show that the proposed method can effectively obtain the optimal path under the multi objective requirements.