| The intelligent optimization algorithm is a random search algorithm proposed by simulating natural evolution or social behavior.The intelligent optimization algorithm has the characteristics that the principle is simple and the model is easy to realize,can solve many traditional methods which is difficult to solve the complex problem,is widely used in various fields.In this paper,a new intelligent optimization algorithm—Simulated Pursuit Algorithm,is proposed to solve the problem of the chasing competition of athletes in the long-distance race.The mathematical model is esTablelished and applied to the optimization problem.The Simulation Pursuit Algorithm combines the advantages of tentative exploitation with purposeful chase,which is an effective global optimization algorithm.In this paper,we first introduce the basic Simulation Pursuit Algorithm,analyzing the basic principle of the algorithm.In order to improve the diversity of the algorithm and introduce the cooperative operator,we propose a Co-evolutionary Simulation Pursuit Algorithm.In order to extend the simulation pursuit algorithm from the continuous optimization problem to the discrete optimization problem,a new design definition is given for the detection operator and the chasing operator,and an improved simulation pursuit algorithm is proposed to solve the TSP problem.Specific research work in the following three aspects.1、Proposed a new group of intelligent algorithm-Simulation Pursuit Algorithm.The algorithm designs the pursuit operator and the detection operator.The leading individual performs the operation of the detection operator in order to obtain the better position.The backward individual takes the competitive advantage,sets the catch-up target,performs the pursuit operator operation,completes the follow-Evolutionary optimization.The performance of the chase operator is analyzed.Six typical test functions are used to simulate the experiment,and the precision,convergence speed and sTableility of the algorithm are analyzed.The simulation results show that the Simulation Pursuit Algorithm has a fast convergence rate and a high precision,which is a s Tablele optimization algorithm.2、In order to maintain the population diversity in the algorithm search process,this paper introduces three cooperative operators in the basic Simulation Pursuit Algorithm(SPA),fully shares the information among individuals,and proposes a Co-evolutionary Simulation Pursuit Algorithm.The four benchmark function tests show that adding the flip cross operator in the later stage of the algorithm can avoid generating too many repeated solutions.The improved algorithm balances the concentration of the search and the diversity of the population and improves the ability of the algorithm to jump out of the local optimal.The Simulation Pursuit Algorithm is superior to the basic Simulation Pursuit Algorithm in the optimization ability and convergence rate.3、In this paper,an improved Simulation Pursuit Algorithm is proposed to solve TSP.The algorithm uses the greedy strategy and the symmetric strategy to initialize the population.Define the exchange operation,the exchange matrix,the pursuit operator and the detection operator.In this paper,an improved simulation pursuit algorithm is proposed to solve the problem of combinatorial optimization.The simulation results show that the improved simulation pursuit algorithm has a higher accuracy for TSP,and it is an effective algorithm.The Simulation Pursuit Algorithm is applied to solve the problem of TSP.It provides a template to apply the Simulation Pursuit Algorithm to the discrete optimization problem,and broaden the application field of the Simulation Pursuit Algorithm. |
