Improvement Of Ant Lion Optimizer And Its Application In The Ship Route Planning

With the development of engineering technology and the scale of scientific calculation,the traditional numerical method cannot solve the complex optimization problems.In recent years,scholars have found that the intelligent optimization algorithm,which simulates the biological behavior of nature can effectively solve the defects of traditional numerical calculation methods in the face of complex problems,and can be widely used in many fields.Ant Lion Optimizer(ALO)is a population intelligent optimization algorithm proposed by Seyedali Mirjalili,an Australian scholar in 2015.It has attracted the attention of many scholars because of its few adjustment parameters,high precision of low-dimensional convergence,and ease of implementation.At present,the algorithm has been successfully used to solve many problems such as the optimization of lever structure,reactive power optimization of power system and uav route planning.However,ant Lion algorithm itself has some shortcomings,such as easy to fall into local extremum,unbalanced exploration and development ability,especially the poor performance of high-dimensional solution,which affects its wide application.In order to avoid these problems,this paper improved ant lion algorithm after consulting a large number of literatures and conducting many experimental tests,and applied the improved algorithm to function optimization and ship route planning.The main tasks are as follows:(1)In order to solve the problem of poor performance of ant-lion algorithm in high dimension,a preferred strategy based self-adaptive ant lion optimization algorithm(PSALO)is proposed.Firstly,an adaptive boundary mechanism is introduced in the process of ants wandering around ant lions to increase the activity of ant population and prevent the algorithm from falling into local extremum.Then,the optimal roulette strategy was added to the selection of antlions through roulette to maintain the diversity of antlions while speeding up the convergence rate of the algorithm.Finally,the dynamic scaling coefficient is added to the ant position updating formula,which improves the exploration ability in the early stage and development ability in the later stage.The analysis reveals the temporal complexity of the PSALO,and the experimentally demonstrated that the PSALO is blessed with minimal variation in the dimensions,and with greater robustness and stability in the higher dimensions.(2)In order to solve the ship route planning problem with the basic ant-lion optimization algorithm,which is prone to local extremum and has low optimization accuracy,a chaos initial ant lion algorithm with disturbance and mutation strategy(DMCALO)is proposed.First of all,chebyshev mapping is used to improve the initialization of the ant lion algorithm,which improves the population diversity of the algorithm.Then,a perturbation term with random small oscillation decreasing property is introduced and added to the ant location determination formula,which not only increases the activity and diversity of ants,but also improves the global searching ability of the algorithm.Finally,a mutation operation was added to the ant-lion position comparison updating formula to carry out beta mutation for ants with poor fitness value and prevent the algorithm from falling into local extremum.The analysis proves the time complexity of DMCALO algorithm,and the experimental results show that DMCALO has good solving ability and strong adaptive ability of solving in high dimensions.(3)Convert DMCALO and ship route planning,establish a two-dimensional mathematical model of ship route planning,and construct reasonable objective function and constraint conditions.By comparing ship route with other four algorithms in simple environment,general environment and complex environment,the effectiveness of DMCALO algorithm in ship route planning is proved.