Improved Multi-objective Moth-flame Optimization Algorithm For The Synthesis Of Antenna Arrays

Multi-objective optimization belongs to the field of multi criteria decision-making.It is a mathematical problem involving the simultaneous optimization of multiple objective functions.Multi-objective optimization problems generally involve many scientific fields such as engineering,economy and logistics.It is necessary to weigh two or more conflicting objectives and finally give a set of compromise solutions.Multi objective moth-flame algorithm is a new optimization algorithm proposed in recent years.It has the advantages of less adjustment parameters and strong robustness,but it still has the problems of uneven distribution of Pareto approximate solution set and premature convergence.Based on the in-depth study of multi-objective optimization theory,this paper studies and improves the multi-objective moth flame optimization algorithm The main work includes:(1)Aiming at the problems of poor diversity and insufficient convergence of the multi-objective moth-flame algorithm,this paper designs a new crowding distance calculation method that considers the overall situation,which can better distinguish individuals with similar crowding levels in the entire space,so that can effectively improve the distribution of solution set.At the same time,the crossover operator based on greedy selection strategy is used to reduce the useless crossover calculation and mutate the elite individuals,so as to improve the probability of the algorithm jumping out of the local optimal trap.(2)Aiming at the imbalance of global search ability and local development ability of multiobjective moth-flame optimization algorithm.firstly,the good point set generation moth is introduced to ensure the quality of the initial population,and then the improved Sigmod function is used to adjust the number of flames in the iterative process of the algorithm,so that the flame converges nonlinearly with the number of iterations,In the early stage of iteration,a large number of flames can be reserved to expand the search range.In the middle stage of iteration,the flame convergence speed can be adjusted as needed to ensure the convergence speed of the algorithm.In the later stage of iteration,a small number of flames can be maintained for local exploration,which effectively balances the comprehensive optimization ability of the algorithm in each period.Through the above improved scheme,a multi-objective moth-flame optimization algorithm based on multi strategy cooperation(NMOMFO)is proposed.(3)The performance of the algorithm is tested on several classical test problems.Compared with several other classical multi-objective optimization algorithms,it is found that NMOMFO algorithm has better optimization performance,can more accurately converge to the real Pareto front of the test problem,and can maintain good distribution.(4)NMOMFO algorithm is applied to the pattern synthesis problems of linear array and planar array respectively.Then we analyze the antenna pattern function and design the fitness function for each optimization objective.The experimental results show that the NMOMFO algorithm has achieved better optimization results than the classical analytical method on the linear array synthesis problem.Moreover,on the complex and irregular plane array synthesis problem which is difficult to deal with by the classical analytical method,the algorithm can also better balance each target to achieve the design index,which verifies the practical value of NMOMFO algorithm in the engineering field.