Research On Hybrid Biological Swarm Intelligent Optimization Algorithms And Their Applications Research

Each field has the problem of finding the optimal solution under specific requirements.It is an arduous task to solve the optimal solution of a complex problem under specified preconditions.The swarm intelligent optimization algorithm of biota provides new ideas and new methods for solving typical optimization problems.The swarm intelligence optimization algorithm of biota is not easy to jump out of the local optimum when optimizing complex problems with many constraints.It comprehensively utilizes the mutual coordination and complementation and disparity of various biological swarm intelligence optimization algorithms to realize the effective sharing of information and complement each other.Thereby enhancing the overall performance of the algorithm.The fusion of multiple algorithms is divided into three structures,embedded structure,pseudo-parallel structure and serial structure.details as follows:(1)An improved grey wolf optimizer based on tracking mode and seeking mode with embedded hybrid structure was proposed.The tracking mode and seeking mode are used to improve the randomness of the algorithm,thereby increasing the diversity of the population and the ability of the algorithm to balance exploration and exploitation.The improved algorithm and other swarm intelligence algorithms are separately optimized for the single-objective test function,and it is concluded that the improved algorithm is more accurate than other intelligent algorithms;in order to further confirm the effect of the selected embedded mixing factor,the improved algorithm proposed in this paper is compared with Binary Grey Wolf Optimizer,hybrid PSOGWO optimization and GWO Algorithm Integrated with Cuckoo Search.The statistical simulation results obtained by optimizing 21 typical benchmark functions are compared.The results show that the performance of the improved algorithm is better.(2)An improved ant lion optimizer based on spiral complex path searching pattern with embedded hybrid structure is proposed.Since the predation radius of ant lion decreases with the increase of iteration times,it is difficult to jump out of the local optimal cycle.Eight kinds of spiral search paths(Hypotrochoid,Rose spiral curve,Logarithmic spiral curve,Archimedes spiral curve,Epitrochoid,Inverse spiral curve,Cycloid,Overshoot parameter setting of the spiral)are adopted.In order to make the Ant Lion Optimizer have a variety of populations.The performance analysis of the improved algorithm includes complexity analysis and convergence analysis.Finally,numerical optimization experiments are carried out.Single-objective experiments prove that the accuracy of the improved algorithm is significantly improved and the convergence speed is increased.The spiral complex path search mode is introduced into the multi-objective ant lion algorithm to optimize the classical multi-objective function.The experimental results show that the comprehensive evaluation index,distribution evaluation index and convergence evaluation index of the improved multi-objective algorithm with embedded hybrid are better than the MOALO.(3)A pseudo-parallel chaotic self-learning antelope migration algorithm based on pseudo parallel structure is proposed.The addition of chaotic local search operator makes the population randomness and diversity significantly improved.After the internal optimization of nine kinds of chaotic self-learning antelope migration algorithm,five migration models are introduced to perform migration and mutation operations to improve the global search ability of the algorithm,and a pseudo parallel chaotic adaptive Tibetan antelope migration algorithm is formed.Two experiments are used to prove the superiority of the proposed pseudo-parallel algorithm based on the mobility model.First,use the CEC-2017 function to test the performance of the improved algorithm.The experimental results show that the average value obtained by the improved algorithm is closer to the optimal value of the function than the selflearning antelope migration algorithm.Then Simulation experiments were carried out on four engineering optimization problems,including three-bar truss design,welded beam design,pressure vessel design and spring design problem.Experimental results show that the improved algorithm can better solve the problem of function optimization and engineering optimization,and improves the global search ability of the selflearning antelope migration algorithm.