Improvement Of Firefly Algorithm And Its Application In The Logistic Center Location

With the increasing scale of engineering technology and scientific computing,the traditional optimization methods cannot find the required solution in a reasonable time.In terms of solving efficiency,the solution of this kind of problem is basically invalid.In recent years,the heuristic intelligent optimization algorithm based on bionics has been developed and studied constantly,and it has been favored by many scholars and widely used in many fields because of its simple operation and high efficiency.In 2008,a scholar named Yang from the University of Cambridge proposed the Firefly Algorithm based on the firefly's own luminescence to transmit information.As a relatively novel heuristic algorithm,this algorithm has attracted many scholars' attention because of its simple and clear mode,fewer parameters to be set,high convergence speed and high precision of solution,and has been applied to the area of clustering,economic dispatch,power generation system,complex network,image annotation and so on.Although the firefly algorithm has obvious advantages and is favored by many researchers,it also has some shortcomings like other heuristic algorithms.For example,under the condition of high dimensions,the algorithm will easily fall into the local minimum value and the performance of the solution is reduced because of the weakening of the attractiveness.In view of the shortcomings of firefly algorithm,this paper analyzed the cause of this phenomenon after consulting a large number of literature and repeated experimental tests,and optimized and improved the parameters involved in the algorithm and its own search mechanism,so as to improve the optimization performance of the algorithm.The improved algorithm is applied to the location problem of logistics distribution center and a better solution is obtained.The main work of this paper is as follows:(1)In order to improve the accuracy of firefly algorithm in high dimensions,the firefly algorithm with oscillation,constraint and natural selection mechanism is proposed.Firstly,the second-order oscillation factor is introduced to balance the influence of the previous generation of individuals on the current generation of individuals,so as to prevent the individuals of fireflies from falling into local extremum.Then,the constraint factor based on sigmoid function is added to dynamically adjust the moving distance of individuals,so as to avoid the situation that the individuals of firefly caused by excessive disturbance near the theoretical optimal value leads to the reduction of precision in the later stage of the algorithm.Finally,natural selection based on the decreasing trend of reciprocal Gaussian integral is used to keep individual diversity and accelerate the convergence speed of the algorithm.The convergence time complexity of the improved algorithm are proved by theoretical analysis.Through the simulation of 12 standard functions with multiple dimensions and different characteristics,the test results show that the optimization accuracy and convergence speed of OCSFA are obviously improved.Especial in the case of high dimension,the theoretical optimum can still be found for almost all functions,which better solves the problem that firefly algorithm is not suitable for high dimension solution.(2)Aiming at the shortcomings of basic firefly algorithm in solving the problem of multi-distribution centers location,such as easily fall into local extremum and low precision,a dynamic adaptive firefly algorithm is proposed.It has the globally-oriented moving mechanism and can dynamically adjust the step size and attractiveness.Firstly,through the adaptive deviation degree strategy of optimal distance combining with the Gaussian distribution,it optimizes the fixed step-factor to balance the exploration and excavation capabilities of the algorithm better,and improve the diversity of the population.Then,the minimum attractiveness is introduced in the algorithm and adaptively changed with the number of iterations,which can avoid random walk due to lack of traction between fireflies.Finally,this paper improves the mobility mechanism based on the position of current optimal firefly.It not only makes firefly move with global orientation,but also expands the sharing of information between fireflies to improve the overall evolutionary optimization performance of the algorithm.Theoretical analysis proves the convergence and time complexity of the improved algorithm.The experimental results show that the proposed algorithm has significantly improved the convergence speed and precision with better solving performance.(3)This paper uses the GDAFA algorithm to solve the problem of multi-distribution centers location.The natural number coding method suitable for solving the problem is defined,and use the boundary buffer domain to deal with the out-of-bound fireflies and increase the diversity of the scheme.The replacement principle is used to deal with the standby sites with the same ordinal number of the individual,which will improve the effectiveness of alternative scheme.Optimize the constraint conditions in the model,improve the fixed penalty distance to adaptive value,and increase the adaptability of the model.The experimental results show that the proposed algorithm is superior to the other four comparison algorithms in the optimal solution,the worst solution or the average solution.It is an effective and feasible method for solving the problem of multi-distribution centers location.