Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network

In order to improve the efficiency and reduce energy consumption of unmanned sailing boats for water quality inspection and sampling, particle swarm optimization was used for global path planning. The traditional particle swarm optimization algorithm has the problem of premature convergence because it is easy to fall into the local optimal solution. To solve this problem, an improved particle swarm optimization algorithm is proposed. Based on the traditional particle swarm optimization algorithm, the crossover and mutation operations in genetic algorithm are introduced. Firstly, the particle swarm was initialized and the fitness of the particle was calculated, and the individual optimal value and local optimal value were updated. Then crossover and mutation operations are introduced to update the particle velocity and position according to the results. Finally, the convergence of the algorithm is judged according to the decision criteria. The simulation results show that compared with the traditional particle swarm optimization algorithm, this algorithm can effectively avoid the algorithm falling into the local optimal solution, and greatly shorten the overall path length and reduce path planning time.

Ultra-high performance concrete has excellent mechanical properties such as ultra-high strength and high durability, and has a broad application prospect in the field of bridge engineering. To make full use of the superior mechanical properties of UHPC and reduce the cost of the bridge, the particle swarm optimization algorithm is used to optimize the structural design of the UHPC beam bridge; Aiming at the problem that the traditional particle swarm optimization algorithm is easy to fall into local optimum, the nonlinear adaptive weight update method is used to improve the particle swarm optimization algorithm. Based on the above-mentioned improved particle swarm optimization algorithm, the optimal design method of ordinary reinforced UHPC beams and prestressed UHPC beams with commonly used highway spans is studied. The reliability of the optimized structural limit state is analyzed by Monte Carlo (MC) importance sampling method. The results show that compared with the traditional particle swarm optimization algorithm, the improved particle swarm optimization algorithm has higher convergence speed and convergence accuracy. The optimal height-span ratio of ordinary reinforced UHPC beams decreases with the increase of the span, and the construction cost gradually increases; The optimal height-span ratio of the prestressed beam decreases first and then increases with the increase of the span, and the construction cost gradually increases. The calculation results of load capacity reliability indexes of optimization results are all higher than the target reliability indexes of similar components stipulated in China’s “Uniform Standard for Structural Reliability Design of Highway Engineering”.

In the application of scheduling strategies for power dispatching robots, traditional particle swarm optimization algorithms often become trapped in local optima during the optimization process, preventing the attainment of globally optimal results. Therefore, this paper proposes multi-objective optimization scheduling strategies for power dispatch robots based on an improved particle swarm optimization algorithm framework. When constructing the multi-objective optimization model for power dispatch robots, the particle swarm optimization algorithm is applied, and an adaptive inertia weight adjustment mechanism is used to improve search efficiency, ultimately achieving the optimal solution for multi-objective scheduling. The experimental results show that using the improved particle swarm optimization algorithm to optimize the multi-objective scheduling task of the power dispatching robot significantly reduces the total operating cost of the power system by approximately 19,326.8 US dollars, while keeping monthly pollutant emissions below 100 tons and effectively reducing system network loss. This method not only improves power scheduling efficiency but also promotes the development of green energy, providing strong support for the sustainable development of the power system.

For the problem of defect inversion in magnetic flux leakage technology, a fast, globally convergent particle swarm optimization algorithm based on the finite-element forward model is introduced as an inverse iterative algorithm in this paper. Two aspects of the traditional particle swarm optimization algorithm have been improved: self-adaptive inertia weight and speed updating strategy. For the inertia weight, it can be adaptively adjusted according to the particle position. The speed update strategy mainly uses the best experience positions of other particles in a randomly selected population to realize the algorithm’s learning. At the same time, the learning factor of the position variable is designed to change with the number of iteration steps. The particle with a good position is added to jump out of the local minimum and accelerate the optimization process. Through the comparison experiment, the improved particle swarm optimization algorithm has a faster convergence speed compared with other traditional particle swarm optimization algorithms. It is more difficult for it to fall into the local minimum value and it is more easily converted to a higher precision.

The paper proposed a network scheduling in cloud computing based on intelligence Particle Swarm Optimization algorithm aimed at the disadvantages of cloud computing network scheduling.Firstly, on the basis of cloud model, used intelligence Particle Swarm Optimization algorithm with strong ability of global searching to find the better solution of cloud computing network scheduling then turned the better solution into the initial pheromone of improved Particle Swarm Optimization algorithm, and found out the cloud computing network scheduling and the algorithm's global optimal solution through improved Particle Swarm Optimization information communications and feedbacks.Finally, made comparison test of the three benchmark function on the basis of MATLAB, the results showed, compared with traditional intelligence Particle Swarm Optimization algorithms, the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution's time of objective function value is shorten which meet the user's needs more.

With the acceleration of the digitalization process of the cultural and tourism industry, the efficient mining and visualization of IP elements in cultural and tourism towns have become a research hotspot. This paper proposes an improved particle swarm optimization algorithm that integrates dynamic inertia weight adjustment, adaptive learning factor design and local search mechanism for data mining of IP elements in cultural and tourism towns. In the experimental stage, a cultural and tourism town data set containing multi-source heterogeneous data is constructed, and the improved algorithm is compared with the traditional particle swarm optimization algorithm and genetic algorithm in the Python environment. The results show that the improved algorithm improves the accuracy of IP element mining by 23.6% compared with the traditional particle swarm optimization algorithm and 18.4% compared with the genetic algorithm; the average running time is shortened by 15.2%, and the convergence speed is increased by 31.7% in complex data environments. The information visualization system constructed based on the algorithm realizes the intuitive display of IP element characteristics and correlation through visualization technologies such as Sankey diagrams and heat maps and interactive design. The research results provide technical support for the in-depth development and precision marketing of IP elements in cultural and tourism towns.

In order to improve the transmission efficiency of communication data, this paper mainly analyzes the control problem of communication data transmission efficiency. Firstly, the control model of information data transmission efficiency is established, and the control mechanism of transmission efficiency is described systematically. Secondly, the important parameters affecting the transmission efficiency of communication data are analyzed in detail, and the limitations of the traditional clustering and local clustering mechanism are pointed out. In order to realize the optimal control of communication data transmission efficiency, this paper improves the traditional particle swarm optimization algorithm, and also improves the iterative formula of data position and speed update. The experimental results show that compared with the traditional particle swarm optimization algorithm and single channel transmission method, the improved particle swarm optimization algorithm can better improve the transmission efficiency of communication data.

The assembly accuracy of aircraft thin-walled components produced in the same batch is subject to fluctuations influenced by part tolerances and assembly processes. Significant variations in assembly precision can lead to increased assembly adjustment costs, severely affecting both efficiency and overall expenses. However, tolerance optimization that takes into account these fluctuations requires the integration of assembly deviation calculations, characterized by a high demand for large sample sizes and frequent iterative computations, which results in escalated computational costs. In this context, this paper establishes a data-driven mathematical model for tolerance optimization that comprehensively considers fluctuations in assembly accuracy, manufacturing costs, and quality losses, using a surrogate model as the core computational engine. Furthermore, to address the shortcomings of the traditional particle swarm optimization algorithm, namely, its tendency to get trapped in local optima and its insufficient local search capabilities, this study proposes an improved particle swarm optimization algorithm that integrates a chaotic local search strategy and second vibration particles. This enhanced algorithm is applied to the tolerance optimization problem of aircraft thin-walled components.

In view of the problem of premature convergence of traditional particle swarm optimization (PSO) algorithm in path planning, which is easy to converge to local optimal solution and poor path quality, some theory about the corresponding PSO algorithm of simulated annealing optimization is studied in this paper. While planning the moving path of the robot, it analyzes the effect of initial temperature and cooling coefficient on path length and iteration times from the major contributing factors of simulated annealing algorithm. Thus deduce the law of its change and seek the optimal parameter matching. Simulated annealing algorithm can not only move the updated particle position on the basis of the particle swarm optimization formula, but also select the updated position with a certain probability. The method is used to avoid the particle converging into the local optimal solution in the whole iterative process. The capabilities of the global optimization is strengthened. Compared with the traditional PSO algorithm, the simulated annealing PSO in complex environment has better optimization ability, shorter path and fewer iterations in the simulation results.KeywordsParticle swarmPath planningSimulated annealingLinear inertia weight

This paper proposes a method to realize better locating and sizing optimization of distributed generation. It combines the advantages of improved particle swarm optimization algorithm and principal component analysis technology. Based on artificial bee colony algorithm, it proposes the improvement about traditional particle swarm optimization algorithm to improve the convergence. Meanwhile, by means of using principal component analysis, it builds comprehensive model including several economy index, voltage stability index and constrained condition for comprehensive evaluation of multi-objective optimization to enhance credibility of the method proposed.

The traditional particle swarm optimization algorithm is generally only suitable for solving the association rule mining of continuous data, and the improved PSO algorithm needs to be used for the association rule mining of discrete data. Firstly, the FP-growth algorithm is used to construct a fuzzy FP-tree to mine frequent itemsets, and then the PSO algorithm is applied to the mined frequent itemsets. Aiming at the shortcomings of the association rule mining algorithm of PSO, an improved association rule data mining algorithm is proposed. This paper proposes application of particle swarm optimization algorithm in improved association rules data mining method. Experiments show that the particle swarm optimization algorithm proposed in this paper is feasible and efficient in improving association rule data mining.

The particle swarm optimization (PSO) algorithm is an intelligent optimization algorithm and has been successfully applied to many fields. However, it has the deficiencies of the poor search capability and the low calculation efficiency. To solve the problems, the genetic algorithm is introduced to improve the PSO algorithm in the paper. Then, three simulated examples were used to assess the ability of the improved PSO algorithm. The results showed that the improved PSO algorithm could jump out of the local minimum and achieve the better solution. Furthermore, the paper reveals that the improved PSO algorithm seems to be an improvement on the original PSO algorithm.

Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal.

In the area of cloud computing load balancing, the Particle Swarm Optimization (PSO) algorithm is neoteric and now praised highly, but recently a more neoteric algorithm which deploys the classifier into load balancing is presented. Besides, an algorithm called red-black tree which is aiming at improving the efficiency of resource dispatching is also praised. But the 3 algorithms all have different disadvantages which cannot be ignored. For example, the dispatch efficiency of PSO algorithm is not satisfying; although classifier and red-black tree algorithm improve the efficiency of dispatching tasks, the performance in load balancing is not that good, as a result the improved PSO algorithm is presented. Some researches are designed to get the advantages of new algorithm. First of all, the time complexity and performance for each algorithm in theory are computed; and then actual data which are generated in experiments are given to demonstrate the performance. And from the experiment result, it can be found that for the speed of algorithm itself PSO is the lowest, and the improved PSO solve this problem in some degree; improved PSO algorithm has the best performance in task solving and PSO is the second one, the red-black and Naive Bayes algorithm are much slower; PSO and improved PSO algorithm perform well in load balancing, while the other two algorithms do not do well.

It is a NP-Hard problem to obtain optimal solutions to deal with the large-size job-shop scheduling problem (JSSP). In this paper, a new hybrid algorithm based on traditional particle swarm optimization (PSO) algorithm for addressing a JSSP is proposed. Firstly, a particles encoding is designed to reduce the range of solution space. Secondly, a simulated annealing operator combined with local search operator is immersed into the algorithm to extricate itself from local optimal solution, and the performance of the individual search is improved as well. Furthermore, an interference operator is integrated to search the optimal solution by the rapid convergence features. Experimental results based on benchmark problems of LA instances and some FT instances demonstrate that the proposed hybrid algorithm shows higher performance in dealing with the classical large-scale problem than the original design.