Exploring multi-objective optimization scheduling strategies for power dispatch robots based on an improved particle swarm optimization algorithm framework.

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 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.

In order to solve the logistics optimization problem, an application method of the improved particle swarm optimization algorithm in logistics energy-saving pickup information network is proposed. Firstly, a mathematical model of logistics cycle picking information scheduling optimization is established, logistics and picking paths are encoded as particles, and the optimal logistics cycle picking optimization scheme is found through the cooperation between particles. Secondly, the deficiencies of the particle swarm optimization algorithm are improved accordingly. In order to test the performance of the IPSO algorithm in solving the logistics circulation picking problem, in the simulation environment of P42 core, 2.6 GHz CPU, 4 GB memory, and Windows XP, the simulation experiment was carried out using VC++6.0 programming operating system. The particle number of the IPSO algorithm is 20, ω max = 5 , ω max = 1 . The experimental results show that the improved particle swarm optimization algorithm can effectively bypass the premature convergence of the traditional particle swarm optimization algorithm and ensure that the optimal solution is searched in the global scope, and the optimal probabilistic solution is obtained, which is better than other scheduling algorithms, with more obvious advantages.

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.

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.

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.

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.

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

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 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.

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.

The traditional particle swarm optimization algorithm is fast and efficient, but it is easy to fall into a local optimum. An improved PSO algorithm is proposed and applied in 3D path planning of UAV to solve the problem. Improvement methods are described as follows: combining PSO algorithm with genetic algorithm (GA), setting dynamic inertia weight, adding sigmoid function to improve the crossover and mutation probability of genetic algorithm, and changing the selection method. The simulation results show that the improved PSO algorithm solves better route results and is faster and more stable.

This paper presents a hybrid approach based discrete Particle Swarm Optimization (PSO) and chaotic strategies for solving multi-objective task scheduling problem in cloud computing. The main purpose is to allocate the summited tasks to the available resources in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The main challenges faced by Particle Swarm Optimization (PSO) when used to solve scheduling problems are premature convergence and trapping into local optimum. This paper presents an enhanced Particle Swarm Optimization algorithm hybridized with Chaotic Map strategies. The proposed approach is called Enhanced Particle Swarm Optimization based Chaotic Strategies (EPSOCHO) algorithm. Our proposed approach suggests two Chaotic Map strategies: sinusoidal iterator and Lorenz attractor to enhanced PSO algorithm in order to get good convergence and diversity for optimizing the task scheduling in cloud computing. The proposed approach is simulated and implemented in Cloudsim simulator. The performance of the proposed approach is compared with the standard PSO algorithm, the improved PSO algorithm with Longest job to fastest processor (LJFP-PSO), and the improved PSO algorithm with minimum completion time (MCT-PSO) using different sizes of tasks and various benchmark datasets. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.