Research on cluster attack mission planning of multi USVs based on DAM-BBOPSO algorithm

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

Particle swarm optimization (PSO) algorithm has shown some important advantages, but it has a tendency to get stuck in a local optimal solution. This paper presents an improved particle swarm optimization (IPSO) algorithm to improve the performance of traditional PSO, and it is applied to solve the flow shop scheduling problem and compared with GA and traditional PSO algorithm. Experimental results indicate that the IPSO algorithm for FSSP improves the search performance and shows the effectiveness of the algorithm to solve optimization problems.

The structural size parameters (SSP) of the beam pumping unit (BPU) are crucial for kinematic analysis and other related technologies. However, early equipment often lacked data, and measuring a large number of SSPs for the BPU is costly and inefficient. To enable the automatic recognition of SSPs and reduce labor and resource investments, this paper proposes a method for recognizing the SSPs of the BPU. A kinematic model for calculating the crank angle based on beam tilt angle and SSPs is established, along with an SSP recognition model. Based on the traditional particle swarm optimization (PSO) algorithm, a comprehensive enhanced particle swarm optimization (CEPSO) algorithm is designed to solve the model. Latin hypercube sampling (LHS) is used to obtain the initial values of the SSPs, which are then substituted into the kinematic model using the measured beam tilt angle. The algorithm aims to minimize the average absolute error between the measured and calculated crank angles. The CEPSO algorithm iteratively updates the SSP values, and when the error converges to a minimum, the SSPs are obtained. Experimental results demonstrate the effectiveness of the model and the algorithm. The parameter recognition error of the CEPSO algorithm is within 5%, significantly outperforming traditional PSO. This method provides a new technological approach for measuring SSPs.

A multi‐unmanned aerial vehicle (UAV) offers the advantages of flexibility, safety, and efficiency with respect to odor source localization. When the traditional particle swarm optimization (PSO) algorithm is used to locate the odor source, the search efficiency of the UAVs is reduced owing to unplanned search routes, and local extreme points and internal collisions may occur, making it impossible to quickly locate the odor source. In response to these problems, this paper proposes an improved particle swarm optimization (IPSO) algorithm, which combines the PSO with the Zigzag algorithm to preplan the search routes and improve the efficiency of the UAVs to search the plume. In addition, the distance between the UAVs is used as an adjustment factor to adaptively adjust the inertia weights of the particles to prevent the UAVs from falling into a local optimum during the plume‐tracking stage and further improve the positioning efficiency. Finally, the PSO combined with the artificial potential field algorithm is used to avoid the internal collision of the UAVs when tracking the plume. The algorithm is verified through simulations and physical experiments. The results show that the IPSO significantly improves the efficiency and can achieve rapid and effective location of the odor source, which verifies the effectiveness and applicability of the algorithm.

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.

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.

Under partial shading condition or imbalanced temperature distribution, the power-voltage characteristic curve of photovoltaic (PV) array exhibits complicated multiple peaks. Traditional maximum power point tracking (MPPT) method is usually easy to trap into local extreme power point. To solve this issue, this paper proposes a novel MPPT method using quantum-behaved particle swarm optimization (QPSO) algorithm. This algorithm adopts detect-search strategy based on voltage closed-loop control, making voltage particles search photovoltaic characteristic curves in quantum-behaved paths. By setting a modulation strategy of reunion and separation factor and stopping criterion, the proposed QPSO algorithm improves the precision of stable state, and eliminates power oscillation under stability condition. Experimental results show that QPSO algorithm has good performance with less parameters, superior global optimization ability, favorable stability and better adaptability compared with traditional particle swarm optimization (PSO) algorithm in PV MPPT.

The prestressed concrete–steel hybrid (PCSH) wind turbine tower, characterized by replacing the lower part of the traditional full-height steel tube wind turbine tower with a prestressed concrete (PC) segment, provides a potential alterative solution to transport difficulties and risks associated with traditional steel towers in mountainous areas. This paper proposes an optimization approach with a parallel updated particle swarm optimization (PUPSO) algorithm which aims at minimizing the objective function of the levelized cost of energy (LCOE) of the PCSH wind turbine towers in a life cycle perspective which represents the direct investments, labor costs, machinery costs, and the maintenance costs. Based on the constraints required by relevant specifications and industry standards, the geometry of a PCSH wind turbine tower for a 2 MW wind turbine is optimized using the proposed approach. The dimensions of the PCSH wind turbine tower are treated as optimization variables in the PUPSO algorithm. Results show that the optimized PCSH wind turbine tower can be an economic alternative for wind farms with lower LCOE requirements. In addition, compared with the traditional particle swarm optimization (PSO) algorithm and UPSO algorithm, the proposed PUPSO algorithm can enhance the optimization computation efficiency by about 60–110%.

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.

A novel orthogonal multi-swarm cooperative particle swarm optimization (PSO) algorithm with a particle trajectory knowledge base is presented in this paper. Different from the traditional PSO algorithms and other variants of PSO, the proposed orthogonal multi-swarm cooperative PSO algorithm not only introduces an orthogonal initialization mechanism and a particle trajectory knowledge base for multi-dimensional optimization problems, but also conceives a new adaptive cooperation mechanism to accomplish the information interaction among swarms and particles. Experiments are conducted on a set of benchmark functions, and the results show its better performance compared with traditional PSO algorithm in aspects of convergence, computational efficiency and avoiding premature convergence.

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”.

Cable-driven parallel robots (CDPRs) have been widely used in engineering fields because of their significant advantages including high load-bearing capacity, large workspace, and low inertia. However, the impact of convergence speed and solution accuracy of optimization approaches on optimal performances can become a key issue when it comes to the optimal design of CDPR applied to large storage space. An adaptive adjustment inertia weight particle swarm optimization (AAIWPSO) algorithm is proposed for the multi-objective optimal design of CDPR. The kinematic and static models of CDPR are established based on the principle of virtual work. Subsequently, two performance indices including workspace and dexterity are derived. A multi-objective optimization model is established based on performance indices. The AAIWPSO algorithm introduces an adaptive adjustment inertia weight to improve the convergence efficiency and accuracy of traditional particle swarm optimization (PSO) algorithm. Numerical examples demonstrate that final convergence values of the objective function by the AAIWPSO algorithm can almost be 14∼20% and 19∼40% higher than those by the PSO algorithm and genetic algorithm (GA) for the optimal design of CDPR with different configurations and masses of end-effectors, respectively.

This paper proposes an algorithm for estimating the true boundary values of non-isothermal control heat equations based on binary sampling information from the boundary values, utilizing the Sparrow Search Algorithm (SSA) and Support Vector Regression (SVR) parameter optimization algorithm (hereinafter referred to as SSA-SVR). The performance of the proposed SSA-SVR algorithm was compared with the standalone SVR algorithm and the traditional Particle Swarm Optimization (PSO) algorithm combined with SVR parameter optimization (referred to as PSO-SVR) in estimating boundary values. The results indicate that the proposed SSA-SVR algorithm possesses advantages in estimating boundary values, with faster speed, lower error, and greater stability. This enables more accurate reconstruction of the boundary values, allowing the reconstructed signals to be utilized for other related synchronization controls.

Gas emission quantity may forecast the quantity of gas inside the coal, which has important significance for predicting the outburst of gas, but the problem always has not been well solved. Traditional Particle swarm optimization (PSO) algorithm lacks the ability to track the optimal solution while the fitness function changes. An improved algorithm named Time Variant PSO (TVPSO) was proposed to track the optimal solution online. Then it was used to choose the parameters of Least Square Support Vector Machine (LSSVM), which could avoid the man-made blindness and enhance the efficiency of online forecasting. The TVPSO-LSSVM method is based on the minimum structure risk of SVM and the globally optimizing ability of TVPSO to forecast continuously the gas emission quantity of the working face. The method was applied to solve the problem of nonlinear chaos time series prediction. Result shows that the method satisfies the need of online forecasting.

MIMO radar array synthesis using QPSO with normal distributed contraction-expansion factor