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

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 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

Based on the polar coordination particle swarm optimization (PPSO), this paper presents a novel method for the robot path planning in dynamic environment. It decomposes the task into a global planning stage and a local planning stage. PPSO algorithm can search for the global optimal path based on static obstacles information. When the robot moves along the optimal global path, an on-line real-time path planning strategy is adopted to avoid dynamic obstacles by means of predicting the future positions of moving obstacles. Simulation experiment shows that the method is more efficient than traditional particle swarm optimization (TPSO) and genetic algorithm (GA) for solving path planning problem. The feasibility and high stability of real-time obstacle avoidance strategy are demonstrated in dynamic environment.

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.

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”.

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 method for the robot path planning in dynamic environment is presented in this paper. Based on the analysis of visual modeling, the reason of premature convergence and diversity loss in PSO is explained, and a new modified algorithm is proposed to ensure the rational flight of every particle dimensional component. Meanwhile, two parameters of particle-distribution-degree and particle dimension-distance are introduced into the proposed algorithm in order to avoiding premature convergence. Simulation results show that it has better ability of finding global optimum, and still is more efficient than traditional particle swarm optimization and genetic algorithm (GA).

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.

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.

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.

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.

In order to improve the efficiency of wind energy, it is necessary to maximum power point tracking(MPPT). Aiming at shortage of the traditional particle swarm optimization algorithm for parameter sensitivity, easily local convergence, particle swarm optimization (PSO) algorithm with variable neighborhood based on saving preponderant velocities is proposed. Firstly, a compact formula for updating particles is researched, which is considering cognitive discipline of individual on its environment; secondly, a mutation operator with a small probability on the velocities which improve particle quality and a random vector generated for the rest velocities are proposed in order to make full use of good information of particle velocities; finally, the method is applied to maximum power point tracking control of the wind power generation system. The simulation results show that the proposed algorithm can achieve maximum power capture of wind power generation system, improve the dynamic response and efficiency compared with the traditional PSO algorithm. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2670

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.