Multi-population Particle Swarm Optimization Research Articles

Multi-population particle swarm optimization algorithm for automatic design of steel frames

Multi-population particle swarm optimization algorithm for automatic design of steel frames

Collaborative Path Planning of Multiple AUVs Based on Adaptive Multi-Population PSO

Collaborative operations of multiple AUVs have been becoming increasingly popular and efficient in underwater tasks of marine applications. Autonomous navigation capability and cooperative control stability of multiple AUVs are crucial and challenging issues in underwater environments. To address the collaborative problem of path planning for multiple AUVs, this paper proposes an adaptive multi-population particle swarm optimization (AMP-PSO). In AMP-PSO, we design a grouping strategy of multi-population and an exchanging mechanism of particles between groups. We separate particles into one leader population and various follower populations according to their fitness. Firstly, in the grouping strategy, particles within the leader population are updated by both the leader population and follower populations so as to keep global optimization, while particles within the follower population are updated by their own group so as to keep local priority. Secondly, in the exchanging mechanism, particles are exchanged between the leader population and follower populations so as to improve multi-population diversity. To accommodate multi-population characteristics, an adaptive parameter configuration is also included to enhance the global search capability, convergence speed, and complex environment adaptability of AMP-PSO. In numerical experiments, we simulate various scenarios of collaborative path planning of multiple AUVs in an underwater environment. The simulation results convincingly demonstrate that AMP-PSO can obtain feasible and optimal path solutions compared to classic PSO and other improved PSO, which enable multiple AUVs to effectively achieve objectives under the conditions of collision avoidance and navigation constraint.

Optimal Trajectory Planning for Robotic Arm Based on Improved Dynamic Multi-Population Particle Swarm Optimization Algorithm

Optimal Trajectory Planning for Robotic Arm Based on Improved Dynamic Multi-Population Particle Swarm Optimization Algorithm

An improved particle swarm optimization based on age factor for multi-AUV cooperative planning

Multi-AUV planning of cooperative path represents the intelligence level of AUV formation system, which has attracted significant attention due to its flexibility and robustness. To enhance the speed and exploration capability of the multi-AUV autonomous path planner, this paper proposes an Age-based Multi-population Particle Swarm Optimization (AMPSO) for three-dimensional path planning of multiple AUVs. Firstly, reverse chaotic mapping is employed to enhance the initial distribution, enabling them to better adapt to the optimization process. In addition, the introduction of multiple populations helps to enhance the diversity within the population, thereby accelerating the convergence rate. Taking into account the search ability, the concept of an "age" coefficient is proposed to optimize the velocity updating formula to reduce the effect of inferior particles. Furthermore, a velocity-independent updating mode is suggested to enhance the population diversity by selecting different update modes. To evaluate the performance of the proposed algorithm, Monte Carlo simulations are conducted for AUV formations across two tracking tasks within the underwater environment established in this paper. The results demonstrate that the proposed algorithm outperforms other commonly used algorithms in terms of both speed and optimality, so the proposed AMPSO can be used for multi-AUV cooperative planning.

A multipopulation particle swarm optimization based on divergent guidance and knowledge transfer for multimodal multiobjective problems

A multipopulation particle swarm optimization based on divergent guidance and knowledge transfer for multimodal multiobjective problems

Deep optimization design of 2D repetitive control systems with saturating actuators: An adaptive multi-population PSO algorithm

This paper presents an optimization design method for a two-dimensional (2D) modified repetitive control system (MRCS) with an anti-windup compensator. Using lifting technology, a 2D hybrid model of the MRCS considering actuator saturation is established to describe the control and learning of the repetitive control. A linear-matrix-inequality (LMI)-based sufficient condition is derived to ensure the stability of the MRCS. Two tuning parameters, the selection of which is critical to the system design, are used in the LMI to adjust the control and learning, and hence the reference-tracking performance. A new cost function, developed through time domain analysis, directly evaluates the control performance of the system without calculating control errors, thus reducing the optimization time. Based on this cost function, an adaptive multi-population particle swarm optimization algorithm is presented to select an optimal pair of tuning parameters in which multiple populations cooperatively search in non-intersecting search intervals. An anti-windup term is added between the low-pass filter and the time delay in the modified repetitive controller to mitigate the undesirable effect of actuator saturation on system performance and stability. Simulations and experiments on the speed control of a rotation control system demonstrate the validity of the approach.

A multi-population particle swarm optimization algorithm with adaptive patterns of movement for the stochastic reconstruction of petroleum fractions

Molecular reconstruction (MR) methods are used to estimate the detailed composition of a hydrocarbon mixture at the molecular level based on experimental data. Stochastic reconstruction (SR) is one of the MR approaches available to this end. Obtaining the solution involves estimating a set of probability density function (PDF) parameters with a global optimization algorithm. However, to the author's knowledge, no other study has thoroughly explored using metaheuristics to improve parameter estimation in this problem. By combining ideas from Chang (2015) and Bonyadi (2018), this study proposes a novel heuristic optimization algorithm named Multi-Population Particle Swarm Optimization with Adaptive Movement Patterns (MPSO-AMP). Since multiple swarms are employed simultaneously with unique movement patterns, the proposed algorithm can render more exact solutions in SR problems than conventional particle swarm optimization (PSO) and other extensions from the literature.

Fault Location of Distribution Network Based on Multi-population Particle Swarm Optimization Algorithm

Fault location of the distribution network is an important direction in the construction of distribution automation. For the problem of slow convergence of intelligent optimization algorithms and easy to fall into local optimality, the multi-population particle swarm optimization algorithm is proposed. The algorithm is compared with single population particle swarm algorithm on IEEE69 node model, it is proved that the new algorithm can find fault location faster. Then the effectiveness of the algorithm in a variety of distribution network fault location scenarios is verified, including single fault location and multiple fault location, and the algorithm can also accurately locate when the input information is distorted which shows good fault tolerance.

Research on Intelligent Pick-Up Route Planning of a Logistics Cycle Automatic Robot

In order to improve the efficiency of a logistics cycle robot picking up goods, a path planning algorithm based on artificial intelligence was proposed. After analyzing the particle swarm optimization algorithm, the particle swarm optimization algorithm is optimized and improved and the path planning of a single robot is obtained. On this basis, a multipopulation particle swarm optimization (CMMPPSO) algorithm is proposed. The results show that the JMPOPSO algorithm is more accurate than the BPSO algorithm and the maximum fitness optimized by the BPSO algorithm is 1.59, while the maximum fitness optimized by the JMPOPSO algorithm is 1.98. The path optimized by the CMMPPSO algorithm based on JMPOPSO is better than that optimized by the CMMPPSO algorithm based on BPSO, shortening by about 25% and shortening the time by about 30. Simulation experiments verify the effectiveness of the CMMPPSO algorithm.

Multipopulation Particle Swarm Optimization Algorithm with Neighborhood Learning

Particle swarm optimization (PSO) algorithm is widely used due to its fewer control parameters and fast convergence speed. However, as its learning strategy is only learning from the global optimal particle, the algorithm has the problem of low accuracy and easily falling into local optimization. In order to overcome this defect, a multipopulation particle swarm optimization algorithm with neighborhood learning (MPNLPSO) is proposed in this article. In MPNLPSO, a small-world network neighborhood learning strategy is proposed to make particles learn from the neighborhood optimal particles instead of only the global optimal particle. Furthermore, the concept of multipopulation cooperation is introduced to balance the ability of global exploration and local exploration. In addition, a dynamic opposition-based learning strategy is proposed to effectively activate the particles in the search stagnation state. Moreover, in order to improve the accuracy of the algorithm and, to some extent, avoid the population diversity decreases too fast, as the searching process continues, Lévy flight is introduced to randomly perturb the particles of historical optimal and neighborhood optimal. To verify the performance of the proposed algorithm experimentally, twenty benchmark functions are solved. Experimental results show that the proposed multipopulation particle swarm optimization algorithm with neighborhood learning presents high efficiency and performance with a certain robustness.

Optimal allocation of product reliability using novel multi-population particle swarm optimization algorithm

Reliability optimal allocation is a critical method in engineering and operation fields to improve and ensure product quality. However, conventional model generally uses the single objective with research and development (R & D) cost or product reliability. Moreover, the popular particle swarm optimization (PSO) algorithm solving the model has insufficient global search ability. In this paper, a novel reliability optimal allocation method is developed based on double-objective model and multi-population PSO (MPPSO) algorithm. The cost function is established considering all the cost related to reliability using analytical hierarchical process (AHP) and ordered weighted averaging (OWA) operator. Multi-population particle swarm optimization algorithm is developed based on information exchange mechanism among subpopulations. In the algorithm, the inertia weight is optimized by chaotic map, and different mutation operations are carried out on the subpopulations. An illustrative example, which implements to computer numerical control (CNC) grinding machine, is presented to illustrate the practicality and application of the proposed method. Finally, the comparison and analysis are implemented to show the advantage of proposed model and MPPSO algorithm.

Multipopulation Particle Swarm Optimization for Evolutionary Multitasking Sparse Unmixing

Recently, the multiobjective evolutionary algorithms (MOEAs) have been designed to cope with the sparse unmixing problem. Due to the excellent performance of MOEAs in solving the NP hard optimization problems, they have also achieved good results for the sparse unmixing problems. However, most of these MOEA-based methods only deal with a single pixel for unmixing and are subjected to low efficiency and are time-consuming. In fact, sparse unmixing can naturally be seen as a multitasking problem when the hyperspectral imagery is clustered into several homogeneous regions, so that evolutionary multitasking can be employed to take advantage of the implicit parallelism from different regions. In this paper, a novel evolutionary multitasking multipopulation particle swarm optimization framework is proposed to solve the hyperspectral sparse unmixing problem. First, we resort to evolutionary multitasking optimization to cluster the hyperspectral image into multiple homogeneous regions, and directly process the entire spectral matrix in multiple regions to avoid dimensional disasters. In addition, we design a novel multipopulation particle swarm optimization method for major evolutionary exploration. Furthermore, an intra-task and inter-task transfer and a local exploration strategy are designed for balancing the exchange of useful information in the multitasking evolutionary process. Experimental results on two benchmark hyperspectral datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art sparse unmixing algorithms.

Electric Power Material Logistics Distribution Based on Improved Multi-Population Particle Swarms

In view of the problem that the information requirements of electric power supplies logistics distribution are increasingly high, this study proposes the research of electric power supplies logistics distribution based on improved multi-population particle swarm. Firstly, the simulated annealing algorithm is introduced in detail, and then the particle swarm optimization (PSO) is improved by using the domain extension method. Then the multi-population particle swarm optimization was carried out through the optimization of coding system, the updating speed formula and the algorithm convergence control. Finally, the optimization effect was verified. Final results show that, this study put forward based on improved particle swarm more population of electricity supplies logistics distribution model has feasibility and suitability, through the optimized parameters and algorithm of the model, to identify the logistics network of electric power supply company the best optimization model, thus reducing power cost of inventory and logistics distribution time, so as to improve the efficiency of logistics distribution.

Grid search based multi-population particle swarm optimization algorithm for multimodal multi-objective optimization

In the multimodal multi-objective optimization problems (MMOPs), there may exist two or multiple equivalent Pareto optimal sets (PS) with the same Pareto Front (PF). The difficulty of solving MMOPs lies in how to locate more equivalent PS in decision space and maintain a promising balance between the diversity of Pareto optimal solutions in decision space and the convergence of Pareto optimal solutions in objective space at the same time. To address these issues, a grid search based multi-population particle swarm optimization algorithm (GSMPSO-MM) is proposed in this paper to handle MMOPs. Multi-populations based on the k-means clustering method is adopted to locate more equivalent PS in decision space, and a grid is applied to explore high-quality solutions in decision space in GSMPSO-MM. The environmental selection operator, including the removing inefficient solutions operator and the updating non-dominated solutions archive, aims to approach the true non-dominated solutions, where the updating non-dominated solution archive is responsible for developing the diverse solutions in both the decision and objective space, simultaneously. Besides, the purpose of removing inefficient solutions with inferior convergence in objective space is to maintain promising convergence solutions in objective space. GSMPSO-MM is compared with seven state-of-the-art algorithms on a well-known MMOPs benchmark function. Experimental results demonstrate the superior performance of our proposed algorithm in solving MMOPs.

A ground-structure-based representation with an element-removal algorithm for truss topology optimization

In this study, a new ground-structure-based representation for truss topology optimization is proposed. The proposed representation employs an algorithm that removes unwanted elements from trusses to obtain the final trusses. These unwanted elements include kinematically unstable elements and useless zero-force elements. Since the element-removal algorithm is used in the translation of representation codes into corresponding trusses, this results in more representation codes in the search space that are mapped into kinematically stable and efficient trusses. Since more representation codes in the search space represent stable and efficient trusses, the strategy increases meaningful competition among representation codes. This remapping strategy alleviates the problem of having large search spaces using ground structures, and encourages faster convergences. To test the effectiveness of the proposed representation, it is used with a simple multi-population particle swarm optimization algorithm to solve several truss topology optimization problems. It is found that the proposed representation can significantly improve the performance of the optimization process.

Block Factor Based Multi-population PSO and Its Application in SVM

In view of the low information transmission efficiency and tendency to fall into local optimal solution of the problems of traditional particle swarm optimization (PSO), an improved bidirectional information transmission block factor based multi-population PSO(BFM-PSO) is proposed. We use the improved BFM-PSO algorithm to optimize SVM penalty factor C and the radius s of the radial basis function. Tests In a number of public data sets show that SVM optimized by the improved BFM-PSO can effectively improve the classification accuracy.

History-Driven Particle Swarm Optimization in dynamic and uncertain environments

Due to dynamic and uncertain nature of many optimization problems in real-world, an algorithm for applying to this environment must be able to track the changing optima over the time continuously. In this paper, we report a novel multi-population particle swarm optimization, which improved its performance by employing an external memory. This algorithm, namely History-Driven Particle Swarm Optimization (HdPSO), uses a BSP tree to store the important information about the landscape during the optimization process. Utilizing this memory, the algorithm can approximate the fitness landscape before actual fitness evaluation for some unsuitable solutions. Furthermore, some new mechanisms are introduced for exclusion and change discovery, which are two of the most important mechanisms for each multi-population optimization algorithm in dynamic environments. The performance of the proposed approach is evaluated on Moving Peaks Benchmark (MPB) and a modified version of it, called MPB with pendulum motion (PMPB). The experimental results and statistical test prove that HdPSO outperforms most of the algorithms in both benchmarks and in different scenarios.

Improved SHEPWM Strategy By The Robustness Optimization Algorithm In Multilevel Inverters With Equal DC Sources

A multilevel Inverters have many advantages such as reduced Harmonics, no need for a transformer, cost and loss reduction. In this paper the multilevel inverter with equal DC sources is used for THD optimization. Different modulation methods have been proposed to control the multilevel inverters. This paper focuses on the Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) technique in multilevel inverter with equal DC sources. In the SHEPWM technique for multilevel inverters, in order to eliminate N-1 non-triplen odd harmonics of the AC-side voltage and to regulate the output line voltage amplitude, N switching angles need to be determined. Therefore a set of N non-linear transcendental equations must be solved to find the desired switching angles for any value of the modulation index. In this paper a new robustness optimization algorithm is presented to elimination of selective low order harmonics (LOH) in the output line voltage of the multilevel inverter based on the Multi Population Particle Swarm Optimization (MPPSO) algorithm. The switching angles are computed by a MPPSO algorithm to eliminate of LOH with high accuracy and determine the fundamental component to the desired value simultaneously. The proposed algorithm can generate stepped voltage waveforms with a wide range of modulation indices. The performance of the proposed algorithm for a seven-level cascaded H-bridge converter, based on simulation studies, is evaluated. MATLAB software is used for programming and simulation.

Performing of the MPPSO Optimization Algorithm to Minimize Line Voltage THD of 3-Level Inverter

Different modulation methods have been proposed to control the multilevel inverters. This paper focuses on the staircase waveform modulation for 3-level inverters with low switching frequencies. In this paper a novel solution along with a new robustness optimization algorithm is presented to minimization of Total Harmonic Distortion (THD) of the output line voltage of the inverter based on the Multi Population Particle Swarm Optimization (MPPSO). The MPPSO algorithm has a lot of advantages compared with a general optimization algorithm such as Genetic algorithm (GA). A comparison has been drawn between aforementioned techniques related to optimization. A new expression of fitness function is presented to optimization of Line Voltage THD (LVTHD) of 3-level Inverter. The advantages of fundamental frequency harmonic minimization and swarm intelligence are combined to improve the quality of output line voltage. The validity of the proposed method is proved through various simulation results by MATLAB software.

A Multipopulation PSO Based Memetic Algorithm for Permutation Flow Shop Scheduling

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP.