Comprehensive Learning Particle Swarm Optimization Algorithm Research Articles

Scheduling of twin automatic stacking cranes and automated guided vehicle considering buffer mode on automated container terminal

To improve the efficiency of automatic stacking crane (ASC) and automated guided vehicle (AGV) scheduling in synchronized import and export mode at automated container terminal (ACT), with the objective of minimizing the maximum ASC completion time and AGV wait time, a twin ASCs and AGV scheduling model is established, which considers both the size of handshake area and buffer, and the location constraints of handshake area. Longest transport time priority (LTTP) and traversal priority (TP) heuristics strategies are designed to adjust the execution order of ASCs to reduce the wait time of ASCs. Based on the comprehensive learning particle swarm optimization algorithm (CLPSO), the discrete coding CLPSO algorithm (CLDPSO) is developed by designed permutation coding for ASC execution order and integer coding for AGV numbering, and the convergence speed is improved by dynamically adjusting the weight parameters. The experimental results show that increasing the handshake area and buffer size can reduce the fitness value by 3.64 % and 5.58 % on average respectively, the selected optimal handshake area location can reduce the fitness value by 3.51 % on average compared with the conventional location, the TP strategy can reduce the waiting time of ASC by up to 16.89 %. Compared to the PSO and DPSO algorithms, the fitness value of CLDPSO can reduce the fitness value by 13.83 % and 7.80 %, and the computation time is reduced by 10.67 % and 7.43 % on average, respectively.

Sequential most probable point update combining Gaussian process and comprehensive learning PSO for structural reliability-based design optimization

This paper proposes an efficient reliability-based design optimization (RBDO) method that advantageously decouples comprehensive learning particle swarm optimization (CLPSO) algorithm with Gaussian process regression (GPR) model, termed as GPR-CLPSO. The method iteratively performs the CLPSO with deterministic parameters based on the most probable point (MPP) underpinning limit-state functions (LSFs) iteratively updated by the active learning reliability evaluation process. The GPR model approximates, from the design data given by CLPSO, the spectrum of LSFs under random parameters, and hence enables a significant computational reduction of Monte-Carlo simulations (MCSs) for failure probability approximation. The expected feasibility function is maximized using the CLPSO code to systematically refine the GPR model by adaptively adding new (intelligent) learning points in the region with high-reliability sensitivity leading to the more accurate prediction of failure probability. A novel inverse MCS constraint boundary method is developed to redefine the MPP assigned for the CLPSO algorithm in determining the new optimal design. The method efficiently leverages the decoupling approach, whilst significantly alleviating computing efforts, to quickly and accurately capture the optimal RBDO design. The resulting failure probability well satisfies the allowable limit. Four RBDO examples are provided to illustrate applications and robustness of the proposed decoupling GPR-CLPSO approach.

Multimodal Optimization Algorithm for Torque Ripple Reduction in Synchronous Reluctance Motors

An accurate analytical model is adopted to estimate the torque ripple of a synchronous reluctance motor (SynRM). Desired behavior of the torque ripple functionin this motor is obtained by changing the angles of one and two flux barriers per pole (FBs) in the rotor. The torque ripple function of the SynRM serves as the multiple and close local optima. By identifying the behavior of this function, a comprehensive learning particle swarm optimization (CLPSO) algorithm (typically applied in solving multimodal functions), is adopted to reduce the torque ripple. The results indicate that compared to PSO (i.e. global optimization algorithms) the CLPSO algorithm is more efficient in torque ripple reduction and finding more local optima. Among the available optimal solutions with four FBs per pole, a sample is selected for motor construction. Finite element analysis and laboratory tests are performed to validate the results.

Adaptive Fault Diagnosis of Motors Using Comprehensive Learning Particle Swarm Optimizer with Fuzzy Petri Net

This study proposes and applies a comprehensive learning particle swarm optimization (CLPSO) fuzzy Petri net (FPN) algorithm, which is based on the CLPSO algorithm and FPN, to the fault diagnosis of a complex motor. First, the transition confidence is replaced by a Gaussian function to deal with the uncertainty of fault propagation. Then, according to the Petri net principle, a competition operator is introduced to improve the matrix reasoning. Finally, a CLPSO-FPN model for motor fault diagnosis is established based on the motor failure mechanism and fault characteristics. The CLPSO algorithm is used to generate the system parameters for fault diagnosis and to improve the adaptability and accuracy of fault diagnosis. This study considers the example of a three-phase asynchronous motor. The results show that the proposed algorithm can diagnose faults in this motor with satisfactory adaptability and accuracy compared with the traditional FPN algorithm. By establishing the system model, the fault propagation process of motors can be accurately and intuitively expressed, thus improving the fault treatment and equipment maintenance of motors.

PID Parameters Optimization of Pressure and Phear Testing Controller Based on CLPSO Algorithm

This paper presents an improved algorithm which is Comprehensive Learning Particle Swarm Optimization(CLPSO).CLPSO utilizes a new learning strategy have achieve the goal to accelerate the convergence of the classical PSO.CLPSO algorithm is effective to optimize PID controller’s parameter.The simulation results show its better performance than traditional ways in the PID parameter optimization of a hydraulic system controller.

A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems

Particle swarm optimization (PSO) is predominately used to find solutions for continuous optimization problems. As the operators of PSO are originally designed in an <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -dimensional continuous space, the advancement of using PSO to find solutions in a discrete space is at a slow pace. In this paper, a novel set-based PSO (S-PSO) method for the solutions of some combinatorial optimization problems (COPs) in discrete space is presented. The proposed S-PSO features the following characteristics. First, it is based on using a set-based representation scheme that enables S-PSO to characterize the discrete search space of COPs. Second, the candidate solution and velocity are defined as a crisp set, and a set with possibilities, respectively. All arithmetic operators in the velocity and position updating rules used in the original PSO are replaced by the operators and procedures defined on crisp sets, and sets with possibilities in S-PSO. The S-PSO method can thus follow a similar structure to the original PSO for searching in a discrete space. Based on the proposed S-PSO method, most of the existing PSO variants, such as the global version PSO, the local version PSO with different topologies, and the comprehensive learning PSO (CLPSO), can be extended to their corresponding discrete versions. These discrete PSO versions based on S-PSO are tested on two famous COPs: the traveling salesman problem and the multidimensional knapsack problem. Experimental results show that the discrete version of the CLPSO algorithm based on S-PSO is promising.