Adaptive Quantum-behaved Particle Swarm Optimization Research Articles

Soft Sensor Modeling of Self-Organizing Interval Type-2 Fuzzy Neural Network Based on Adaptive Quantum-Behaved Particle Swarm Optimization Algorithm

Soft Sensor Modeling of Self-Organizing Interval Type-2 Fuzzy Neural Network Based on Adaptive Quantum-Behaved Particle Swarm Optimization Algorithm

Quantum behaved particle swarm optimization of inbound process in an automated warehouse

The inbound process is of great importance in enhancing the efficiency of automated warehouse operations. This study investigates an optimization problem on the inbound warehouse process by coordinating multiple resources in a type of automated warehouse system, i.e., Shuttle-Based Storage and Retrieval System (SBS/RS). A mixed-integer programming model is formulated to determine the assignment decisions of the pallets towards three types of the resources in the SBS/RS (i.e., forklifts, lifts and shuttles), the sequencing & timing decisions of these three types of resources for transporting the pallets. Then, a novel solution method, called Adaptive Quantum behaved Particle Swarm Optimization (AQPSO) algorithm, is designed to solve the proposed model. The introduction of the quantum mechanism prevents the algorithm from falling into a local minimum. The integration of the adaptive adjustment strategy improves the efficiency of the algorithm by dynamically adjusting the search scale. The efficiency of the proposed algorithm is verified by comparative experiments that use the CPLEX solver and the basic particle swarm optimization algorithm as rivals. The experimental results indicate that the proposed algorithm have an advantage in the solution quality and the computing time. A series of sensitivity analyses are also conducted to bring out some managerial insights. For example, it is beneficial to reduce energy consumption by adjusting the relative velocity and power of the three types of equipment, and setting the best ratios of shuttles to forklifts.

A Novel ANFIS-AQPSO-GA-Based Online Correction Measurement Method for Cooperative Localization

To solve the effect of unreliable acoustic range measurements in cooperative localization of autonomous underwater vehicles, a novel localization method is proposed by using a hybrid metaheuristic algorithm that consists of the adaptive quantum-behaved particle swarm optimization (AQPSO) algorithm combined with the genetic algorithm (GA) to train the adaptive neurofuzzy inference system (ANFIS) model. The method can predict the acoustic ranging errors online, then correct the measurements, and improve the accuracy and stability of the cooperative localization system. We first tested the optimization ability of AQPSO-GA with an open-source dataset, and then, a large number of simulation experiments are performed with actual data collected in lake-water trials to fully compare and analyze the performance of various cooperative localization methods. The experimental results show that, compared with the cooperative localization method based on the cubature Kalman filter, the proposed method can reduce the localization error by 90% and effectively guarantee the stability of the cooperative localization system.

Research of active vibration control optimal disposition based on CMQPSO

The adaptive quantum particle swarm optimization algorithm based on cloud model and the multi-island genetic algorithm [15] have obvious advantages in convergence speed to solve the sensor optimization problem, and can effectively achieve global optimization. Due to the installation of sensors and actuators, the electromechanical coupling coefficient of intelligent structures is changed, which affects the vibration energy of structures. In this paper, the reserved energy index of structural vibration control system is taken as the objective optimization function. The position, number, length and control gain of sensors and actuators of active vibration control system are optimized. The adaptive Quantum-behaved Particle Swarm Optimization algorithm in cloud model(CMQPSO) is used as the optimization strategy, and the cantilever beam is taken as an example. This approach is verified its effectiveness and feasibility. It is found that excellent optimization results are obtained.

An application of interval differential equation on a production inventory model with interval‐valued demand via center‐radius optimization technique and particle swarm optimization

Due to the fluctuation of market economy and uncertainty of customers' demand, it is quite difficult to develop an appropriate inventory model under uncertain situations. To overcome this difficulty, a food production model with preservation technology and credit-linked demand under default risk of capital in uncertain environment is developed with the help of parametric approach and interval mathematics. In this proposed model, all the inventory parameters, including demand rate, production rate and deterioration rate are considered as interval-valued. Because of the consideration of demand rate, production rate as well as deterioration rate as interval-valued, to represent the proposed model mathematically, the interval differential equations have been used. Solving these differential equations by using parametric approach, all the cost components and the corresponding average profit are obtained in the form of intervals. Therefore, the optimization problem of this model becomes interval-optimization problem. Then, to solve the interval-optimization problem, the center-radius optimization technique is established with the help of interval order relations. With the help of this technique, the interval-optimization problem is converted into crisp problem and then it is solved numerically by using different variants (Gaussian Quantum-behaved Particle Swarm Optimization, Weighted Quantum-behaved Particle Swarm Optimization, and Adaptive Quantum-behaved Particle Swarm Optimization) of Quantum-behaved Particle Swarm Optimization technique. Some real-life problems are considered and solved to justify the validity of the proposed model. The same model is also analyzed in crisp environment to verify the result of interval environment. Finally, the sensitivity analyses of both crisp and interval environments are performed separately with respect to the different system parameters.

Accurate blind extraction of arbitrary unknown phase shifts by an improved quantum-behaved particle swarm optimization in generalized phase-shifting interferometry

An optimized method of accurately extracting the arbitrary unknown and unequal phase steps in phase-shifting interferometry is proposed. The approximate phase steps are first calculated based on the statistical nature of the diffraction field, and the mutated adaptive quantum-behaved particle swarm optimization is used to further extract the arbitrary unknown and unequal phase steps. We improve the mutated adaptive quantum-behaved particle swarm optimization by adding mutation operator with Gaussian probability distribution, thereby increasing the population diversity. The developed method here is fast, highly accurate, and can effectively overcomes the “sawtooth-solution phenomenon” often encountered in traditional direct search approach. We demonstrate the speed and quality of the solution by measuring the transmissive object with the four-step phase-shifting interference method, as is verified by both the computer simulations and optical experiments.

Path Planning Method for AUV Docking Based on Adaptive Quantum-Behaved Particle Swarm Optimization

AUV docking requires the platform to have both wide range cruising and accurate operating abilities, to against challenges of ocean currents, obstacles, and constraints. This paper proposed an evolutionary-based method, for the purpose of docking path optimization. First, the ocean environment and constraints are analyzed and modeled. Next, the control points are designed to satisfy the model constraints. Then, the adaptive law and quantum behavior are introduced in particle swarm optimization (PSO), to achieve global time-optimization. Finally, the proposed approach is evaluated via Monte-Carlo trials, which demonstrates a significant improvement with respect to the state-of-the-art approaches.

Optimal design of short fiber Bragg gratings with triangular spectrum

Based on the mutated adaptive quantum-behaved particle swarm optimization (MAQPSO) algorithm and the discrete layer peeling (DLP) algorithm, we propose a method to design triangular-spectrum fiber Bragg gratings (TS-FBGs). In this method, the DLP algorithm is used to generate an appropriate initial range of the index modulation, and the MAQPSO technique is applied to optimize the index modulation within this range. The quantum-behaved particle swarm optimization algorithm with mutation operation can improve the search performance of this algorithm. We purposely use a linear weighting factor for the fitness value function in view of the linear edge triangular-spectrum. This dramatically accelerates the convergence in the numerical implementation. Using the proposed method, a 0.2 nm bandwidth TS-FBG is designed and the performance is shown to be better than previously reported results in the literature. Then, a 2.5 nm bandwidth TS-FBG is reconstructed; it is indicated that a short grating length can be used to realize the TS-FBG with chirp-free structure so that complex phase modulation is avoided in fabrication. Finally, a triple-channel TS-FBG with sampled-free structure is successfully designed and optimized. We also present an analysis on the tolerance of the design method over possible fabrication error.