Binary Quantum-behaved Particle Swarm Optimization Research Articles

An adaptive binary quantum-behaved particle swarm optimization algorithm for the multidimensional knapsack problem

The multidimensional knapsack problem (MKP) is a classical combinatorial optimization problem with wide real-life applications. Binary quantum-behaved particle swarm optimization (BQPSO) algorithm is a popular heuristic algorithm used in binary optimization. While BQPSO exhibits strong global search capabilities, it is still prone to local optima due to particle aggregation. To address this issue, an adaptive BQPSO (ABQPSO) algorithm is proposed to solve the MKP efficiently. A hybrid encoding population initialization scheme is employed, leveraging specific knowledge of MKP to increase population diversity and improve search efficiency. Furthermore, ABQPSO uses a mapping strategy that converts continuous values into discrete values based on the average position of particles. An adaptive repair operator considering two pseudo-utility ratios is introduced to enable particles to explore different feasible regions, which dynamically adjusts current pseudo-utility ratios based on changes in the global best solution. A local search method is applied to guide particles towards convergence to the optimum. A local sparseness degree measurement and a diversity mechanism are utilized to avoid local optima. To evaluate the effectiveness of ABQPSO, it is compared against ten state-of-the-art algorithms using 168 MKP benchmark instances of varying scales. Experimental results reveal that ABQPSO outperforms the comparison algorithms, especially for large-scale problems, demonstrating better solution accuracy.

An improved binary quantum-behaved particle swarm optimization algorithm for knapsack problems

The 0-1 knapsack problem is a typical NP-hard combinatorial optimization problem that is difficult to solve efficiently based on traditional optimization approaches. Binary quantum-behaved particle swarm optimization (BQPSO) algorithm is a simple yet efficient discrete optimization approach since it guarantees global search. However, BQPSO suffers from local optimum and slow convergence problems due to aggregation among particles, limiting its performance. To address this problem, an improved BQPSO (IBQPSO) algorithm is proposed to effectively solve the 0-1 knapsack problem. First, to address the discretization issues in existing QPSO algorithms, a mapping strategy based on the average position of all particles is introduced. Then, the transfer function is used to discretize the local attractors into binary vectors for crossover operations, which can guide individuals in the discrete space to the optimum. In addition, a new repair method is employed to address infeasible solutions, and a diversity maintenance mechanism is developed in IBQPSO to alleviate the local optima problem. The proposed algorithm is compared with ten state-of-the-art heuristic algorithms on a set of 0-1 knapsack problems with different scales. Experimental results show that IBQPSO has obvious superiority over the other ten algorithms in terms of convergence speed and search performance.

Analysis and application of functional connectivity in synchronic hybrid mental tasks for brain-computer interface

As a new type of human–computer interaction technology, brain-computer interface (BCI) systems can translate the electroencephalography (EEG) signals as control commands. In our previous study, the synchronous hybrid mental tasks have been proposed with higher classification accuracy. In this study, to analyze the superiority of synchronous hybrid mental tasks, the causal networks of synchronous hybrid mental tasks and single mental tasks are compared. The causal networks are based on functional connectivity, which is calculated by partial directed coherence. The networks of the synchronous hybrid mental tasks present different interaction patterns. To further improve the classification accuracy of EEG signals, a feature extraction method combining the causal network and common spatial pattern (CSP) is proposed. After combining as mixed feature vectors, they are selected by binary quantum-behaved particle swarm optimization. The optimal feature subsets are classified by extreme learning machine. This average result is 3.1% and 9.5% higher than CSP and causal flow, respectively. The superiority of hybrid experimental paradigm can be studied by the causal network, and the performance of BCIs is also improved.

Channel selection in motor imaginary-based brain-computer interfaces: a particle swarm optimization algorithm.

The number of electrode channels in a brain-computer interface affects not only its classification performance, but also its convenience in practical applications. However, an effective method for determining the number of channels has not yet been established for motor imagery-based brain-computer interfaces. This paper proposes a novel evolutionary search algorithm, binary quantum-behaved particle swarm optimization, for channel selection, which is implemented in a wrapping manner, coupling common spatial pattern for feature extraction, and support vector machine for classification. The fitness function of binary quantum-behaved particle swarm optimization is defined as the weighted sum of classification error rate and relative number of channels. The classification performance of the binary quantum-behaved particle swarm optimization-based common spatial pattern was evaluated on an electroencephalograph data set and an electrocorticography data set. It was subsequently compared with that of other three common spatial pattern methods: using the channels selected by binary particle swarm optimization, all channels in raw data sets, and channels selected manually. Experimental results showed that the proposed binary quantum-behaved particle swarm optimization-based common spatial pattern method outperformed the other three common spatial pattern methods, significantly decreasing the classification error rate and number of channels, as compared to the common spatial pattern method using whole channels in raw data sets. The proposed method can significantly improve the practicability and convenience of a motor imagery-based brain-computer interface system.

Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection

Purpose – The purpose of this paper is to improve the performance of E-nose in the detection of wound infection. Feature extraction and selection methods have a strong impact on the performance of pattern classification of electronic nose (E-nose). A new hybrid feature matrix construction method and multi-objective binary quantum-behaved particle swarm optimization (BQPSO) have been proposed for feature extraction and selection of sensor array. Design/methodology/approach – A hybrid feature matrix constructed by maximum value and wavelet coefficients is proposed to realize feature extraction. Multi-objective BQPSO whose fitness function contains classification accuracy and a number of selected sensors is used for feature selection. Quantum-behaved particle swarm optimization (QPSO) is used for synchronization optimization of selected features and parameter of classifier. Radical basis function (RBF) network is used for classification. Findings – E-nose obtains the highest classification accuracy when the maximum value and db 5 wavelet coefficients are extracted as the hybrid features and only six sensors are selected for classification. All results make it clear that the proposed method is an ideal feature extraction and selection method of E-nose in the detection of wound infection. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring, and is beneficial for realizing the clinical application of E-nose.

Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine.

This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

Energy-Efficient Resource Allocation for Heterogeneous Services in OFDMA Downlink Networks: Systematic Perspective

In the area of energy-efficient (EE) resource allocation, only limited work has been done on consideration of both transmitter and receiver energy consumption. In this paper, we propose a novel EE resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered. In addition, different quality-of-service (QoS) requirements, including minimum-rate guarantee service and best effort service, are taken into account. The time slot, subcarrier (frequency), and power-allocation policies are jointly considered to optimize system EE. With all these considerations, the EE resource-allocation problem is formulated as a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we tackle this problem in three steps. First, for a given power allocation, we obtain the time-frequency resource unit (RU) allocation policy via binary quantum-behaved particle swarm optimization (BQPSO) algorithm. Second, under the assumption of known RU allocation, we transform the original optimization problem into an equivalent concave optimization problem and obtain the optimal power-allocation policy through the Lagrange dual approach. Third, an iteration algorithm is developed to obtain the joint time-frequency power-resource-allocation strategy. We validate the convergence and effectiveness of the proposed scheme by extensive simulations.

Network Anomaly Detection Based on BQPSO-BN Algorithm

As cloud computing becomes popular, intrusion detection has been focused again, since huge amount of network attacks have increased the requirement of efficient network intrusion detection techniques. Currently, lots of methods are used to solve this issue, but lower detection rate of these original models cannot satisfy complex Internet environment. In this paper, we propose a novel intrusion detection model–Bayesian Network-based binary quantum-behaved particle swarm optimization (BQPSO-BN). Since the classical QPSO algorithm only operates in continuous and real-valued space, and the problem of Bayesian networks learning is in discrete space, we redefine the position vector and the distance between two positions, and adjust the iterative equations of QPSO to binary search space. Experiment results with KDD’99 dataset show that BQPSO-BN is an efficient and effective algorithm and has better convergence speed compared with BPSO-BN and GA-BN models.

Network anomaly detection based on BQPSO-BN algorithm

AbstractAs cloud computing becomes popular, intrusion detection has been focused again, since huge amount of network attacks have increased the requirement of efficient network intrusion detection techniques. Currently, lots of methods are used to solve this issue, but lower detection rate of these original models cannot satisfy complex Internet environment. In this paper, we propose a novel intrusion detection model–Bayesian Network-based binary quantum-behaved particle swarm optimization (BQPSO-BN). Since the classical QPSO algorithm only operates in continuous and real-valued space, and the problem of Bayesian networks learning is in discrete space, we redefine the position vector and the distance between two positions, and adjust the iterative equations of QPSO to binary search space. Experiment results with KDD’99 dataset show that BQPSO-BN is an efficient and effective algorithm and has better convergence speed compared with BPSO-BN and GA-BN models.

A Modified Binary Quantum-Behaved Particle Swarm Optimization Algorithm with Bit Crossover Operator

Particle swarm optimization algorithm with binary encoding (BQPSO) is an intelligent algorithm which is the binary version of particle swarm optimization (QPSO) and can solve the problem in discrete space. This paper analyzes BQPSO, especially discusses the crossover method of bits in algorithm. The paper proposes that the crossover operation of particles should be crossed from bit to bit randomly in algorithm. And the new bit crossover operator is used in computing the coordinate of the local attractor to increasing the diversity of populations and improves the global searching ability of algorithm, and then a modified BQPSO algorithm is proposed. The proposed new algorithm is tested on several functions and compared with BPSO. The experiment results show the superiority of BQPSO