Hybrid Quantum-behaved Particle Swarm Optimization Research Articles

A hybrid quantum-behaved particle swarm optimization solution to non-convex economic load dispatch with multiple fuel types and valve-point effects

Economic dispatch problems (EDPs) can be reduced to non-convex constrained optimization problems, and most of the population-based algorithms are prone to have problems of premature and falling into local optimum when solving EDPs. Therefore, this paper proposes a hybrid quantum-behaved particle swarm optimization (HQPSO) algorithm to alleviate the above problems. In the HQPSO, the Solis and Wets local search method is used to enhance the local search ability of the QPSO so that the algorithm can find solutions that is close to optimal when the constraints are met, and two evolution operators are proposed and incorporated for the purpose of making a better balance between local search and global search abilities at the later search stage. The performance comparison is made among the HQPSO and the other ten population-based random search methods under two different experimental configurations and four different power systems in terms of solution quality, robustness, and convergence property. The experimental results show that the HQPSO improves the convergence properties of the QPSO and finally obtains the best total generation cost without violating any constraints. In addition, the HQPSO outperforms all the other algorithms on 7 cases of all 8 experimental cases in terms of global best position and mean position, which verifies the effectiveness of the algorithm.

Dynamic cyclic kitting part-feeding scheduling for mixed-model assembly line by a hybrid quantum-behaved particle swarm optimization

PurposeThe purpose of this paper is to cut down energy consumption and eliminate production waste on mixed-model assembly lines. Therefore, a supermarket integrated dynamic cyclic kitting system with the application of electric vehicles (EVs) is introduced. The system resorts to just-in-time (JIT) and segmented sub-line assignment strategies, with the objectives of minimizing line-side inventory and energy consumption.Design/methodology/approachHybrid opposition-based learning and variable neighborhood search (HOVMQPSO), a multi-objective meta-heuristics algorithm based on quantum particle swarm optimization is proposed, which hybridizes opposition-based learning methodology as well as a variable neighborhood search mechanism. Such algorithm extends the search space and is capable of obtaining more high-quality solutions.FindingsComputational experiments demonstrated the outstanding performance of HOVQMPSO in solving the proposed part-feeding problem over the two benchmark algorithms non-dominated sorting genetic algorithm-II and quantum-behaved multi-objective particle swarm optimization. Additionally, using modified real-life assembly data, case studies are carried out, which imply HOVQMPSO of having good stability and great competitiveness in scheduling problems.Research limitations/implicationsThe feeding problem is based on static settings in a stable manufacturing system with determined material requirements, without considering the occurrence of uncertain incidents. Current study contributes to assembly line feeding with EV assignment and could be modified to allow cooperation between EVs.Originality/valueThe dynamic cyclic kitting problem with sub-line assignment applying EVs and supermarkets is solved by an innovative HOVMQPSO, providing both novel part-feeding strategy and effective intelligent algorithm for industrial engineering.

Quantum-behaved particle swarm optimization with dynamic grouping searching strategy

The quantum-behaved particle swarm optimization (QPSO) algorithm, a variant of particle swarm optimization (PSO), has been proven to be an effective tool to solve various of optimization problems. However, like other PSO variants, it often suffers a premature convergence, especially when solving complex optimization problems. Considering this issue, this paper proposes a hybrid QPSO with dynamic grouping searching strategy, named QPSO-DGS. During the search process, the particle swarm is dynamically grouped into two subpopulations, which are assigned to implement the exploration and exploitation search, respectively. In each subpopulation, a comprehensive learning strategy is used for each particle to adjust its personal best position with a certain probability. Besides, a modified opposition-based computation is employed to improve the swarm diversity. The experimental comparison is conducted between the QPSO-DGS and other seven state-of-art PSO variants on the CEC’2013 test suit. The experimental results show that QPSO-DGS has a promising performance in terms of the solution accuracy and the convergence speed on the majority of these test functions, and especially on multimodal problems.

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle (UUV) in underwater combat, this paper proposes a battlefield ambush scene with UUV considering ocean current. Firstly, by establishing these mathematical models of ocean current environment, target movement, and sonar detection, the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively. Then, based on the Hybrid Quantum-behaved Particle Swarm Optimization (HQPSO) algorithm, the path with the highest target search probability is found. Finally, through simulation calculations, the influence of different UUV parameters and target parameters on the target search probability is analyzed, and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated, and the optimal search path scheme is obtained. The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

A Hybrid Quantum-Behaved Particle Swarm Optimization Algorithm for Solving Inverse Scattering Problems

A hybrid inversion approach based on the quantum-behaved particle swarm optimization (QPSO) method is presented in this article to solve electromagnetic inverse problems. Inverse scattering problems are ill-posed and are often transformed into optimization problems by defining a suitable cost function, which can be minimized by evolutionary algorithms. This article is aimed at assessing the effectiveness of a customized QPSO in reconstructing 2-D dielectric scatterers. The bottleneck that restricts the application of the evolutionary algorithm in large-scale optimization problems is its computational cost. In this article, the diffraction tomographic image is used as an initial guess for the QPSO. Moreover, a weighted mean best position according to the fitness values of the particles is introduced to expand the contribution of excellent particles on population evolution. This hybrid approach, denoted as HQPSO, makes full use of the complementary advantages of linear reconstruction algorithms and stochastic optimization algorithms and is, thus, able to ensure accuracy and improve computational efficiency. Numerical experiments for different types of dielectric objects are performed with synthetic and experimental inverse-scattering data.

QPSO-CD: quantum-behaved particle swarm optimization algorithm with Cauchy distribution

Motivated by the particle swarm optimization (PSO) and quantum computing theory, we have presented a quantum variant of PSO (QPSO) mutated with Cauchy operator and natural selection mechanism (QPSO-CD) from evolutionary computations. The performance of proposed hybrid quantum-behaved particle swarm optimization with Cauchy distribution (QPSO-CD) is investigated and compared with its counterparts based on a set of benchmark problems. Moreover, QPSO-CD is employed in well-studied constrained engineering problems to investigate its applicability. Further, the correctness and time complexity of QPSO-CD are analyzed and compared with the classical PSO. It has been proved that QPSO-CD handles such real-life problems efficiently and can attain superior solutions in most of the problems. The experimental results shown that QPSO associated with Cauchy distribution and natural selection strategy outperforms other variants in context of stability and convergence.

Multireservoir system operation optimization by hybrid quantum-behaved particle swarm optimization and heuristic constraint handling technique

Generally, the multireservoir system operation optimization (MSOO) is classified as a large-scale and multi-stage optimization problem with a set of complex constraints. Here, the goal of MSOO is chosen to determine the optimal operation policy of all the reservoirs to minimize the energy deficit of electrical system. In order to effectively resolve this problem, a hybrid quantum-behaved particle swarm optimization (HQPSO) is developed in this study. In HQPSO, the external archive set conserving the elite particles is used to provide multiple search directions for various agents; the modified evolution strategy and mutation operator are used to enhance the convergence rate of the swarm; while a practical heuristic constraint handling method is employed to address the complex physical constraints imposed on all the hydropower reservoirs. The simulations of 12 benchmark functions indicate that HQPSO can produce better results than several existing evolutionary methods. Then, two multireservoir systems are chosen to verify the performance of the proposed method. The results show that compared with the conventional methods, the HQPSO method can obtain scheduling results with better performances in reducing the energy deficits of power system. Hence, this paper provides an effective tool for the complex multireservoir system operation problem.

An Enriched Prediction Intervals Construction Method with Hybrid Intelligent Optimization

Prediction intervals (PIs), within which future observations of time series are expected to fall, are a powerful method for uncertainty modeling and forecasting. This paper presents the construction of optimal PIs using an enriched extreme learning machine (ELM)-based method. While quality evaluation indices for PIs on reliability and sharpness of prediction results have been defined in the literature, this paper proposes a new PIs evaluation index, robustness, which focuses on the forecasting error. Combined with the above three indices, a more comprehensive objective function is then formed for optimal PIs construction. The paper also proposes an efficient hybrid quantum-behaved particle swarm optimization method with bacterial foraging mechanism to optimize the parameters in the ELM model. The effectiveness of the additional robustness index and the proposed improved ELM approach in determining higher quality PIs is demonstrated by applying them to PIs constructions for the cases of prediction in different datasets.

OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

A hybrid Improved Quantum-behaved Particle Swarm Optimization–Simplex method (IQPSOS) to solve power system load flow problems

This study proposes a new approach, based on a hybrid algorithm combining of Improved Quantum-behaved Particle Swarm Optimization (IQPSO) and simplex algorithms. The Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is the main optimizer of algorithm, which can give a good direction to the optimal global region and Nelder Mead Simplex method (NM) which is used as a local search to fine tune the obtained solution from QPSO. The proposed improved hybrid QPSO algorithm is tested on several benchmark functions and performed better than particle swarm optimization (PSO), QPSO and weighted QPSO (WQPSO). To assess the effectiveness and feasibility of the proposed method on real problems, it is used for solving the power system load flow problems and demonstrated by different standard and ill-conditioned test systems including IEEE 14, 30 and 57 buses test systems, and compared with the conventional Newton–Raphson (NR) method, PSO and some versions of QPSO algorithms. Furthermore, the proposed hybrid algorithm is proposed for solving load flow problems with considering the reactive limits at generation buses. Simulation results prove the robustness and better convergence of IQPSOS under normal and critical conditions, when conventional load flow methods fail.

Implementing Trustworthy Service Flow Based on Improved HQPSO Algorithm

Service-oriented Architecture (SOA) is the new paradigm for software development, the key point of which is the trustworthiness of the service and the service flow. The construction problem of trustworthy service flow can be formalized as the constrained optimization problem, and which is solved by hybrid quantum-behaved particle swarm optimization algorithm with the good results. By reason of the existence of implicit parallelism of hybrid quantum-behaved particle swarm optimization algorithm itself, an improved hybrid quantum-behaved particle swarm optimization algorithm with parallelization is proposed in the paper, and which is used to solving the construction problem of trustworthy service flow. The experimental data show that the improved hybrid quantum-behaved particle swarm optimization algorithm with parallelization can receive the better result in solving the construction problem of trustworthy service flow, compared with the hybrid quantum-behaved particle swarm optimization algorithm.

Implementing Trustworthy Service Flow Based on Improved HQPSO Algorithm

Service-oriented Architecture (SOA) is the new paradigm for software development, the key point of which is the trustworthiness of the service and the service flow. The construction problem of trustworthy service flow can be formalized as the constrained optimization problem, and which is solved by hybrid quantum-behaved particle swarm optimization algorithm with the good results. By reason of the existence of implicit parallelism of hybrid quantum-behaved particle swarm optimization algorithm itself, an improved hybrid quantum-behaved particle swarm optimization algorithm with parallelization is proposed in the paper, and which is used to solving the construction problem of trustworthy service flow. The experimental data show that the improved hybrid quantum-behaved particle swarm optimization algorithm with parallelization can receive the better result in solving the construction problem of trustworthy service flow, compared with the hybrid quantum-behaved particle swarm optimization algorithm.

A Thread Pool Scheduling Optimization Method of Real-time System

To deal with concurrent requests, thread pool technology is wildly used in multi-task real-time systems, which chief demand is timeout avoidance. In order to reduce request timeout ratio, least square support vector machine(LS-SVM) algorithm which parame- ters optimization based on hybrid quantum-behaved particle swarm optimization(HQPSO) was applied to estimate the execution time of requests. Also, a scheduling priority algorithm of thread pool was designed based on the estimation results. In performance test, a node of wireless sensor network is implemented to test the request timeout ratios of HQPSO LS-SVM based thread pool and other ones in different states. Test result indicates that HQPSO LS-SVM based thread pool has remarkable superiority of timeout avoidance while the thread amount of thread pool is set properly.

Cultural algorithm-based quantum-behaved particle swarm optimization

A hybrid quantum-behaved particle swarm optimization (QPSO) based on cultural algorithm (CA), which we call cultural QPSO, is proposed. Although QPSO is a promising algorithm for many optimization problems, it is apt to lose the diversity of the swarm in the later period of the search and prematurely converges to the local optimum. Inspired by the structure of human society, this paper uses the CA model to diversify the QPSO population and improve the QPSO's performance. In this model, the swarm is divided into two sub-swarms: the common particle and the elite particle sub-swarm. If a particle comes from a common sub-swarm, it will evolve according to the QPSO method, and during the evolvement, it will be affected not only by the other common particles but also by the elites. For the elites, the differential evolution (DE) method is adopted for evolvement. After each generation, the elites will be re-elected from the whole swarm according to fitness values. The simulation results on benchmark functions demonstrate that cultural QPSO outperforms the original QPSO for many problems.