Multi-objective Quantum-behaved Particle Swarm Optimization Research Articles

An RSU Deployment Scheme for Vehicle-Infrastructure Cooperated Autonomous Driving

For autonomous driving vehicles, there are currently some issues, such as limited environmental awareness and locally optimal decision-making. To increase the capacity of autonomous cars’ environmental awareness, computation, decision-making, control, and execution, intelligent roads must be constructed, and vehicle–infrastructure cooperative technology must be used. The Roadside unit (RSU) deployment, a critical component of vehicle–infrastructure cooperative autonomous driving, has a direct impact on network performance, operation effects, and control accuracy. The current RSU deployment mostly uses the large-spacing and low-density concept because of the expensive installation and maintenance costs, which can accomplish the macroscopic and long-term communication functions but fall short of precision vehicle control. Given these challenges, this paper begins with the specific requirements to control intelligent vehicles in the cooperative vehicle–infrastructure environment. An RSU deployment scheme, based on the improved multi-objective quantum-behaved particle swarm optimization (MOQPSO) algorithm, is proposed. This RSU deployment scheme was based on the maximum coverage with time threshold problem (MCTTP), with the goal of minimizing the number of RSUs and maximizing vehicle coverage of communication and control services. Finally, utilizing the independently created open simulation platform (OSP) simulation system, the model and algorithm’s viability and effectiveness were assessed on the Nguyen–Dupuis road network. The findings demonstrate that the suggested RSU deployment scheme can enhance network performance and control the precision of vehicle–infrastructure coordination, and can serve as a general guide for the deployment of RSUs in the same application situation.

DMO-QPSO: A Multi-Objective Quantum-Behaved Particle Swarm Optimization Algorithm Based on Decomposition with Diversity Control

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has shown remarkable effectiveness in solving multi-objective problems (MOPs). In this paper, we integrate the quantum-behaved particle swarm optimization (QPSO) algorithm with the MOEA/D framework in order to make the QPSO be able to solve MOPs effectively, with the advantage of the QPSO being fully used. We also employ a diversity controlling mechanism to avoid the premature convergence especially at the later stage of the search process, and thus further improve the performance of our proposed algorithm. In addition, we introduce a number of nondominated solutions to generate the global best for guiding other particles in the swarm. Experiments are conducted to compare the proposed algorithm, DMO-QPSO, with four multi-objective particle swarm optimization algorithms and one multi-objective evolutionary algorithm on 15 test functions, including both bi-objective and tri-objective problems. The results show that the performance of the proposed DMO-QPSO is better than other five algorithms in solving most of these test problems. Moreover, we further study the impact of two different decomposition approaches, i.e., the penalty-based boundary intersection (PBI) and Tchebycheff (TCH) approaches, as well as the polynomial mutation operator on the algorithmic performance of DMO-QPSO.

Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm

The application of multiple UAVs in complicated tasks has been widely explored in recent years. Due to the advantages of flexibility, cheapness and consistence, the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV. Accordingly, several constraints should be satisfied to realize the efficient cooperation, such as special time-window, variant equipment, specified execution sequence. Hence, a proper task allocation in UAVs is the crucial point for the final success. The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics. To this end, a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints. In addition, four optimization objectives: completion time, target reward, UAV damage, and total range, are introduced to evaluate various allocation plans. Subsequently, to efficiently solve the multi-objective optimization problem, an improved multi-objective quantum-behaved particle swarm optimization (IMOQPSO) algorithm is proposed. During this algorithm, a modified solution evaluation method is designed to guide algorithmic evolution; both the convergence and distribution of particles are considered comprehensively; and boundary solutions which may produce some special allocation plans are preserved. Moreover, adaptive parameter control and mixed update mechanism are also introduced in this algorithm. Finally, both the proposed model and algorithm are verified by simulation experiments.

Multiobjective Long-Term Generation Scheduling of Cascade Hydroelectricity System Using a Quantum-Behaved Particle Swarm Optimization Based on Decomposition

Multiobjective long-term generation scheduling (MOLTGS) plays a vital role in coordinating the contradiction between the generation and reliability of cascade hydroelectricity system (CHS). In this paper, a multiobjective quantum-behaved particle swarm optimization based on decomposition (MOQPSO/D) is presented to solve the MOLTGS problem of maximizing total hydroelectricity generation and firm hydroelectricity output. In MOQPSO/D, the improved logistic map is adopted to initialize the population in the feasible space, where various equality and inequality constraints are handled by a constraint handling method based on conversion and repair strategies. An improved Tchebycheff decomposition with a modified generator of direction vectors, a modified QPSO operator and normal cloud mutation are the key components of MOQPSO/D in addressing the nonlinearity and complexity of MOLTGS. The feasibility and effectiveness of MOQPSO/D are verified first on numerical experiments of benchmark instances and then on real engineering examples of the Three Gorges (TG) and Gezhouba (GZB) plants within three representative years. The results show that MOQPSO/D can not only perform better robustness, convergence and diversity performance than other seven competitors but also output a group of Pareto optimal schemes within a reasonable amount of time. So, this paper provides a new effective alternative to solve the MOLTGS problem of CHS when considering both generation and reliability objectives.

Modeling and optimization for laser cladding via multi-objective quantum-behaved particle swarm optimization algorithm

The process parameters of laser cladding have significant effects on the quality of the laser clad coating. Generally, optimizing one property usual lead to deterioration in some other performances. In this study, the influence of key parameters to preparing the CoCr1.5FeNiNb0.5 coating, namely laser beam power, scanning speed and defocus, on the dilution and residual stress were systematically researched. The polynomial model for the parameters on dilution and residual stress was designed by response surface methodology and the effect of each parameter on the responses was investigated. Then the quadratic models were used as the constraint functions and multi-objective quantum-behaved particle swarm optimization was applied to find the minimum dilution and residual stress. At last, the optimal process parameters were predicted by this algorithm and the high entropy alloy coating was manufactured. The CoCr1.5FeNiNb0.5 HEA coating consisted of FCC and Laves phase.

An Improved Multi-Objective Quantum-Behaved Particle Swarm Optimization for Railway Freight Transportation Routing Design

With the development of railway transportation, the railway transportation enterprises expand their freight transportation from station-to-station transportation to door-to-door transportation, which makes the routing design more complicated. The existing classical optimization algorithms are difficult to meet the needs of practical applications. Therefore, the paper introduces an Improved Multi-objective Quantum-behaved Particle Swarm Optimization algorithm (IMOQPSO). Then based on the continuous coding for the Railway Freight Transportation Routing Design, the proposed improved algorithm was applied to solve the problem to verify the performance of algorithm. Finally, the paper compared the performance of Improved Multi-objective Quantum-behaved Particle Swarm Optimization algorithm with other four continuous multi-objective swarm intelligence algorithms. The results shown that the proposed algorithm obtained the best Pareto front which is closer to the real Pareto front of Railway Freight Transportation Routing Design. Hence, the proposed Improved Multi-objective Quantum-behaved Particle Swarm Optimization algorithm can provide support for the railway transport enterprises routing design decisions to some extent.

An Improved Multiobjective Quantum-Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism

Although multiobjective particle swarm optimization (MOPSO) has good performance in solving multiobjective optimization problems, how to obtain more accurate solutions as well as improve the distribution of the solutions set is still a challenge. In this paper, to improve the convergence performance of MOPSO, an improved multiobjective quantum-behaved particle swarm optimization based on double search strategy and circular transposon mechanism (MOQPSO-DSCT) is proposed. On one hand, to solve the problem of the dramatic diversity reduction of the solutions set in later iterations due to the single search pattern used in quantum-behaved particle swarm optimization (QPSO), the double search strategy is proposed in MOQPSO-DSCT. The particles mainly learn from their personal best position in earlier iterations and then the particles mainly learn from the global best position in later iterations to balance the exploration and exploitation ability of the swarm. Moreover, to alleviate the problem of the swarm converging to local minima during the local search, an improved attractor construction mechanism based on opposition-based learning is introduced to further search a better position locally as a new attractor for each particle. On the other hand, to improve the accuracy of the solutions set, the circular transposon mechanism is introduced into the external archive to improve the communication ability of the particles, which could guide the population toward the true Pareto front (PF). The proposed algorithm could generate a set of more accurate and well-distributed solutions compared to the traditional MOPSO. Finally, the experiments on a set of benchmark test functions have verified that the proposed algorithm has better convergence performance than some state-of-the-art multiobjective optimization algorithms.

Operating Parameters Optimization for the Aluminum Electrolysis Process Using an Improved Quantum-Behaved Particle Swarm Algorithm

Improvements in the production and energy consumption of the aluminum electrolysis process (AEP) directly depend on the operating parameters of the electrolytic cell. To balance the conflicting goals of efficiency and productivity with reduced energy consumption and emissions, AEP operating parameter optimization is formulated as a constrained multiobjective optimization problem with competing objectives of current efficiency and cell voltage. Then, the improved multiobjective quantum-behaved particle swarm optimization (IMQPSO) algorithm is proposed. The application of an adaptive opposition-based learning strategy and a piecewise Gauss mutation operator can increase the diversity of the population and enhance the global search ability of the IMQPSO. To expand the creativity of the particles, two iterative methods of the mean best position with weighting and the attractor position are redesigned. Experimental analyses are conducted for the benchmark problems and a real case to verify the effectiveness of the proposed method.

Multi-Task Multi-View Learning Based on Cooperative Multi-Objective Optimization

Traditional multi-task multi-view (MTMV) models work under the single-objective learning framework and cannot incorporate too many regularization terms, which are primarily attributed to the utilization of the conventional numerical optimization methods. To this end, a cooperative multi-objective MTMV (CMO-MTMV) learning method is proposed in this paper. In CMO-MTMV, the MTMV problem is formulated as a multi-objective optimization problem. Compared with the existing single-objective MTMV learning methods, the proposed CMO-MTMV method integrates more relations, including task–task, view–view, instance–instance, and feature–feature relations as multiple objectives. An effective cooperative multi-objective quantum-behaved particle swarm optimization (CMOQPSO) algorithm is further developed to solve the multi-objective optimization problem. The integration of a multi-swarm scheme and a local communication strategy in CMOQPSO renders this algorithm efficient. The experimental results verify the superiority of the proposed CMO-MTMV method compared with the several state-of-the-art machine-learning methods.

Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling

With increasing attention paid to energy and environment in recent years, the hydrothermal scheduling considering economic and environmental objectives is becoming one of the most important optimization problems in power system. With two competing objectives and a set of operation constraints, the economic environmental hydrothermal scheduling problem is classified as a typical multi-objective nonlinear constrained optimization problem. Thus, in order to efficiently resolve this problem, the multi-objective quantum-behaved particle swarm optimization (MOQPSO) is presented in this paper. In MOQPSO, the elite archive set is adopted to conserve Pareto optimal solutions and provide multiple evolutionary directions for individuals, while the neighborhood searching and chaotic mutation strategies are used to enhance the search capability and diversity of population. Furthermore, a novel constraint handling method is designed to adjust the constraint violation of hydro and thermal plants, respectively. In order to verify its effectiveness, the MOQPSO is applied to a classical hydrothermal system with four hydropower plants and three thermal plants. The simulations show that the proposed method has competitive performance compared with several traditional methods.

MOQPSO-D/S for Air and Missile Defense WTA Problem under Uncertainty

Aiming at the shortcomings of single objective optimization for solving weapon target assignment (WTA) and the existing multiobjective optimization based WTA method having problems being applied in air and missile defense combat under uncertainty, a fuzzy multiobjective programming based WTA method was proposed to enhance the adaptability of WTA decision to the changes of battlefield situation. Firstly, a multiobjective quantum-behaved particle swarm optimization with double/single-well (MOQPSO-D/S) algorithm was proposed by adopting the double/single-well based position update method, the hybrid random mutation method, and the two-stage based guider particles selection method. Secondly, a fuzzy multiobjective programming WTA model was constructed with consideration of air and missile defense combat’s characteristics. And, the uncertain WTA model was equivalently clarified based on the necessity degree principle of uncertainty theory. Thirdly, with particles encoding and illegal particles adjusting, the MOQPSO-D/S algorithm was adopted to solve the fuzzy multiobjective programming based WTA model. Finally, example simulation was conducted, and the result shows that the WTA model constructed is rational and MOQPSO-D/S algorithm is efficient.

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.

Multi-objective quantum-behaved particle swarm optimization algorithm with double-potential well and share-learning

For improving the convergence accuracy and diversity of multi-objective optimization algorithm a multi-objective quantum-behaved particle swarm optimization algorithm with double-potential well and share-learning is proposed, which overcomes the deficiency of particles readily gathering in identical solutions. The two local attractors, inside and outside, are introduced to construct the particle locations updating model, using the quantum tunneling and transition effects in double-potential well model. In this way, the particle moves to the solution sparseness region in later evolution stage, so as to avoid gathering in the single local attractor and escape from local optimum. Therefore the optimization accuracy of the algorithm is improved. The share-learning strategy is adopted to extend the search range of particles and increase the diversity of solutions. The problem of easily converging to boundary solutions in quantum-behaved particle swarm optimization algorithm could be avoided. Simulation results show that the proposed algorithm makes excellent performance in optimization accuracy, convergence, diversity, and distribution, compared with three existing algorithms. Moreover, the proposed algorithm can hold on better convergence and distribution performance when handling high-dimensional multi-objective problems.

A Novel Multiobjective Quantum-Behaved Particle Swarm Optimization Based on the Ring Model

A Novel Multiobjective Quantum-Behaved Particle Swarm Optimization Based on the Ring Model

A Novel Multiobjective Quantum-Behaved Particle Swarm Optimization Based on the Ring Model

Due to its fast convergence and population-based nature, particle swarm optimization (PSO) has been widely applied to address the multiobjective optimization problems (MOPs). However, the classical PSO has been proved to be not a global search algorithm. Therefore, there may exist the problem of not being able to converge to global optima in the multiobjective PSO-based algorithms. In this paper, making full use of the global convergence property of quantum-behaved particle swarm optimization (QPSO), a novel multiobjective QPSO algorithm based on the ring model is proposed. Based on the ring model, the position-update strategy is improved to address MOPs. The employment of a novel communication mechanism between particles effectively slows down the descent speed of the swarm diversity. Moreover, the searching ability is further improved by adjusting the position of local attractor. Experiment results show that the proposed algorithm is highly competitive on both convergence and diversity in solving the MOPs. In addition, the advantage becomes even more obvious with the number of objectives increasing.

Overlapping community detection through an improved multi-objective quantum-behaved particle swarm optimization

Community detection is one of the most important problems in the field of complex networks in recent years. The majority of present algorithms only find disjoint communities, however, community often overlap to some extent in many real-world networks. In this paper, an improved multi-objective quantum-behaved particle swarm optimization (IMOQPSO) based on spectral-clustering is proposed to detect the overlapping community structure in complex networks. Firstly, the line graph of the graph modeling the network is formed, and a spectral method is employed to extract the spectral information of the line graph. Secondly, IMOQPSO is employed to solve the multi-objective optimization problem so as to resolve the separated community structure in the line graph which corresponding to the overlapping community structure in the graph presenting the network. Finally, a fine-tuning strategy is adopted to improve the accuracy of community detection. The experiments on both synthetic and real-world networks demonstrate our method achieves cover results which fit the real situation in an even better fashion.