A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

Tu, Shanshan; Rehman, Obaid Ur; Rehman, Sadaqat Ur; Ullah, Shafi; Waqas, Muhammad; Zhu, Ran

doi:10.1109/access.2020.2968980

Cited by 39 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These algorithms were able to solve NPproblems such as the 0-1 knapsack problem and Travelling-Salesperson Problem better than many traditional algorithms [17]. Other applications of the QIPSO were in the design of a superconducting magnetic-energy-storage system [46], and in solving J30 of the RCPSP [47]; it was effective in reducing the makespan. Another quantum-inspired meta-heuristics, the quantum-inspired differential-evolution algorithm (QIDEA), was better than the classical methods in solving problems such as the N-Queens Problem [48] and the deep-belief network [49].…”

Section: B Quantum-inspired Evolutionary Algorithmsmentioning

confidence: 99%

Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling

et al. 2021

View full text Add to dashboard Cite

The Resource-Constrained Project-Scheduling Problem (RCPSP) is an NP-hard problem which can be found in many research domains. The optimal solution of the RCPSP problems requires a balance between exploration/exploitation and diversification/intensification. With this in mind, quantuminspired evolutionary algorithms' ability to improve the population and quality of solutions, this work investigates the performance of a quantum-inspired genetic algorithm (QIGA), which has been adapted to work with RCPSPs. The proposed QIGA possesses the same structure as classical genetic algorithms, but the initial and updated populations are implemented using quantum gates and quantum superposition, bearing in mind the adaptation of such operators to fit with discrete problems. This work aims to solve RCPSPs using the QIGA to investigate the influence of the various quantum parameters in the proposed algorithm, such as the quantum population size, different options for the quantum gates and re-combination to use in the QIGA. The well-known PSPLIB benchmark instances of J30, J60 and J120 activities are used to test the effectiveness and performance of the proposed QIGA. It is apparent from the results that quantum mutation, quantum crossover and representation of quantum superposition using quantum gates enhances population diversity. The QIGA is also found to outperform many other evolutionary algorithms. INDEX TERMS Quantum-inspired evolutionary algorithm, resource-constrained project-scheduling problems, NP-hard problems, genetic algorithm, quantum gates, quantum superposition.

show abstract

Section: B Quantum-inspired Evolutionary Algorithmsmentioning

confidence: 99%

Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Simulations are carried out for solving optimization problems and it demonstrates improved performance. Shanshan Tu et al [42] proposed updating of crossover parameter to improve the quantum PSO performance and global search abilities. An approach proposed in [43] combines QPSO with Cauchy mutation operator (QPSO-CD) which adds extended capabilities for global hunt.…”

Section: Related Workmentioning

confidence: 99%

“…We have also compared performance of EDCQPSO with PSO and its versions. These algorithms are; PSO [15], PDQPSO [40], QPSO -CD [42], CLQPSO [43], and CSQPSO [44]. All these algorithms are simulated in the same environment on the IEEE CEC2019 benchmark functions by setting parameters the same as that of the original paper.…”

Section: B Comparisons Of the Edcqpso With Other Versions Of Psomentioning

confidence: 99%

“…In the second step, it computes the location of the normal sensor node. For performance analysis, the results of EDCQPSO are compared with PDQPSO [40], CLQPSO [42], QPSO-CD [43] and CSQPSO [44]. With random deployment of sensor nodes in localization area, average localization error (ALE) is calculated as a standard statistical metric and given by…”

Section: Edcqpso For Iot Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

et al. 2021

View full text Add to dashboard Cite

An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process's continuance and adding proactive and reactive features. The proposed algorithm's performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application.

show abstract

“…Swarm intelligence is a kind of heuristic algorithm with random population as evolutionary unit. It has been widely used in recent years for a variety of complex problems in industry [16][17][18][19][20]. Solving for UAV deployment optimization using such algorithms begins with designing the UAV coding scheme.…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence Application to UAV Aided IoT Data Acquisition Deployment Optimization

Chen

Mohamed

et al. 2020

IEEE Access

View full text Add to dashboard Cite

It is feasible and safe to use unmanned aerial vehicle (UAV) as the data collection platform of the Internet of things (IoT). In order to save the energy loss of the platform and make the UAV perform the collection work effectively, it is necessary to optimize the deployment of UAV. The objective problem is to minimize the sum of the lost energy of UAV and the loss of data transmission of Internet of things devices. The key to solving the problem is to calculate the location of the docking points and the number of docking points when the UAV is working to collect data. This paper proposes a coding scheme based on swarm intelligence optimization, which encapsulates the docking position of UAV into a dimension, so the number of docking points to be calculated is the dimension number of optimization objective. This problem is considered as a dynamic dimension optimization problem. Each individual in swarm intelligence algorithm is a solution. When adjusting the dimension, the best individual is added or deleted to achieve dynamic search in the evolutionary process. Collaborative search among multiple individuals can improve the local optimal limit of search to a certain extent. Finally, the validity of the swarm intelligence-based coding approach is verified by simulation experiments under seven different IoT device distribution scenarios. INDEX TERMS UAV deployment optimization, Internet of things data collection, Swarm intelligence, Dynamic dimension optimization.

show abstract

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

Cited by 39 publications

References 25 publications

Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling

Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling

Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

Swarm Intelligence Application to UAV Aided IoT Data Acquisition Deployment Optimization

Contact Info

Product

Resources

About