Quantum Particle Swarm Optimization Method Research Articles

Quantum particle swarm optimization algorithm based on diversity migration strategy

Particle swarm optimization algorithm has been successfully applied to solve practical optimization problems due to its simplicity and efficiency. However, the traditional particle swarm optimization algorithm has inferior search performance in complicated high-dimensional optimization issues and is prone to falling into local optima. To address these problems, a new migration mechanism is introduced and a quantum particle swarm optimization method based on diversity migration is proposed. The strategy can capture different ranges of particles in the population, and the selection of migrating individuals depends not only on their fitness values but is also influenced by the positions within the population. The individual with the minimal average Hamming distance in the population can indicate the direction of iterative population optimization. After comparing the fitness values and the average Hamming distance between particles, the particles deviating from the central range of the population are replaced. The performance of the proposed algorithm is investigated under seven different sets of benchmark function optimization problems in the CEC2020 single-objective boundary-constrained optimization competition, and is compared with those of several other representative optimization algorithms. The quantum particle swarm optimization algorithm based on diversity migration strategy outperforms other typical optimization algorithms. Moreover, the proposed algorithm is convergent and stable.

Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm

INTRODUCTION: With the large-scale integration of new energy into the grid, the safety and reliability of the power grid have been severely tested. The optimized configuration of micro power systems is a key element of intelligent power systems, playing a crucial role in reducing energy consumption and environmental pollution.
 OBJECTIVES: a power grid optimization scheduling model is proposed that comprehensively considers the issues of power grid operating costs and environmental governance costs
 METHODS: Using quantum particle swarm optimization method to optimize the objective function with the lowest system operating cost and the lowest environmental governance cost. In order to improve the search ability of the algorithm and eliminate the problem of easily getting stuck in local optima, the Levy flight strategy is introduced, and the variable weight method is used to update the particle factor to improve the optimization ability of the algorithm.
 RESULTS: The simulation results show that the improved quantum particle swarm optimization algorithm has strong optimization ability, and the scheduling model proposed in this paper can achieve good scheduling results in different scheduling tasks.
 CONCLUSION: （1）The improved particle swarm algorithm, in comparison to itspredecessor, boasts a greater degree of optimization accuracy, aswifter convergence rate, and the capability to avoid the algorithm'sdescent into the local optimal solution at a later stage of the process. （2）The proposed model can effectively reduce users’ electricity costs and environmental pollution, and promote the optimized operation of microgrids.

Reduction of side lobes in the entire azimuth plane in a planar elliptical array antenna

SummaryA novel synthesis method to reduce the maximum side lobe in the entire azimuth plane and for achieving a desired value of directivity of a concentric elliptical array antenna is addressed in this paper. Three well‐performing meta‐heuristic optimization algorithms, teaching learning‐based optimization (TLBO), symbiotic organism search (SOS), and quantum particle swarm optimization (QPSO), are employed to optimize the cost function and to obtain the desired radiation pattern. Different simulation‐based experiments are performed to obtain the best possible set of angular positions and the distribution of isotropic antennas across the ellipses by rigorously maintaining non‐overlapping constraints. The major benefit of the proposed design is the deprecation of interference in all phi planes. The statistical analysis of the simulation results demonstrate perceptible improvement compared with that of a uniformly spaced concentric elliptical array and hence show the efficiency of the proposed algorithms. In both simulation studies, the results obtained using TLBO, SOS, and QPSO are compared. Analysis of the results exhibits that for the stated objective, the TLBO‐ and SOS‐based side lobe reduction approach performs superior to QPSO method. Proposed multi‐objective research work could lead to reduce payload and can simultaneously achieve isoflux coverage in entire azimuth plane on the earth surface which can essentially be utilized in (Low Earth Orbit) LEO and (Geostationary Earth Orbit) GEO satellites applications.

Bigradient neural network-based quantum particle swarm optimization for blind source separation

<p><span id="docs-internal-guid-df1e3816-7fff-2396-860a-693df6c8ad2e"><span>An independent component analysis (ICA) is one of the solutions of a blind source separation problem. ICA is a statistical approach that depends on the statistical properties of the mixed signals. The purpose of the ICA method is to demix the mixed source signals (observation signals) and rcovering those signals. The abbreviation of the problem is that the ICA needs for optimizing by using one of the optimization approaches as swarm intelligent, neural neworks, and genetic algorithms. This paper presents a hybrid method to optimize the ICA method by using the quantum particle swarm optimization method (QPSO) to optimize the Bigradient neural network method that applies to separate mixed signals and recover sources signals. The results of an implement this work prove that this method gave good results comparing with other methods such as the Bigradient neural network and the QPSO method, based on several evaluation measures as signal-to-noise ratio, signal-to-distortion ratio, absolute value correlation coefficient, and the computation time.</span></span></p>

Designing a standalone wind-diesel-CAES hybrid energy system by using a scenario-based bi-level programming method

Compressed air energy storage (CAES) systems are promising for the application of a standalone hybrid system. This study adopts a scenario-based bi-level programming method to design a standalone hybrid system that mainly contains wind turbines, diesel generators (DGs), and a CAES system. The demand response is considered as a deferrable load. The uncertainties on the wind power outputs and load demand are modeled using scenario generation and reduction techniques. The generated scenarios are used in a bi-level programming model for designing the hybrid energy system (HES). The model is composed of an outer planning layer and an inner operation layer. The outer layer optimizes the size of each component in the HES using a quantum particle swarm optimization (QPSO) method with the objective of minimizing daily total costs including daily investment costs and daily operating costs. On the other hand, the inner layer optimizes the operational strategies of the HES using a sequential quadratic programming method with the objective of minimizing the total operating costs, including the generation and emission costs of the DGs and the degradation cost of the CAES system. The well-established HES tool HOMER is used to validate the results obtained by the developed and adopted models. The results indicate that 1) the QPSO method performs better than the particle swarm optimization and genetic algorithm methods. 2) The results obtained by the scenario-based bi-level programming method have an average similarity of approximately 97%, which is very high compared to that of the results obtained by HOMER.

Apply the quantum particle swarm optimization for the K-traveling repairman problem

This paper deals with an optimization problem encountered in the field of transport of goods and services, namely the K-traveling repairman problem (K-TRP). This problem is a generalization of the metric traveling repairman problem (TRP) which is also known as the deliveryman problem and the minimum latency problem. The K-TRP and the related problems can be considered as “customer-centric” routing problems because the objectif function consists in minimize the sum of the waiting times of customers rather than the vehicles travel time. These problems are also considered as problems with “cumulative costs.” In this paper, we propose a quantum particle swarm optimization (QPSO) method to solve the K-TRP. In order to avoid the violations of problem constraints, the proposed approach also incorporates a heuristic repair operator that uses problem-specific knowledge instead of the penalty function technique commonly used for constrained problem. To the best of our knowledge, this study is the first to report on the application of the QPSO method to the K-TRP. Experimental results obtained on sets of the Capacitated Vehicle Routing Problem test instances, of up to 100 customers, available in the literature clearly demonstrate the competitiveness of the proposed method compared to the commercial MIP solver CPLEX 12.5 of IBM-ILOG and the state-of-the-art heuristic methods. The results also demonstrate that the proposed approach was able to reach more optimal solutions and to improve 5 best known solutions in a short and reasonable computation time.

A quantum particle swarm optimization for the 0–1 generalized knapsack sharing problem

This study proposes a new hybrid heuristic approach that combines the quantum particle swarm optimization (QPSO) technique with a local search phase to solve the binary generalized knapsack sharing problem (GKSP). The approach also incorporates a heuristic repair operator that uses problem-specific knowledge instead of the penalty function technique commonly used for constrained problems. This study is the first to report on the application of the QPSO method to the GKSP. The efficiency of our proposed approach was tested on a large set of instances, and the results were compared to those produced by the commercial mixed integer programming solver CPLEX 12.5 of IBM-ILOG. The Experimental results demonstrated the good performance of the QPSO in solving the GKSP.

Reactive Power Optimization Using Quantum Particle Swarm Optimization

Problem statement: The problem of controlling a power system is not an easy task; it is subjected to various constraints. There are at risks of voltage instability problems due to highly stressed operating conditions caused by increased load demand and other constraints in the power system network. Approach: This study presents the implementation of Quantum Particle Swarm Optimization (QPSO) in solving the Reactive Power Optimization (RPO) problem. The main aim of this algorithm is the minimization of the real power loss and to improvise the voltage in the system. In this new algorithm, the particles were made to perform studies on itself and also the best ones in the system. Results: The implementations of QPSO were carried on modified IEEE 14 bus system for obtaining solution to the reactive power optimization and the output results are found predominant with classical PSO. Conclusion: This technique is used to find the best solution and also the convergence time is reduced. The proposed QPSO method is demonstrated and results are compared with traditional optimization methods.