I Introduction
In the late 19th century, the theory of classical mechanics experienced several issues in reporting the physical phenomena of light masses and high velocity microscopic particles. In 1920s, Bohr’s atomic theory bohr1928quantum , Heisenberg’s discovery of quantum mechanics robertson1929uncertainty and Schrödinger’s wessels1979schrodinger discovery of wave mechanics influence the conception of a new field i.e. the quantum mechanics. In 1982, Feynman feynman1982simulating stated that quantum mechanical systems can be simulated by quantum computers in exponential time, i.e. better than with classical computers. Till then, the concept of quantum computing was thought to be only a theoretical possibility, but over the last three decades the research has evolved such as to make quantum computing applications a realistic possibility wang2012handbook .
In the last two decades, the field of swarm intelligence has got overwhelming response among research communities. It is inspired by nature and aims to build decentralized and selforganized systems by collective behavior of individual agents with each other and with their environment. The research foundation of swarm intelligence is constructed mostly upon two families of optimization algorithms i.e. ant colony optimization (Dorigo et at. dorigo1999ant and Colorni et al. colorni1992distributed 1992); and particle swarm optimization (PSO) (Kennedy & Eberhart kennedy1995particle 1995). Originally, the swarm intelligence is inspired by certain natural behaviors of flocks of birds and swarms of ants.
In the mid 1990s, particle swarm optimization technique was introduced for continuous optimization, motivated by flocking of birds. The evolution of PSO based bioinspired techniques has been in an expedite development in the last two decades. It has got attention from different fields such as inventory planning wang2014modified , power systems alrashidi2009survey , manufacturing yildiz2009novel , communication networks latiff2007energy
lin2008particle, to estimate binary inspiral signal
wang2010particle , gravitational waves normandin2018particleand many more. Similar to evolutionary genetic algorithm, it is inspired by simulation of social behavior, where each individual is called particle, and group of individuals is called swarm. In multidimensional search space, the position and velocity of each particle represent a probable solution. Particles fly around in a search space seeking potential solution. At each iteration, each particle adjusts its position according to the goal of its own and its neighbors. Each particle in a neighborhood share the information with others
sun2004particle . Later, each particle keeps the record of best solution experienced so far to update their positions and adjust their velocities accordingly.Since the first PSO algorithm proposed, the several PSO algorithms have been introduced with plethora of alterations. Recently, the combination of quantum computing, mathematics and computer science have inspired the creation of optimization techniques. Initially, Narayanan and Moore narayanan1996quantum introduced quantuminspired genetic algorithm (QGA) in 1995. Later, Sun et al. sun2004particle applied the quantum laws of mechanics to PSO and proposed quantuminspired particle swarm optimization (QPSO). It is the commencement of quantumbehaved optimization algorithms, which has subsequently made a significant impact on the academic and research communities alike.
Recently, Yuanyuan and Xiyu yuanyuan2018quantum
proposed a quantum evolutionary algorithm to discover communities in complex social networks. Its applicability is tested on five real social networks and results are compared with classical algorithms. It has been proved that PSO lacks convergence on local optima i.e. it is tough for PSO to come out of the local optimum once it confines into optimal local region. QPSO with mutation operator (QPSOMO) is proposed to enhance the diversity to escape from local optimum in search
liu2005quantum . Protopopescu and Barhen protopopescu2002solvingsolved set of global optimization problems efficiently using quantum algorithms. In future, the proposed algorithm can be integrated with matrix product state based quantum classifier for supervised learning
40 ; 44 .In this paper, we have combined QPSO with Cauchy mutation operator to add long jump ability for global search and natural selection mechanism for elimination of particles. The results shown that it has great tendency to overcome the problem of trapping into local search space. Therefore, the proposed hybrid QPSO strengthened the local and global search ability and outperformed the other variants of QPSO and PSO due to fast convergence feature.
The illustration of particles movement in PSO and QPSO algorithm is shown in Fig 1. The big circle at center denotes the particle with the global position and other circles are particles. The particles located away from global position are lagged particles. The blue color arrows signify the directions of other particles and the big red arrows point towards the side in which it goes with high probability. During iterations, if the lagged particle is unable to find better position as compared to present global position in PSO, then their impact is null on the other particles. But, in QPSO, the lagged particles move with higher probability in the direction of gbest position. Thus, the contribution of lagged particles is more to the solution in QPSO in comparison with PSO algorithm.
The organization of rest of this paper is as follows: Sect. 2 is devoted to prior work. In Sect. 3, the quantum particle swarm optimization is described. In Sect. 4, the proposed hybrid QPSO algorithm with Cauchy distribution and natural selection mechanism is presented. The experimental results are plotted for a set of benchmark problems and compared with several QPSO variants in Sect. 5. The correctness and time complexity are analyzed in Section 6. QPSOCD is applied to three constrained engineering design problems in Sect. 7. Finally, Sect. 8 is the conclusion.
Ii Prior Work
Since the quantumbehaved particle swarm optimization was proposed, various revised variants have been emerged. Initially, Sun et al. sun2004particle applied the concept of quantum computing to PSO and developed a quantum Delta potential well model for classical PSO sun2004global . It has been shown that the convergence and performance of QPSO are superior as compared to classical PSO. The selection and control of parameters can improve its performance, which is posed as an open problem. Sun et al. sun2007using tested the performance of QPSO on constrained and unconstrained problems. It has been claimed that QPSO is a promising optimization algorithm, which performs better than classical PSO algorithms. In 2011, Sun et al. sun2011quantum
proposed QPSO with Gaussian distribution (GAQPSO) with the local attenuator point and compared its results with several PSO and QPSO counterparts. It has been proved that GAQPSO is efficient and stable with superior features in quality and robustness of solutions.
Further, Coelho dos2010gaussian
applied GQPSO to constrained engineering problems and shown that the simulation results of GQPSO are much closer to the perfect solution with small standard deviation. Li et al.
li2012improved presented a cooperative QPSO using Monte Carlo method (CQPSO), where particles cooperate with each other to enhance the performance of original algorithm. It is implemented on several representative functions and performed better than the other QPSO algorithms in context of computational cost and quality of solutions. Peng et al. introduced peng2013quantumQPSO with Levy probability distribution and claimed that there are very less chances to be stuck in local optimum.
Researchers have applied PSO and QPSO to reallife problems and achieved optimal solutions as compared to existing algorithms. Ali et al. 90 performed energyefficient clustering in mobile adhoc networks (MANET) with PSO. The similar approach can be followed to analyse and execute mobility over MANET with QPSOCD 88 . Zhisheng zhisheng2010quantum used QPSO in economic load dispatch for power system and proved superior to other existing PSO optimization algorithms. Sun et al. sun2006qpso applied QPSO for QoS multicast routing. Firstly, the QoS multicast routing is converted into constrained integer problems and then effectively solved by QPSO with loop deletion task. Further, the performance is investigated on random network topologies. It has been proved that QPSO is more powerful than PSO and genetic algorithm. Geis and Middendorf proposed PSO with Helix structure for finding ribonucleic acid (RNA) secondary structures with same structure and low energy geis2011particle . The QPSOCD algorithm can be used with twoway quantum finite automata to model the RNA secondary structures bhatia2018modeling . Bagheri et al. bagheri2014financial applied the QPSO for tuning the parameters of adaptive networkbased fuzzy inference system (ANFIS) for forecasting the financial prices of future market. Davoodi et al. davoodi2014hybrid introduced a hybrid improved QPSO with Neldar Mead simplex method (IQPSONM), where NM method is used for tuning purpose of solutions. Further, the proposed algorithm is applied to solve load flow problems of power system and acquired the convergence accurately with efficient search ability. Omkar omkar2009quantum proposed QPSO for multiobjective design problems and results are compared with PSO. Recently, Fatemeh et al. fatemeh2019shuffled proposed QPSO with shuffled complex evolution (SPQPSO) and its performance is demonstrated using five engineering design problems. Prithi and Sumathi prithi2020ld2fa integrated the concept of classical PSO with deterministic finite automata for transmission of data and intrusion detection. The proposed algorithm QPSOCD can be used with quantum computational models for wireless communication 10 ; 20 ; 30 .
Iii Quantum Particle Swarm Optimization
Before we explain our hybrid QPSOCD algorithm mutated with Cauchy operator and natural selection method, it is useful to define the notion of quantum PSO. We assume that the reader is familiar with the concept of classical PSO; otherwise, reader can refer to particle swarm optimization algorithm kennedy2010particle ; shi2001particle . The specific principle of quantum PSO is given as:
In QPSO, the state of a particle can be represented using wave function
. The probability density function
is used to determine the probability of particle occurring in position x at any time t sun2004particle ; sun2006qpso . The position of particles is updated according to equations:(1) 
(2) 
where each particle must converge to its local attractor , where D is the dimension, N and M are the number of particles and iterations respectively, and denote the previous and optimal position vector of each particle respectively, , where ; are the acceleration coefficients, ; and u
are normally distributed random numbers in (0, 1),
is contractionexpansion coefficient and mbest defines the mean of best positions of particles as:(3) 
In Eq. (1), denotes contractionexpansion coefficient, which is setup manually to control the speed of convergence. It can be decreased linearly or fixed. In PSO, to ensure convergence performance of the particle. In QPSOCD, the value of is determined by =1(1.00.5)k/M, i.e. decreases linearly from 1.0 to 0.5 to attain good performance, where k is present iteration and M is maximum number of iterations.
Iv Hybrid Particle Swarm Optimization
The hybrid quantumbehaved PSO algorithm with Cauchy distribution and natural selection strategy (QPSOCD) is described as follows:
The QPSOCD algorithm begins with the standard QPSO using equations (1), (2) and (3). The position and velocity of particles cannot be determined exactly due to varying dynamic behavior. So, it can only be learned with the probability density function. Each particle can be mutated with Gaussian or Cauchy distribution. We mutated QPSO with Cauchy operator due to its ability to make larger perturbation. Therefore, there is a higher probability with Cauchy as compared to Gaussian distribution to come out of the local optima region. The QPSO algorithm is mutated with Cauchy distribution to increase its diversity, where mbest or global best position is mutated with fixed mutation probability (Pr). The probability density function of the standard Cauchy distribution is given as:
(4) 
It should be noted that mutation operation is executed on each vector by adding Cauchy distribution random value (D(.)) independently such that
(5) 
where is new location after mutated with random value to x. At last, the position of particle is selected and the particles of swarm are sorted on the basis of their fitness values after each iteration. Further, substitute the group of particles having worst fitness values with the best ones and optimal solution is determined. The main objective of using natural mechanism is to refine the capability and accuracy of QPSO algorithm.
The natural selection method is used to enhance the convergence characteristics of proposed QPSOCD algorithm, where the fitter solutions are used for the next iteration. The procedure of selection method for N particles is as follows:
(6) 
where is position vector of particles at time t and is the fitness function of swarm. Next step is to sort the particles according to their fitness values from best one to worst position such that
(7) 
In Algorithm 1, and are the sorting functions of fitness and position respectively. On the basis of natural selection parameters and fitness values, the positions of swarm particles are updated for the next iteration,
(8) 
where , S denotes the selection parameter, Z signifies the number of best positions selected according to fitness values such that and is updated position vector of particles. The selection parameter S is generally set as 2 to replace the half of worst positions with the half of best positions of particles. It improves the precision of the direction of particles, protect the global searching capability and speed up the convergence.
V Experimental results
The performance of proposed QPSOCD algorithm is investigated on representative benchmark functions, given in Table 1. Further, the results are compared with classical PSO (PSO), standard QPSO, QPSO with delta potential (QDPSO) and QPSO with mutation operator (QPSOMO). The details of numerical benchmark functions are given in Table 1.
Test function  Initial range 

Sphere function  
(100, 100)  
Rosenbrock function  
(5.12, 5.12)  
Greiwank function  
(600, 600)  
Rastrigrin function  
(5.12, 5.12) 
The performance of QPSO has been widely tested for various test functions. Initially, we have considered four representative benchmark functions to determine the reliability of QPSOCD algorithm. For all the experiments, the size of population is 20, 40 and 80 and dimension sizes are 10, 20 and 30. The parameters for QPSOCD algorithm are as follows: the value of decreases from 1.0 to 0.5 linearly, the natural selection parameter S=2 is taken, correlation coefficients are set equal to 2.
The mean best fitness values of PSO, QPSO, QDPSO, QPSOMO and QPSOCD are recorded for 1000, 1500 and 2000 runs of each function. Fig. 2 to Fig. 5 depict the performance of functions to with respect to mean best fitness against the number of iterations. In Table 2, P denotes the population, dimension is represented by D and G stands for generation. The numerical results of QPSOCD shown optimal solution with fast convergence speed and high accuracy. The results shown that QPSOCD performs better on Rosenbrock function than its counterparts in some cases. When the size of population is 20 and dimension is 30, the results of proposed algorithm are not better than QPSOMO, but QPSOCD performs better than PSO, QPSO and QDPSO. The performance of QPSOCD is significantly better than its variants on Greiwank and Rastrigrin functions. It has outperformed other algorithms and obtained optimal solution (near zero) for Greiwank function. In most of the cases, QPSOCD is more efficient and outperformed the other algorithms.
Sphere function  Rosenbrock function  

P  D  G  PSO  QPSO  QDPSO  QPSOMO  QPSOCD  PSO  QPSO  QDPSO  QPSOMO  QPSOCD 
20  10  1000  0.0  4.01e40  1.513e49  1.508e48  1.738e50  95.10  58.41  14.22  22.18  34.67 
20  1500  0.0  2.58e21  1.339e30  1.296e31  1.032e30  204.38  110.5  175.31  68.40  54.76  
30  2000  0.0  2.08e13  1.953e21  1.918e21  1.808e21  314.46  148.5  242.37  113.30  122.5  
40  10  1000  0.0  2.73e67  1.087e73  1.146e51  1.154e72  70.28  10.42  15.86  7.985  8.843 
20  1500  0.0  4.84e28  1.397e42  1.417e42  1.237e41  178.98  48.45  112.46  52.93  41.77  
30  2000  0.0  2.02e25  2.850e30  2.471e28  1.946e23  288.58  58.32  76.42  64.19  58.04  
80  10  1000  0.0  7.66e95  5.553e90  4.872e71  6.437e72  36.29  8.853  36.34  5.715  7.419 
20  1500  0.0  1.62e60  1.654e54  1.677e58  1.609e62  84.78  34.88  23.54  24.45  21.78  
30  2000  0.0  2.05e44  1.042e40  1.131e42  1.128e41  202.58  52.17  70.81  45.22  40.97 
Greiwank function  Rastrigrin function  
P  D  G  PSO  QPSO  QDPSO  QPSOMO  QPSOCD  PSO  QPSO  QDPSO  QPSOMO  QPSOCD 
20  10  1000  0.089  0.078  0.1003  0.0732  0.072  5.526  5.349  4.969  4.478  4.051 
20  1500  0.0300  0.2001  0.0086  0.0189  0.0078  23.17  21.28  17.08  15.63  13.22  
30  2000  0.0181  0.0122  0.0544  0.0103  0.0026  46.29  32.57  48.61  27.80  31.48  
40  10  1000  0.0826  0.055  0.048  0.0520  0.041  3.865  3.673  2.032  3.383  2.100 
20  1500  0.0272  0.0149  0.0004  0.0247  0.0106  15.68  14.37  10.94  11.01  10.77  
30  2000  0.0125  0.0117  0.0009  0.0105  0.0102  37.13  23.01  21.37  21.01  21.19  
80  10  1000  0.0723  0.0341  0.0  0.0542  0.0702  2.562  2.234  0.923  2.183  1.943 
20  1500  0.0274  0.0189  0.0  0.0194  0.0161  12.35  9.66  6.955  8.075  7.021  
30  2000  0.0123  0.0118  0.0  0.0082  0.0031  26.89  17.48  18.13  14.99  11.73  
Vi Correctness and Time Complexity Analysis of a QPSOCD Algorithm
In this Section, the correctness and time complexity of a proposed algorithm QPSOCD is analyzed and compared with the classical PSO algorithm.
Theorem 1.
The sequence of random variables
generated by QPSO with Cauchy distribution converges to zero in probability as n approaches infinity.Proof.
Recall, the probability density function of standard Cauchy distribution and its convergence probability rudolph1997local are given as
(9) 
Consider a random variable interpreted as
where denotes a fixed positive constant. Correspondingly, the probability density function can be calculated as
i.e. the probability density function of random variable .
Using Eq. (9), the probability density function of random variable becomes
This completes the proof of the theorem. ∎
Definition 1.
Let a random sequence of variables. It is converges to some random variable s with probability 1, if for every and , there exists such that or
(10) 
The efficiency of the QPSOCD algorithm is evaluated by number of steps needed to reach the optimal region . The method is to evaluate the distribution of number of steps needed to hit
by comparing the expected value and moments of distribution. The total number of stages to reach the optimal region is determined as
. The variance
and expectation value are determined as(11) 
(12) 
In fact, the depends upon the convergence of . It is needed that , so that QPSOCD can converge globally. The number of objective function evaluations are used to measure time. The main benefit of this approach is that it shows relationship between processor and measure time as the complexity of objective function increases. We used Sphere function with a linear constraint to compute the time complexity. It has minimum value at 0. The value of optimal region is set as To determine the time complexity, the algorithms PSO and QPSOCD are executed 40 times on with initial scope [10, 10], where N denotes the dimension. We determine the mean number of objective function evaluations (), the variance (), the standard deviation (SD) (
), the standard error (SE) (
) and ratio of mean and dimension (). The contraction coefficient is used for QPSOCD and constriction coefficient for PSO with acceleration factors =2.25.Dimension (N)  Mean  Variance  SD  SE  Mean/N 

2  302.38  4164.3  64.532  10.203  151.19 
3  452.18  4541.9  67.394  10.655  150.72 
4  621.29  5208.2  72.168  11.410  155.32 
5  755.88  6675.0  81.701  12.918  151.17 
6  879.13  8523.7  92.324  14.597  146.52 
7  1022.06  9575.4  97.854  15.472  146.00 
8  1158.52  10269.7  101.341  16.023  144.81 
9  1308.17  12053.4  109.788  17.359  145.35 
10  1459.3  12648.3  112.465  17.782  145.93 
Dimension (N)  Mean  Variance  SD  SE  Mean/N 

2  691.4  17297.5  131.52  20.795  345.7 
3  979.1  22281.5  149.27  23.601  326.3 
4  1167.2  24282.9  155.72  24.638  291.8 
5  1328.7  21853.7  147.83  23.373  265.7 
6  1489.9  32008.7  178.91  28.288  248.3 
7  1744.3  502297  224.12  35.436  249.1 
8  1978.5  41233.3  203.06  31.106  247.3 
9  2259.1  36217.8  190.31  30.090  251.0 
10  2604.2  43559.8  208.71  32.999  260.4 
Table 1 and 2 show the statistical results of time complexity test for QPSOCD and PSO algorithm, respectively. Fig 6 indicates that the time complexity of proposed algorithm increases nonlinearly as the dimension increases. However, the time complexity of PSO algorithm increases adequately linearly. Thus, the time complexity of QPSOCD is lower than PSO algorithm. In Fig 7, QPSOCD shows a strong correlation between and N, i.e. the correlation coefficient is 0.9996. For PSO, the linear correlation coefficient is 0.9939, which is not so phenomenal as that in case of QPSOCD. The relationship between mean and dimension clearly shows that the value of correlation coefficient is fairly stable for QPSOCD as compared to PSO algorithm.
Vii QPSOCD for Constraint Engineering Design Problems
There exists several approaches for handling constrained optimization problems. The basic principle is to convert the constrained optimization problem to unconstrained by combining objective function and penalty function approach. Further, minimize the newly formed objective function with any unconstrained algorithm. Generally, the constrained optimization problem can be described as in Eq. (13).
The objective is to minimize the objective function f(x) subjected to equality and inequality constrained functions, where is the upper bound and denotes the search space lower bound. The strict inequalities of form can be converted into and equality constraints can be converted into inequality constraints and . Sun et al. sun2007using
adopted nonstationary penalty function to address nonlinear programming problems using QPSO. Coelho
dos2010gaussian used penalty function with some positive constant i.e. set to 5000. We adopted the same approach and replace the constant with dynamically allocated penalty value.(13) 
Usually, the procedure is to find the solution for design variables that lie in search space upper and lower bound constraints such that . If solution violates any of the constraint, then the following rules are applied
(14) 
where rand[0, 1] is randomly distributed function to select value between 0 and 1. Finally, the unconstrained optimization problem is solved using dynamically modified penalty values according to inequality constraints . Thus, the objective function is evaluated as
(15) 
where is the main objective function of optimization problem in Eq. (13), t is the iteration number and represents the dynamically allocated penalty value.
In this Section, QPSOCD is tested for threebar truss, tension/compression spring and pressure vessel design problems consisting different members and constraints. The performance of QPSOCD is compared and analyzed with the results of PSO, QPSO, and SPQPSO algorithms as reported in the literature.
vii.1 Threebar truss design problem
Threebar truss is a constraint design optimization problem, which has been widely used to test several methods. It consists crosssection areas of three bars (and ) and as design variables. The aim of this problem is to minimize the weight of truss subject to maximize the stress on these bars. The structure should be symmetric and subjected to two constant loadings as shown in Fig 8. The mathematical formulation of two design bars (, ) and three restrictive mathematical functions are described as:
(16) 
Variables  PSO  QPSO  SPQPSO  QPSOCD 

0.78911058  0.788649  0.788796  0.788658  
0.40702683  0.408322  0.407898  0.40828488  
6.6720e06  1.6313e07  6.4748e06  9.00037e06  
1.4655  1.4640  1.4644  1.4640  
0.5345  0.5359  0.5354  0.5359  
263.89686  263.89584  263.89500  263.89465 
The results are obtained by QPSOCD are compared with its counterparts in Table 6. For threebar truss problem, QPSOCD is superior to optimal solutions previously obtained in literature. The difference of best solution obtained by QPSOCD among other algorithms is shown in Fig 9.
vii.2 Tension/Compression spring design problem
The main aim is to lessen the volume V of a spring subjected to tension load constantly as shown in Fig 10. Using the symmetry of structure, there are practically three design variables (), where is the wire diameter, the coil diameter is represented by and denotes the total number of active coils. The mathematical formulation for this problem is described as:
(17) 
Variables  PSO  QPSO  SPQPSO  QPSOCD 

0.0516  0.0524  0.05  0.0513  
0.3542  0.2505  0.25  0.2502  
11.7942  2  2  2  
2.3006e02  0.93145095  0.93034756  4.11004e06  
5.6059e03  0.17471558  0.16568318  0.17352479  
3.9057  50.67  48.180  49.561  
0.7294  0.79986567  0.80  0.799  
0.01305  0.00275  0.00250  0.00263 
It has been observed that QPSO algorithm with Cauchy distribution and natural selection strategy is robust and obtains optimal solutions than PSO and QPSO, shown in Table 7. The difference between best solutions found by QPSOCD () and other algorithms for tension spring design problem are reported in Fig 11.
vii.3 Pressure vessel design problem
Initially, Kannan and Kramer kannan1994augmented studied the pressure vessel design problem with the main aim to reduce the total fabricating cost. Pressure vessels can be of any shape. For engineering purposes, a cylindrical design capped by hemispherical heads at both ends is widely used sandgren1990nonlinear . Fig 12 describes the structure of pressure vessel design problem. It consists four design variables (), where denotes the shell thickness , is used for head thickness (), denotes the inner radius (R) and represents the length of vessel (L). The objective function and constraint equations are described as:
(18) 
Variables  PSO  QPSO  SPQPSO  QPSOCD 

0.8125  0.7783  0.7782  0.7776  
0.4375  0.3849  0.3845  0.3848  
42.0984  40.3289  40.3206  40.3278  
176.6365  199.8899  199.9988  199.8865  
4.500e15  4.777e05  1.242e05  7.2654e04  
0.035880  1.62294e04  1.58523e04  7.2787e05  
1.164e10  97.39720071  63.63686942  0.734359  
63.3634  40.1100  40.0012  40.1135  
6059.714  5886.189  5885.268  5886.137 
The optimal results of QPSOCD is compared with the SPQPSO, QPSO and PSO best results noted in the previous work, and are given in Table 8. The best solution obtained from QPSOCD is better than other algorithms as shown in Fig 13.
Viii Conclusion
In this paper, a new hybrid quantum particle swarm optimization algorithm is proposed with natural selection method and Cauchy distribution. The performance of the proposed algorithm is experimented on four benchmark functions and the optimal results are compared with existing algorithms. Further, the QPSOCD is applied to solve engineering design problems. The efficiency of QPSOCD is successfully presented with superiority than preceding results for three engineering design problems: threebar truss, tension/compression spring and pressure vessel. The efficiency of QPSOCD algorithm is evaluated by number of steps needed to reach the optimal region and proved that time complexity of proposed algorithm is lower in comparison to classical PSO. In the context of convergence, the experimental outcomes shown that the QPSOCD converge to get results closer to the superior solution.
Additional information
Competing interests: The authors declare no competing interests.
Acknowledgement
S.Z. acknowledges support in part from the National Natural Science Foundation of China (Nos. 61602532), the Natural Science Foundation of Guangdong Province of China (No. 2017A030313378), and the Science and Technology Program of Guangzhou City of China (No. 201707010194).
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