Abstract
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.
Highlights
Optimization problem frequently occurs in real-time scenarios and one need to have efficient technique to attain the optimal solution with high convergence while dealing with a specific problem
To tackle the challenges of optimizing the node density and coverage, we propose a novel enhanced differential crossover quantum particle swarm optimization (EDCQPSO) algorithm
We have introduced a crossover operator with quantum PSO (QPSO)
Summary
Optimization problem frequently occurs in real-time scenarios and one need to have efficient technique to attain the optimal solution with high convergence while dealing with a specific problem. Metaheuristic algorithms are extensively utilized in solving the real life optimization problems. They are iterative and based on social behaviors or natural phenomena [2], [3]. The metaheuristic algorithms are comparatively efficient than the gradient based on the optimization [4]–[8]. The capability of parallel execution and disseminated features of swarm intelligence algorithms facilitates the probability of solving complex non-linear problems with innovative abilities such as flexibility, robustness, and searching capacity. The metaheuristic algorithm still needs to be upgraded because the convergence rate towards an optimum solution is comparatively slower. There is a need to alter and enhance exploration and exploitation abilities of the algorithms. [9]–[14]
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