Related papers: Generating Exact Optimal Designs via Particle Swar…
Compared to other techniques, particle swarm optimization is more frequently utilized because of its ease of use and low variability. However, it is complicated to find the best possible solution in the search space in large-scale…
A Particle Swarm Optimizer for the search of balanced Boolean functions with good cryptographic properties is proposed in this paper. The algorithm is a modified version of the permutation PSO by Hu, Eberhart and Shi which preserves the…
This paper preliminarily investigates the duality between flow matching in generative models and particle swarm optimization (PSO) in evolutionary computation. Through theoretical analysis, we reveal the intrinsic connections between these…
The paper introduces particle swarm optimization as a viable strategy to find numerical solution of Diophantine equation, for which there exists no general method of finding solutions. The proposed methodology uses a population of integer…
This paper provides a formalization of the energy disaggregation problem for particle swarm optimization and shows the successful application of particle swarm optimization for disaggregation in a multi-tenant commercial building. The…
This paper presents a method for choosing a Particle Swarm Optimization based optimizer for the Dynamic Vehicle Routing Problem on the basis of the initially available data of a given problem instance. The optimization algorithm is chosen…
Selection of perefect parameters for low-pass filters can sometimes be an expensive problem with no analytical solution or differentiability of cost function. In this paper, we introduce a new PSO-inspired algorithm, that incorporates the…
This work utilizes a particle swarm optimizer (PSO) for initial orbit determination for a chief and deputy scenario in the circular restricted three-body problem (CR3BP). The PSO is used to minimize the difference between actual and…
Indoor positioning applications are craving for ever higher precision and accuracy across the entire coverage zone. Optimal anchor placement and the deployment of multiple distributed anchor nodes could have a major impact in this regard.…
This study proposes a method for estimating the mechanical parameters of vehicles and bridges and the road unevenness, using only vehicle vibration and position data. In the proposed method, vehicle input and bridge vibration are estimated…
Advanced inventory management in complex supply chains requires effective and robust nonlinear optimization due to the stochastic nature of supply and demand variations. Application of estimated gradients can boost up the convergence of…
In this paper, we present a hybrid of Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) algorithms for numerically efficient global optimization of antenna arrays and metasurfaces. The hybrid EP-PSO algorithm uses an…
Particle swarm optimization is used in several combinatorial optimization problems. In this work, particle swarms are used to solve quadratic programming problems with quadratic constraints. The approach of particle swarms is an example for…
Trilateration-based localization (TBL) has become a corner stone of modern technology. This study formulates the concern on how wireless sensor networks can take advantage of the computational intelligent techniques using both single- and…
We study the variant of Particle Swarm Optimization (PSO) that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called potential of the swarm falls below a certain bound in this…
This paper proposes the use of an optimization algorithm, namely PSO to decide the initial centroids in K-means, to eventually get better accuracy. The vectorized notation of the optimal centroids can be thought of as entities in an…
Application of metaheuristic algorithms has been of continued interest in the field of electrical engineering because of their powerful features. In this work special design is done for a tapered transmission line used for matching an…
Parameter updating is an important stage in parallelism-based distributed deep learning. Synchronous methods are widely used in distributed training the Deep Neural Networks (DNNs). To reduce the communication and synchronization overhead…
Particle Swarm Optimization (PSO) is an Evolutionary Algorithm (EA) that utilizes a swarm of particles to solve an optimization problem. Slow Intelligence System (SIS) is a learning framework which slowly learns the solution to a problem…
This paper presents Variable Interaction Graph Particle Swarm Optimization (VIGPSO), an adaptation to Particle Swarm Optimization (PSO) that dynamically learns and exploits variable interactions during the optimization process. PSO is…