Related papers: Particle Swarm Optimization for Quantum Circuit Sy…
Data clustering is a recognized data analysis method in data mining whereas K-Means is the well known partitional clustering method, possessing pleasant features. We observed that, K-Means and other partitional clustering techniques suffer…
Many real-world problems are dynamic optimization problems. In this case, the optima in the environment change dynamically. Therefore, traditional optimization algorithms disable to track and find optima. In this paper, a new multi-swarm…
We present a newly developed -Gaussian Swarm Quantum-like Particle Optimization (q-GSQPO) algorithm to determine the global minimum of the potential energy function. Swarm Quantum-like Particle Optimization (SQPO) algorithms have been…
A new approach to the solution of Economic Dispatch using Particle Swarm Optimization is presented. It is the progression of allocating production amongst the dedicated units such that the restriction forced are fulfilled and the power…
This study introduces an innovative crossover operator named Particle Swarm Optimization-inspired Crossover (PSOX), which is specifically developed for real-coded genetic algorithms. Departing from conventional crossover approaches that…
Premature convergence in particle swarm optimization (PSO) algorithm usually leads to gaining local optimum and preventing from surveying those regions of solution space which have optimal points in. In this paper, by applying special…
Ant Colony System (ACS) is a distributed (agent- based) algorithm which has been widely studied on the Symmetric Travelling Salesman Problem (TSP). The optimum parameters for this algorithm have to be found by trial and error. We use a…
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…
We dramatically improve convergence speed and global exploration capabilities of particle swarm optimization (PSO) through a targeted position-mutated elitism (PSO-TPME). The three key innovations address particle classification, elitism,…
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…
Metaheuristic particle swarm optimization (PSO) algorithm has emerged as one of the most promising optimization techniques in solving highly constrained non-linear and non-convex optimization problems in different areas of electrical…
Motion planning is a key element of robotics since it empowers a robot to navigate autonomously. Particle Swarm Optimization is a simple, yet a very powerful optimization technique which has been effectively used in many complex…
We propose novel particle swarm optimization (PSO) variants incorporated with deep neural networks (DNNs) for particles to pursue globally optimal positions in dynamic environments. PSO is a heuristic approach for solving complex…
We introduce an efficient algorithm for the systematic design of shallow-depth quantum circuits capable of preparing many-body quantum states represented as Matrix Product States (MPS). The proposed method leverages Schmidt spectrum…
Swarm intelligence effectively optimizes complex systems across fields like engineering and healthcare, yet algorithm solutions often suffer from low reliability due to unclear configurations and hyperparameters. This study analyzes…
An experimental evaluation is conducted to asses the performance of 4 different Particle Swarm Optimization neighborhood structures in solving Max-Sat problem. The experiment has shown that none of the algorithms achieves statistically…
In this paper we enhance Generalized Self-Adapting Particle Swarm Optimization algorithm (GAPSO), initially introduced at the Parallel Problem Solving from Nature 2018 conference, and to investigate its properties. The research on GAPSO is…
This article introduces a robust hybrid method for solving supervised learning tasks, which uses the Echo State Network (ESN) model and the Particle Swarm Optimization (PSO) algorithm. An ESN is a Recurrent Neural Network with the…
The problem of development of the SVM classifier based on the modified particle swarm optimization has been considered. This algorithm carries out the simultaneous search of the kernel function type, values of the kernel function parameters…
The analysis of vast amounts of data constitutes a major challenge in modern high energy physics experiments. Machine learning (ML) methods, typically trained on simulated data, are often employed to facilitate this task. Several choices…