Effects of random values for particle swarm optimization algorithm
HP Dai, DD Chen, ZS Zheng - Algorithms, 2018 - mdpi.com
Particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting
the inertia weight or combining with other evolution algorithms. However, in most modified …
the inertia weight or combining with other evolution algorithms. However, in most modified …
[Retracted] Improved Particle Swarm Optimization Algorithm in Power System Network Reconfiguration
Y Wu, Q Song - Mathematical Problems in Engineering, 2021 - Wiley Online Library
With the rapid development of the social economy, the rapid development of all social circles
places higher demands on the electricity industry. As a fundamental industry supporting the …
places higher demands on the electricity industry. As a fundamental industry supporting the …
Opposition-based hybrid strategy for particle swarm optimization in noisy environments
Particle swarm optimization (PSO) is a population-based algorithm designed to tackle
various optimization problems. However, its performance deteriorates significantly when …
various optimization problems. However, its performance deteriorates significantly when …
A new particle swarm optimization algorithm for noisy optimization problems
We propose a new particle swarm optimization algorithm for problems where objective
functions are subject to zero-mean, independent, and identically distributed stochastic noise …
functions are subject to zero-mean, independent, and identically distributed stochastic noise …
A surrogate-assisted evolutionary algorithm with hypervolume triggered fidelity adjustment for noisy multiobjective integer programming
Although surrogate-assisted evolutionary algorithms (SAEAs) have been widely developed
to address computationally expensive multi-objective optimization problems (MOPs), they …
to address computationally expensive multi-objective optimization problems (MOPs), they …
An opposition-based particle swarm optimization algorithm for noisy environments
MC Zhou, Z Zhao, C **ong… - 2018 IEEE 15th …, 2018 - ieeexplore.ieee.org
Particle Swarm Optimization (PSO) is a population-based algorithm designed to tackle
various optimization problems. However, its performance deteriorates significantly when …
various optimization problems. However, its performance deteriorates significantly when …
A new multi-function global particle swarm optimization
In this paper, we introduce the concept of population density in PSO, and accordingly, we
discuss the relationship between the search capability of PSO and the population density …
discuss the relationship between the search capability of PSO and the population density …
Interest and applicability of meta-heuristic algorithms in the electrical parameter identification of multiphase machines
Multiphase machines are complex multi-variable electro-mechanical systems that are
receiving special attention from industry due to their better fault tolerance and power-per …
receiving special attention from industry due to their better fault tolerance and power-per …
Population statistics for particle swarm optimization: Hybrid methods in noisy optimization problems
Particle swarm optimization (PSO) is a metaheuristic designed to find good solutions to
optimization problems. However, when optimization problems are subject to noise, the …
optimization problems. However, when optimization problems are subject to noise, the …
Kinetic parameters estimation of protease production using penalty function method with hybrid genetic algorithm and particle swarm optimization
Almost all optimization techniques are restricted by the problems' dimensions and large
search spaces. This research focuses on a special hybrid method combining two meta …
search spaces. This research focuses on a special hybrid method combining two meta …