# Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review

@article{Bonyadi2017ParticleSO, title={Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review}, author={Mohammad Reza Bonyadi and Zbigniew Michalewicz}, journal={Evolutionary Computation}, year={2017}, volume={25}, pages={1-54} }

This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.

## 364 Citations

Runtime analysis of discrete particle swarm optimization algorithms: A survey

- Computer Scienceit Inf. Technol.
- 2019

A comparison of known upper and lower bounds of expected runtimes is given and the techniques used to obtain these bounds are discussed.

Improved Exploration and Exploitation in Particle Swarm Optimization

- Computer ScienceIEA/AIE
- 2018

This analysis focuses on the pbest positions that reflect the actual levels of exploration and exploitation that have been achieved by PSO, and provides a clear criterion for when restarting particles can be expected to be a useful strategy in PSO.

PSO for Job-Shop Scheduling with Multiple Operating Sequences Problem - JS

- BusinessHIS
- 2016

This paper focus on a complex problem of job shop scheduling where each jobs have a multiple possible operations sequences and a new algorithm based on Particle Swarm Optimization Global Velocity (PSOVG) was proposed to solve this.

Particle Swarm Optimization with feasibility rules in constrained numerical optimization. A brief review

- Computer Science2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)
- 2016

The conclusions suggest that the original PSO has changed to avoid its own disadvantages as premature convergence and also that some methods related to the inertia weight, constriction factor, additional operators, or hybridization with other metahuristics have been applied to improve the results in complex problems.

Using particle swarm optimization to solve test functions problems

- Computer Science
- 2021

The benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm and it is compared with genetic algorithm (GA) in order to prove capability of PSO.

An Analysis of Minimum Population Search on Large Scale Global Optimization

- Computer Science2019 IEEE Congress on Evolutionary Computation (CEC)
- 2019

An analysis using some recent categorizations for exploitation and exploration, and especially the effects of selection, sheds light on how MPS performs better in high dimensions than popular metaheuristics such as Differential Evolution and Particle Swarm Optimization.

A survey on particle swarm optimization with emphasis on engineering and network applications

- Computer ScienceEvol. Intell.
- 2019

This work focuses on reviewing a heuristic global optimization method called particle swarm optimization (PSO), the mathematical representation of PSO in contentious and binary spaces, the evolution and modifications ofPSO over the last two decades and a comprehensive taxonomy of heuristic-based optimization algorithms.

On the Particle Swarm Optimization Control Using Analytic Programming and Self Organizing Migrating Algorithm

- Computer Science, Mathematics2019 IEEE Congress on Evolutionary Computation (CEC)
- 2019

This article looks at the feedback control of system which contains randomness from the time delay feedback control perspective, and selected particles are controlled to a stable point by adding small perturbations to the particle position.

Hybrid PSO Algorithm with Iterated Local Search Operator for Equality Constraints Problems

- Computer Science2018 IEEE Congress on Evolutionary Computation (CEC)
- 2018

A hybrid PSO algorithm with an ILS (Iterated Local Search) operator for handling equality constraints problems in mono-objective optimization problems and shows improvement in accuracy, reducing the gap for the tested problems.

Convergence analysis of particle swarm optimization using stochastic Lyapunov functions and quantifier elimination

- MathematicsArXiv
- 2020

This paper presents a computational procedure and shows that this approach leads to reevaluation and extension of previously know stability regions for PSO using a Lyapunov approach under stagnation assumptions.

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