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

  title={Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review},
  author={Mohammad Reza Bonyadi and Zbigniew Michalewicz},
  journal={Evolutionary Computation},
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. 
Runtime analysis of discrete particle swarm optimization algorithms: A survey
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
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
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
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
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.
A survey on particle swarm optimization with emphasis on engineering and network applications
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.
An Analysis of Minimum Population Search on Large Scale Global Optimization
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.
On the Particle Swarm Optimization Control Using Analytic Programming and Self Organizing Migrating Algorithm
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
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
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.


Parameter Selection in Particle Swarm Optimization
This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters.
The particle swarm optimization algorithm: convergence analysis and parameter selection
  • I. Trelea
  • Computer Science
    Inf. Process. Lett.
  • 2003
Recent advances in particle swarm
This paper reviews the development of the particle swarm optimization method in recent years and modifications to adapt to different and complex environments are reviewed, and real world applications are listed.
A new locally convergent particle swarm optimiser
This paper introduces a new Particle Swarm Optimisation (PSO) algorithm with strong local convergence properties. The new algorithm performs much better with a smaller number of particles, compared
Particle swarm optimization
A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Fuzzy adaptive particle swarm optimization
  • Yuhui Shi, R. Eberhart
  • Computer Science
    Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546)
  • 2001
The experimental results illustrate that the fuzzy adaptive PSO is a promising optimization method, which is especially useful for optimization problems with a dynamic environment.
Particle swarm optimisation with spatial particle extension
This paper introduces spatial extension to particles in the PSO model in order to overcome premature convergence in iterative optimisation and shows that the SEPSO indeed managed to keep diversity in the search space and yielded superior results.
Particle Swarm Optimization Method for Constrained Optimization Problems
The performance of the Particle Swarm Optimization method in coping with Constrained Optimization problems is investigated and results are compared with those obtained through other evolutionary algorithms, such as Evolution Strategies and Genetic Algorithms.
Research on particle swarm optimization: a review
  • Meiping Song, Guo-chang Gu
  • Computer Science
    Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826)
  • 2004
The fundamental and standard algorithm is introduced, the work on the algorithm improvement during the past years is surveyed, as well as the applications on the multi-objective optimization, neural networks and electronics, etc.
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
  • P. Angeline
  • Computer Science
    Evolutionary Programming
  • 1998
This paper investigates the philosophical and performance differences of particle swarm and evolutionary optimization by comparison experiments involving four non-linear functions well studied in the evolutionary optimization literature.