# A locally convergent rotationally invariant particle swarm optimization algorithm

@article{Bonyadi2014ALC, title={A locally convergent rotationally invariant particle swarm optimization algorithm}, author={Mohammad Reza Bonyadi and Zbigniew Michalewicz}, journal={Swarm Intelligence}, year={2014}, volume={8}, pages={159-198} }

Several well-studied issues in the particle swarm optimization algorithm are outlined and some earlier methods that address these issues are investigated from the theoretical and experimental points of view. These issues are the: stagnation of particles in some points in the search space, inability to change the value of one or more decision variables, poor performance when the swarm size is small, lack of guarantee to converge even to a local optimum (local optimizer), poor performance when…

## 60 Citations

Locating Potentially Disjoint Feasible Regions of a Search Space with a Particle Swarm Optimizer

- Computer Science
- 2015

The results of this test show that the new method with nonoverlapping topology with small swarm size in each sub-swarm performs better in terms of locating different feasible regions in comparison to other topologies, such as the global best topology and the ring topology.

Searching for structural bias in particle swarm optimization and differential evolution algorithms

- Computer ScienceSwarm Intelligence
- 2016

The present study focuses on the search for structural bias in various variants of particle swarm optimization and differential evolution algorithms, as well as in the traditional direct search methods proposed by Nelder–Mead and Rosenbrock half a century ago, and found that these historical directsearch methods are structurally unbiased.

Analysis of Stability, Local Convergence, and Transformation Sensitivity of a Variant of the Particle Swarm Optimization Algorithm

- Mathematics, Computer ScienceIEEE Transactions on Evolutionary Computation
- 2016

This paper identifies boundaries of coefficients for this algorithm that ensure particles converge to their equilibrium and investigates the local convergence property of this algorithm and proves that the original standard PSO algorithm is not sensitive to rotation, scaling, and translation of the search space.

An improved rotationally invariant PSO: A modified standard PSO-2011

- Computer Science2016 IEEE Congress on Evolutionary Computation (CEC)
- 2016

It is clarified that SPSO2011 performance is affected by the distribution of the center of the search range, and a novel update rule is proposed to improve the global search ability.

Particle Swarm Optimization Algorithm with Chaotic Mapping Model

- Computer Science
- 2014

Experimental results show that the proposed particle swarm optimization algorithm with chaotic mapping (CM-PSO) has strong global searching ability, can effectively avoid the premature convergence problem, and the ability of the algorithm to escape from local optima.

A rotationally invariant semi-autonomous particle swarm optimizer with directional diversity

- Computer ScienceSwarm Evol. Comput.
- 2020

Stability Analysis of the Particle Swarm Optimization Without Stagnation Assumption

- MathematicsIEEE Transactions on Evolutionary Computation
- 2016

It is proved that the convergence of expectation and variance of the positions generated by the stochastic recurrence relation is independent of the mean and varianceof the distribution of the personal and global best vectors.

Dense conjugate initialization for deterministic PSO in applications: ORTHOinit+

- Computer ScienceAppl. Soft Comput.
- 2021

Stability analysis of the human behavior-based particle swarm optimization without stagnation assumption

- Computer ScienceExpert Syst. Appl.
- 2020

A novel particle swarm optimization algorithm for non-separable and ill-conditioned problems

- Computer Science2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
- 2016

This article discusses the relation between the Hessian matrix of a function and the covariance matrix of the search distribution and proposes a simple covariance Matrix adaptation mechanism that uses the difference vector of the personal best positions.

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