Applying a Discrete Particle Swarm Optimization Algorithm to Combinatorial Problems

Rosendo, Matheus; Pozo, Aurora

doi:10.1109/sbrn.2010.48

Cited by 13 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other different aspects of the original version of PSO have also been modified, and many variants have been proposed to address different kinds of problems; e.g., a discrete binary version of PSO [ 7 ] that is useful for combinatorial optimisation problems, such as the travelling salesman problem [ 8 ] and task scheduling problems [ 9 , 10 ].…”

Section: Modifications To the Particle Swarm Optimisationmentioning

confidence: 99%

Particle Swarm Optimisation: A Historical Review Up to the Current Developments

Freitas

Lopes

Morgado‐Dias

2020

Entropy

166

View full text Add to dashboard Cite

The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space seeking for the global best position that minimises (or maximises) a given problem. Today, PSO is one of the most well-known and widely used swarm intelligence algorithms and metaheuristic techniques, because of its simplicity and ability to be used in a wide range of applications. However, in-depth studies of the algorithm have led to the detection and identification of a number of problems with it, especially convergence problems and performance issues. Consequently, a myriad of variants, enhancements and extensions to the original version of the algorithm, developed and introduced in the mid-1990s, have been proposed, especially in the last two decades. In this article, a systematic literature review about those variants and improvements is made, which also covers the hybridisation and parallelisation of the algorithm and its extensions to other classes of optimisation problems, taking into consideration the most important ones. These approaches and improvements are appropriately summarised, organised and presented, in order to allow and facilitate the identification of the most appropriate PSO variant for a particular application.

show abstract

Section: Modifications To the Particle Swarm Optimisationmentioning

confidence: 99%

Particle Swarm Optimisation: A Historical Review Up to the Current Developments

Freitas

Lopes

Morgado‐Dias

2020

Entropy

166

View full text Add to dashboard Cite

show abstract

“…The particle has its own velocity of movement and position. With different forms of PSOs, each discrete PSO (DPSO) normally requires that the position of each particle should be an integer and not repeated in different dimensions [45][46][47][48][49]. Such discrete problem optimizations all follow the basic principle of updating the velocity of a particle and combining with other algorithms to update the position of the particle based on different problems.…”

Section: B Multistage and Multiswarm Discrete Particle Swarm Optimization (Msms-dpso)mentioning

confidence: 99%

“…However, before applying the velocity-and positionupdating formulas, the initial positions of all particles need to be considered seriously. The initial positions of the particles could have a functional influence on the performance of a PSO [45]. In our paper, the position of the first dimension is assigned to the odd position.…”

Section: B Multistage and Multiswarm Discrete Particle Swarm Optimization (Msms-dpso)mentioning

confidence: 99%

Wearable Accelerometer Layout Optimization for Activity Recognition Based on Swarm Intelligence and User Preference

Xia¹,

Sugiura

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Individuals are guided by the best individuals of the group and the overall movement is also influenced by the historically best points found in the search space. [18,19] There are other different swarm algorithms inspired by biology: Ant Colony, Bees Algorithm, Artificial Bee Colony Algorithm, Cuckoo, Blind Naked Mole-Rats (BNMR) algorithm, and the newly described Bison Seeker Algorithm [20].…”

Section: Swarm Intelligencementioning

confidence: 99%

Hybrid Symbolic Regression with the Bison Seeker Algorithm

Merta

2019

mendel

View full text Add to dashboard Cite

This paper focuses on the use of the Bison Seeker Algorithm (BSA) in a hybrid genetic programming approach for the supervised machine learning method called symbolic regression. While the basic version of symbolic regression optimizes both the model structure and its parameters, the hybrid version can use genetic programming to find the model structure. Consequently, local learning is used to tune model parameters. Such tuning of parameters represents the lifetime adaptation of individuals. This paper aims to compare the basic version of symbolic regression and hybrid version with the lifetime adaptation of individuals via the Bison Seeker Algorithm. Author also investigates the influence of the Bison Seeker Algorithm on the rate of evolution in the search for function, which fits the given input-output data. The results of the current study support the fact that the local algorithm accelerates evolution, even with a few iterations of a Bison Seeker Algorithm with small populations.

show abstract

Applying a Discrete Particle Swarm Optimization Algorithm to Combinatorial Problems

Cited by 13 publications

References 15 publications

Particle Swarm Optimisation: A Historical Review Up to the Current Developments

Particle Swarm Optimisation: A Historical Review Up to the Current Developments

Wearable Accelerometer Layout Optimization for Activity Recognition Based on Swarm Intelligence and User Preference

Hybrid Symbolic Regression with the Bison Seeker Algorithm

Contact Info

Product

Resources

About