Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A genetic algorithm and a particle swarm optimizer hybridized with Nelder-Mead simplex search
Computers and Industrial Engineering - Special issue: Sustainability and globalization: Selected papers from the 32 nd ICC&IE
A hybrid watermarking technique applied to digital images
Applied Soft Computing
Ant colony and particle swarm optimization for financial classification problems
Expert Systems with Applications: An International Journal
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
Tackling magnetoencephalography with particle swarm optimization
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
Particle swarm optimizer with C-Pg mutation
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Predicted-velocity particle swarm optimization using game-theoretic approach
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
International Journal of Bio-Inspired Computation
Metaheuristic algorithms for inverse problems
International Journal of Innovative Computing and Applications
Hi-index | 0.01 |
Particle swarm optimisation (PSO) is a novel population-based stochastic optimisation algorithm inspired by the Reynolds' boid model. The original biological background of boid obeys three basic simple steering rules: separation, alignment and cohesion. However, to promote a simple update equation, none of these rules of boid model is employed by PSO methodology. Due to the weakness of biological background of PSO, in this paper, a new variant of PSO, boid particle swarm optimisation (BPSO), is designed in which cohesion rule and alignment rule are both employed to improve the performance. In BPSO, each particle has two motions: divergent motion and convergent motion. For divergent motion, each particle adjusts its moving direction according to the alignment direction and the cohesion direction, as well as in convergent motion, the original update equation of the standard version of PSO is used. To make a motion transition, a threshold is introduced to make the divergent motion is employed in the first period, whereas the convergent motion is used in the final stage. To testify the efficiency, several unconstrained benchmarks are used to compare. Simulation results show the proposed variant is more effective and efficient than other two variants of PSO when solving multi-modal high-dimensional numerical problems.