Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The particle swarm: social adaptation in information-processing systems
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
A Combined Swarm Differential Evolution Algorithm for Optimization Problems
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Particle Evolutionary Swarm Optimization Algorithm (PESO)
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Extending particle swarm optimisation via genetic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Evolving the structure of the particle swarm optimization algorithms
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rapid evaluation and evolution of neural models using graphics card hardware
Proceedings of the 10th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Evolutionary Algorithms (EAs) can be used for designing Particle Swarm Optimization (PSO) algorithms that work, in some cases, considerably better than the human-designed ones. By analyzing the evolutionary process of design PSO algorithm we can identify different swarm phenomena (such as patterns or rules) that can give us deep insights about the swarm's behaviours. The observed rules can help us to design better PSO algorithms for optimization. In this paper we investigate and analyze swarm phenomena by looking to process of evolving PSO algorithms. Several interesting facts are inferred from the strategy evolution process (the particle quality could influence the update order, some particles are updated more frequently than others are, the initial swarm size is not always optimal).