


Registros recuperados: 28  

 

 

 

 


Bruno R. Nery; Rodrigo F. de Mello; André P. L. F. de Carvalho; Laurence T. Yang. 
The behavior of real ants motivated Dorigo et al. [DMC96] to propose the Ant Colony Optimization (ACO) technique, which can be used to solve problems in dynamic environments. This technique has been successfully applied to several optimization problems [FMS05, PB05, BN06, SF06, PLF02, WGDK06, CF06, HND05]. Such results have motivated this chapter which presents ACO concepts, case studies and also a complete example on process scheduling optimization. Besides the successful adoption of ACO, it presents some relevant questions which have been motivating future directions such as: how to adjust parameters which depend on the optimization problem [SocOSj; how to reduce the execution time [G.N06, MBSD06]; the optimization improvement by using incremental local... 
Tipo: 13 
Palavraschave: Swarm Intelligence; Focus on Ant and Particle Swarm Optimization. 
Ano: 2007 
URL: http://www.intechopen.com/articles/show/title/artificial_ants_in_the_real_world__solving_online_problems_using_ant_colony_optimization 
 

 


Aaron C. Zecchin; Holger R. Maier; Angus R. Simpson. 
To gain a more complete understanding of ACO algorithms, it is important to not only consider their performance with respect to their solution quality and computational efficiency, but also the algorithms' searching behaviour. In this chapter, two statistics of searching behaviour have been considered, (i) the minimum cost found within an iteration, which is an indication of search quality, and (ii) the mean colony distance, a topological measure that describes the spread of solutions through the solution space and thus provides an indication of the degree of convergence of an algorithm. Four ACO algorithms were considered in this chapter, namely, Ant System (AS), Elitist Ant System (ASelite), ElitistRank Ant System (ASrank), and MaxMin Ant System... 
Tipo: 24 
Palavraschave: Swarm Intelligence; Focus on Ant and Particle Swarm Optimization. 
Ano: 2007 
URL: http://www.intechopen.com/articles/show/title/case_study_based_convergence_behaviour_analysis_of_aco_applied_to_optimal_design_of_water_distributi 
 

 


BingRui Chen; XiaTing Feng. 
Two modified versions of PSO are introduced: one is CSVPSO algorithm in which random numbers are generated by the mixed congruential method, and another is PCSVPSO algorithm for recognizing rheological parameters of rockmass. A great deal of numerical simulations show that the CSVPSO algorithm has better convergence performance and more accurate convergence precision, its run is more stable and it can provide certainty solution in different runtime. Sensitivity analysis of the CSVPSO algorithm indicates that random seed, stagnancy number and constant α 0 determining flying velocity of particles have a great effect on performance of the algorithm. Proper random seed can accelerate convergence of the algorithm; while bad random seed can not only slow... 
Tipo: 15 
Palavraschave: Swarm Intelligence; Focus on Ant and Particle Swarm Optimization. 
Ano: 2007 
URL: http://www.intechopen.com/articles/show/title/csvpso_and_its_application_in_geotechnical_engineering 
 

 

 

 

 

 

 

 

 


Ruben E. Perez; Kamran Behdinan. 
Particle Swarm Optimization is a populationbased algorithm, which mimics the social behaviour of animals in a flock. It makes use of individual and group memory to update each particle position allowing global as well as local search optimization. Analytically the PSO behaves similarly to a traditional linesearch where the step length and search direction are stochastic. Furthermore, it was shown that the PSO search strategy can be represented as a discretedynamic system which converges to an equilibrium point. From a stability analysis of such system, a parameter selection heuristic was developed which provides an initial guideline to the selection of the different PSO setting parameters. Experimentally, it was found that using the derived heuristics... 
Tipo: 21 
Palavraschave: Swarm Intelligence; Focus on Ant and Particle Swarm Optimization. 
Ano: 2007 
URL: http://www.intechopen.com/articles/show/title/particle_swarm_optimization_in_structural_design 
 

 

 
Registros recuperados: 28  


