Name
Abstract | ||
|---|---|---|
This paper proposes a new approach to Particle Swarm Optimization (PSO) to solve nonlinear problems with linear and nonlinear constraints. A crossover operator and a new particle updating method, named Footholds Concept, were developed to guarantee fully feasible solutions and better search-space coverage, respectively. In addition, a novel swarm initialization heuristic is applied to benchmarks with equality constraints. The algorithm has been tested on 13 common benchmark functions. Experimental results show that it is very competitive as it increases PSO efficiency and improves convergence speed. |
Year | Venue | Field |
|---|---|---|
2016 | CEC | Particle swarm optimization,Heuristic,Mathematical optimization,Crossover,Swarm behaviour,Computer science,Multi-swarm optimization,Artificial intelligence,Initialization,Imperialist competitive algorithm,Machine learning,Metaheuristic |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manoela Kohler | 1 | 0 | 0.68 |
| Leonardo Forero | 2 | 0 | 0.68 |
| Marley M. B. R. Vellasco | 3 | 10 | 6.74 |
| Ricardo Tanscheit | 4 | 118 | 21.53 |
| Marco Aurélio Cavalcanti Pacheco | 5 | 143 | 22.29 |