Name
Abstract | ||
|---|---|---|
A novel hybrid Krill herd (KH) and quantum-behaved particle swarm optimization (QPSO), called KH–QPSO, is presented for benchmark and engineering optimization. QPSO is intended for enhancing the ability of the local search and increasing the individual diversity in the population. KH–QPSO is capable of avoiding the premature convergence and eventually finding the function minimum; especially, KH–QPSO can make all the individuals proceed to the true global optimum without introducing additional operators to the basic KH and QPSO algorithms. To verify its performance, various experiments are carried out on an array of test problems as well as an engineering case. Based on the results, we can easily infer that the hybrid KH–QPSO is more efficient than other optimization methods for solving standard test problems and engineering optimization problems.
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Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00521-015-1914-z | Neural Computing and Applications |
Keywords | Field | DocType |
Swarm intelligence, Krill herd, Quantum-behaved particle swarm optimization, Quantum computation | Particle swarm optimization,Population,Mathematical optimization,Premature convergence,Swarm intelligence,Quantum computer,Multi-swarm optimization,Local search (optimization),Engineering optimization,Mathematics | Journal |
Volume | Issue | ISSN |
27 | 4 | 1433-3058 |
Citations | PageRank | References |
39 | 0.93 | 49 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gai-Ge Wang | 1 | 1251 | 48.96 |
| Amir Hossein Gandomi | 2 | 1836 | 110.25 |
| Amir Hossein Alavi | 3 | 1016 | 45.59 |
| Suash Deb | 4 | 1926 | 82.86 |