Research Article
Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems
Table 3
Benchmark problems.
| Number | Problem | Formulation | Feature | Search range |
| 1 | Ackley | | MN | ±32 | 2 | Booth | | MN | ±10 | 3 | Easom | | UN | ±100 | 4 | Griewank | | MN | ±600 | 5 | Dixon-Price | | UN | ±10 | 6 | Levy | where | MN | ±10 | 7 | Michalewicz | | MS | 0, | 8 | Noisy Quartic | | US | ±1.28 | 9 | Noncontinous Rastrigin |
| MS | ±5.12 | 10 | Rastrigin | | MS | ±5.12 | 11 | Rosenbrock | | UN | ±30 | 12 | Rotated Ellipsoid | | UN | ±100 | 13 | Salomon | | MN | ±100 | 14 | Schaffer's f6 | | MN | ±100 | 15 | Schwefel | | MS | ±500 | 16 | Schwefel P2.22 | | UN | ±10 | 17 | Shubert | | MN | ±10 | 18 | Sphere | | US | ±100 | 19 | Step | | US | ±10 | 20 | SumSquares | | US | ±10 | 21 | Trid | | UN | ±d 2 |
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