Computational Intelligence and Neuroscience / 2016 / Article / Tab 1

Research Article

Particle Swarm Optimization with Double Learning Patterns

Table 1

Benchmark functions used in this paper.

NumberDescription and expressionSearch space

Group 1: conventional problems
Sphere [−100, 10010−60
Schwefel’s function 1.2 [−100, 10010−60
Noise quadric [−1.28, 1.2810−20
Rosenbrock [−10, 1010−20
Ackley [−32.768, 32.76810−60
Griewank [−600, 60010−60
Rastrigin [−5.12, 5.1210−60
Noncontinuous Rastrigin   [−5.12, 5.1210−60
Expanded Schaffer
[−100, 10010−60

Group 2: rotated problems
Rotated Rosenbrock , , is an orthogonal matrix[−10, 10100
Rotated Ackley , [−32.768, 32.768100
Rotated Griewank , [−600, 600100
Rotated Rastrigin , [−5.12, 5.12100
Rotated noncontinuous Rastrigin , [−5.12, 5.12100

Group 3: shifted problems
Shifted Sphere , , [−100, 10010−6−450
Shifted Rosenbrock , , [−10, 1010−6390
Shifted Rastrigin , , [−5.12, 5.1210−6−330
Shifted non-Rastrigin , , [−5.12, 5.1210−6−330
Shifted rotated Ackley’s function with global optimum on bounds[−32.76, 32.7610−6−140
Shifted rotated Rastrigin’s function [−5.12, 5.1210−6−330

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.