Research Article

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

Table 3

Design and evolutionary data for BGAwEIS (spatial truss with 25-bars).

Design data

Material properties
Modulus of elasticity:  ksi
Density of material: 0.1 lb/

Loading data
Case numberJoint numberX (kips)Y (kips)Z (kips)
111−10−10
20−10−10
30.500
60.600

Constraint data
Displacement constraints:  inc ( ) for X and Y directions
Stress constraints:  ksi ( )

Elements of discrete sets and their position number for ( )
0.1(1),0.2(2),0.3(3),0.4(4),0.5(5),0.6(6),0.7(7),0.8(8),0.9(9),1.0(10),1.1(11),1.2(12),1.3(13),1.4(14),1.5(15),1.6(16),1.7(17),
1.8(18),1.9(19),2.0(20),2.1(21),2.2(22),2.3(23),2.4(24),2.5(25),2.6(26),2.8(27), 3.0(28),3.2(29),3.4(30)

Evolutionary data

Input

Number of design variables: 8
Size of solution region: 30
Number of generation: 400
Size of inward population: 300
Size of outward population: 300
Size of core population: 300

Cases
Case ICase IICase IIICase IV
NGGES50202025
NSBS20502015

Output

NSAS133411
NFS761018
Ratio 1 R13.753.753.753.75
Ratio 2 R22082027
Ratio 3 R357674022
Best feasible fitness value571.618592.656515.845485.90
Mean of feasible fitness values659.771619.972587.133521.678
Standard deviation of feasible fitness values59.59118.85667.09645.880