A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy

Table 3

Results of different numbers of variables being executed with the IVL strategy.

Functions	𝑛=0	𝑛=5	𝑛=10	𝑛=15	𝑛=20	𝑛=25	𝑛=30
Scaled Rosenbrock 100: 𝑓7	8.45𝐸+04	2.34𝐸+04	5.82𝐸+03	3.65𝐸+03	1.48𝐸+02	7.55𝐸+01	2.32𝐸+01
Noise Schwefel 1.2: 𝑓9	9.82𝐸+02	6.37𝐸+01	1.46𝐸+00	8.17𝐸−04	3.25𝐸−08	9.72𝐸−13	0.00𝐸+00
Rotated Schwefel 2.21: 𝑓11	6.63𝐸−01	1.16𝐸−01	3.61𝐸−02	8.28𝐸−04	2.52𝐸−08	1.07𝐸−14	0.00𝐸+00
Rotated Ellipse: 𝑓12	2.91𝐸+02	3.97𝐸+00	1.76𝐸−03	5.11𝐸−06	5.34𝐸−11	6.28𝐸−19	0.00𝐸+00
Rotated Rosenbrock: 𝑓13	1.82𝐸+04	8.82𝐸+03	2.57𝐸+03	9.07𝐸+02	2.93𝐸+02	5.64𝐸+01	2.87𝐸+01
Noise Rotated Schwefe1.2: 𝑓17	2.77𝐸+03	5.75𝐸+02	6.03𝐸+00	4.23𝐸−04	2.45𝐸−09	1.87𝐸−15	0.00𝐸+00