Fig. 1: The global optimization performance assessment of QLSA, LSA, BSA, GSA and PSO under different benchmark functions. | Nature Communications

Fig. 1: The global optimization performance assessment of QLSA, LSA, BSA, GSA and PSO under different benchmark functions.

From: Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement

Fig. 1

a The global optimisation results for QLSA, LSA, BSA, GSA and PSO in benchmark function F1 (Sphere) is obtained based on dimension problem, search space and function minimum (Supplementary Table 1). b The global optimisation results for QLSA, LSA, BSA, GSA and PSO in benchmark function F2 (Step) is obtained based on dimension problem, search space and function minimum (Supplementary Table 1). c The global optimisation results for QLSA, LSA, BSA, GSA and PSO in benchmark function F3 (Quartic). d The global optimisation results for QLSA, LSA, BSA, GSA and PSO in benchmark function F4 (Schwefel 2.22). e The global optimisation results for QLSA, LSA, BSA, GSA and PSO in benchmark function F5 (Schwefel 1.2). f The global optimisation results for QLSA, LSA, BSA, GSA and PSO in benchmark function F6 (Schwefel 2.21) is obtained based on dimension problem, search space and function minimum (Supplementary Table 1). g The global optimisation results for QLSA, LSA, BSA, GSA and PSO in benchmark function F7 (Rosenbrock). h The global optimisation results for QLSA, LSA, BSA, GSA and PSO in benchmark function F8 (Rastrigin). i The global optimisation results for QLSA, LSA, BSA, GSA and PSO in benchmark function F9 (Foxholes). j The global optimisation results for QLSA, LSA, BSA, GSA and PSO in benchmark function F10 (Branin) is obtained based on dimension problem, search space and function minimum (Supplementary Table 1).

Back to article page