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V. Rajashekar and Y. Shivraj Narayan
2.1 Applications of TLBO Algorithm
Wenwen et al. proposed and implemented MOTLBO and GRA in the turning process.
Analytical hierarchy process was used to find optimum results (OR) [12]. Rao et al.
applied NSTLBO and MOTLBO to 3 different manufacturing processes namely wire
EDM, turning and laser cutting process (LCP). In WEDM and turning processes,
fewer evaluations are required to get optimum results as compared to NSGA-II. In
micro-WEDM and LCP, Pareto solution obtained in NSTLBO is better than PSO
and GA [13]. Rao et al. implemented priori-based TLBO in thermoacoustic devices
(TAD), namely thermo-acoustic refrigerator (TAR), thermo-acoustic prime-mover
(TAP), and thermo-acoustic engine (TAE). TLBO is found to achieve optimum condition in fewer functional evaluations and effective than GAMS and RSM [14]. Saleh
et al. applied hybrid TLBO-PSO technique to choose the Associated Genes with
Breast Cancer. Hybrid TLBO-PSO technique produces accurate results than TLBO,
PSO [15]. Venkata Rao implemented NSTLBO in micro ball-end milling process.
NSTLBO results are found to be in good concurrence with FEM [16]. Venkata
Rao applied TLBO and NSTLBO in fused deposition modelling process optimization. NSTLBO produced better Pareto fronts in terms of spacing and coverage than
NSGA-II [17]. Subhrajyoti et al. applied priori-based TLBO and MIPSO (Mixed
Integer Particle Swarm Optimization) in MO power flow problems. TLBO outperforms MIPSO in convergence rate to get OC [18]. Neelesh and Atul applied prioribased TLBO, JAYA and GA to optimize the turning process. Size of population and
generations for all algorithms are the same, TLBO and JAYA performed better than
GA [19]. Dashuang Li et al. proposed and implemented MITLBO in assembly line
optimization. Better Pareto fronts are acquired rapidly in MITLBO than MOTLBO
and NSGA-II [20]. Vivek and Vimal proposed TS-TLBO algorithm and applied it to
MOP of a sterling heat engine (SHE). It is easily applied in case of high number of
variables and objectives [21]. Edmund and Rajesh applied MOTLBO to 4 cases of
spur gear design to minimize weight and maximize power. TLBO produced better
OR compared GA, SA [22].
3 Jaya Algorithm [6]
Jaya algorithm is completed in one step, by modification of variables using the simple
equation given by Rao [6]. At any iteration ‘i’, consider ‘m’ input variables (number
of subjects j = 1,…m), ‘n’ population (number of learners k = 1,…n) the best and
worst overall result are X total,KbeFst,i and X total,Kworst,i . In Jaya algorithm, ‘P’ initial
solutions are there. These are updated using the below equation
A(i + 1, j, k) = A(i, j, k) + r (i, j, 1)(A(i, j, b) − |A(i, j, k)|)
− r (i, j, 2)(A(i, j, w) − |A(i, j, k))
V. Rajashekar and Y. Shivraj Narayan
2.1 Applications of TLBO Algorithm
Wenwen et al. proposed and implemented MOTLBO and GRA in the turning process.
Analytical hierarchy process was used to find optimum results (OR) [12]. Rao et al.
applied NSTLBO and MOTLBO to 3 different manufacturing processes namely wire
EDM, turning and laser cutting process (LCP). In WEDM and turning processes,
fewer evaluations are required to get optimum results as compared to NSGA-II. In
micro-WEDM and LCP, Pareto solution obtained in NSTLBO is better than PSO
and GA [13]. Rao et al. implemented priori-based TLBO in thermoacoustic devices
(TAD), namely thermo-acoustic refrigerator (TAR), thermo-acoustic prime-mover
(TAP), and thermo-acoustic engine (TAE). TLBO is found to achieve optimum condition in fewer functional evaluations and effective than GAMS and RSM [14]. Saleh
et al. applied hybrid TLBO-PSO technique to choose the Associated Genes with
Breast Cancer. Hybrid TLBO-PSO technique produces accurate results than TLBO,
PSO [15]. Venkata Rao implemented NSTLBO in micro ball-end milling process.
NSTLBO results are found to be in good concurrence with FEM [16]. Venkata
Rao applied TLBO and NSTLBO in fused deposition modelling process optimization. NSTLBO produced better Pareto fronts in terms of spacing and coverage than
NSGA-II [17]. Subhrajyoti et al. applied priori-based TLBO and MIPSO (Mixed
Integer Particle Swarm Optimization) in MO power flow problems. TLBO outperforms MIPSO in convergence rate to get OC [18]. Neelesh and Atul applied prioribased TLBO, JAYA and GA to optimize the turning process. Size of population and
generations for all algorithms are the same, TLBO and JAYA performed better than
GA [19]. Dashuang Li et al. proposed and implemented MITLBO in assembly line
optimization. Better Pareto fronts are acquired rapidly in MITLBO than MOTLBO
and NSGA-II [20]. Vivek and Vimal proposed TS-TLBO algorithm and applied it to
MOP of a sterling heat engine (SHE). It is easily applied in case of high number of
variables and objectives [21]. Edmund and Rajesh applied MOTLBO to 4 cases of
spur gear design to minimize weight and maximize power. TLBO produced better
OR compared GA, SA [22].
3 Jaya Algorithm [6]
Jaya algorithm is completed in one step, by modification of variables using the simple
equation given by Rao [6]. At any iteration ‘i’, consider ‘m’ input variables (number
of subjects j = 1,…m), ‘n’ population (number of learners k = 1,…n) the best and
worst overall result are X total,KbeFst,i and X total,Kworst,i . In Jaya algorithm, ‘P’ initial
solutions are there. These are updated using the below equation
A(i + 1, j, k) = A(i, j, k) + r (i, j, 1)(A(i, j, b) − |A(i, j, k)|)
− r (i, j, 2)(A(i, j, w) − |A(i, j, k))