244
V. Rajashekar and Y. Shivraj Narayan
Venkata Rao et al. done MOO of abrasive water jet machining (AWJM) and
abrasive water jet turning processes (AWJT). MO-Jaya produces more significant
OR than GA, PSO, cuckoo search (CS), NSTLBO and NSGA-II. [26]. Warid et al.
developed and applied MO-Jaya and QO-Jaya to PFP, IEEE 30-bus system taken as
input in all cases, MO-Jaya and QO-Jaya produced better results with efficient and
quickly [27]. R. Venkat Rao et al. applied MO-Jaya algorithm in AWJM process.
MO-Jaya results are assessed with NSTLBO and NSGA. It produced better Pareto
fronts in fewer generations and higher hypervolume metric [28]. R. Venkat Rao et al.
implemented adaptive multi term perturbation guiding Jaya (AMTPG Jaya) to three
cases of solar dish SHE and solar heat pump. AMTPG Jaya produced better Pareto
solutions than TOPSIS and NSGA-II [29]. Singh et al. applied priori-based Jaya to
optimize wire EDM process Jaya algorithm do not require specific parameters like
other algorithms [30].
4 Conclusions
An overview of novel multi-objective optimization techniques like TLBO and Jaya
algorithms is presented. Either priori-based approach or posteriori-based approach
is used for MOO. TLBO and Jaya algorithms have been applied for optimization
in design, process parameters in manufacturing processes, medical field to find the
diseases, assembly line operations to improve line efficiency, and scheduling. In
priori-based approach, TLBO and Jaya algorithms have a good convergence rate
to reach optimum conditions in fewer functional evaluations as compared to other
algorithms like GA, PSO, SA, BBO, TS, CS. In posterior-based approach, NSTLBO,
MOTLBO, MO-Jaya, MO-SAMP, and AMTPG-Jaya produced good Pareto fronts,
higher hypervolume metrics compared to all other algorithms NSGA, NSGA-II,
MOPSO.
References
1. Tamaki H, Kita H, Kobayashi S (1996) Multi-objective optimization by genetic algorithms: a
review. Evol Comput 517–522
2. Wang H, Olhofer M, Jin Y (2017) A mini-review on preference modeling and articulation in
multi-objective optimization: current status and challenges. J Complex Intell Syst 3(4):233–245
3. Mukherjee I, Kumar Ray P (2006) A review of optimization techniques in metal cutting
processes. Comput Ind Eng 50(1–2)15–34
4. Said LB, Bechikh S, Ghédira K (2010) The r-dominance: a new dominance relation for
interactive evolutionary multicriteria decision making. IEEE T Evolut Comput 14(5):801–818
5. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel
method for constrained mechanical design optimization problems. Comput Aided Des
43(3):303–315
6. Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and
unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34
V. Rajashekar and Y. Shivraj Narayan
Venkata Rao et al. done MOO of abrasive water jet machining (AWJM) and
abrasive water jet turning processes (AWJT). MO-Jaya produces more significant
OR than GA, PSO, cuckoo search (CS), NSTLBO and NSGA-II. [26]. Warid et al.
developed and applied MO-Jaya and QO-Jaya to PFP, IEEE 30-bus system taken as
input in all cases, MO-Jaya and QO-Jaya produced better results with efficient and
quickly [27]. R. Venkat Rao et al. applied MO-Jaya algorithm in AWJM process.
MO-Jaya results are assessed with NSTLBO and NSGA. It produced better Pareto
fronts in fewer generations and higher hypervolume metric [28]. R. Venkat Rao et al.
implemented adaptive multi term perturbation guiding Jaya (AMTPG Jaya) to three
cases of solar dish SHE and solar heat pump. AMTPG Jaya produced better Pareto
solutions than TOPSIS and NSGA-II [29]. Singh et al. applied priori-based Jaya to
optimize wire EDM process Jaya algorithm do not require specific parameters like
other algorithms [30].
4 Conclusions
An overview of novel multi-objective optimization techniques like TLBO and Jaya
algorithms is presented. Either priori-based approach or posteriori-based approach
is used for MOO. TLBO and Jaya algorithms have been applied for optimization
in design, process parameters in manufacturing processes, medical field to find the
diseases, assembly line operations to improve line efficiency, and scheduling. In
priori-based approach, TLBO and Jaya algorithms have a good convergence rate
to reach optimum conditions in fewer functional evaluations as compared to other
algorithms like GA, PSO, SA, BBO, TS, CS. In posterior-based approach, NSTLBO,
MOTLBO, MO-Jaya, MO-SAMP, and AMTPG-Jaya produced good Pareto fronts,
higher hypervolume metrics compared to all other algorithms NSGA, NSGA-II,
MOPSO.
References
1. Tamaki H, Kita H, Kobayashi S (1996) Multi-objective optimization by genetic algorithms: a
review. Evol Comput 517–522
2. Wang H, Olhofer M, Jin Y (2017) A mini-review on preference modeling and articulation in
multi-objective optimization: current status and challenges. J Complex Intell Syst 3(4):233–245
3. Mukherjee I, Kumar Ray P (2006) A review of optimization techniques in metal cutting
processes. Comput Ind Eng 50(1–2)15–34
4. Said LB, Bechikh S, Ghédira K (2010) The r-dominance: a new dominance relation for
interactive evolutionary multicriteria decision making. IEEE T Evolut Comput 14(5):801–818
5. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel
method for constrained mechanical design optimization problems. Comput Aided Des
43(3):303–315
6. Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and
unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34