TLBO and JAYA: Insights into Novel
Multi-objective Optimization Techniques
V. Rajashekar and Yeole Shivraj Narayan
Abstract Optimization is an effective tool for optimum utilization of existing
resources so as to improve quality, productivity and to reduce the cost. The majority
of the real-world situations have multiple objectives that conflict with each other.
Hence multiple objectives or criteria need to be optimized effectively and simultaneously for achieving the best output. Hence, many evolutionary algorithms like
Pareto Optimization, Non-Sorted Genetic Algorithm (NSGA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), etc. have been developed in the past
for this purpose. However, in order to obtain more accurate predictions, these techniques are continuously being modified to make them evolutionary in nature resulting
in newer multi-objective optimization techniques. Teaching-Learning Based Optimization (TLBO) and JAYA are two state-of-the-art multi-objective optimization
techniques. This paper presents a review of TLBO and JAYA multi-objective optimization techniques with an emphasis on key insights into the methodology and
algorithms, followed by their application in different fields. It is observed that these
techniques yielded better performance than the existing ones.
Keywords Multi-objective optimization (MOO) · TLBO · JAYA · Evolutionary
algorithms
1 Introduction
Optimization is an important aspect to improve the quality, productivity and reduction
of the cost. Most of the literature is focused on single objective (SO) optimization, but
real-world problems are conflicting in nature. Hence, MOO comes into the picture.
Based on decision-making processes MOO classified as no preference-based, priori,
V. Rajashekar · Y. Shivraj Narayan (B)
VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad,
Telangana 500090, India
e-mail: shivrajyeole@vnrvjiet.in
© The Editor(s) (if applicable) and The Author(s), under exclusive license
to Springer Nature Singapore Pte Ltd. 2021
N. Gascoin and E. Balasubramanian (eds.), Innovative Design, Analysis
and Development Practices in Aerospace and Automotive Engineering, Lecture Notes
in Mechanical Engineering, https://doi.org/10.1007/978-981-15-6619-6_25
239
Multi-objective Optimization Techniques
V. Rajashekar and Yeole Shivraj Narayan
Abstract Optimization is an effective tool for optimum utilization of existing
resources so as to improve quality, productivity and to reduce the cost. The majority
of the real-world situations have multiple objectives that conflict with each other.
Hence multiple objectives or criteria need to be optimized effectively and simultaneously for achieving the best output. Hence, many evolutionary algorithms like
Pareto Optimization, Non-Sorted Genetic Algorithm (NSGA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), etc. have been developed in the past
for this purpose. However, in order to obtain more accurate predictions, these techniques are continuously being modified to make them evolutionary in nature resulting
in newer multi-objective optimization techniques. Teaching-Learning Based Optimization (TLBO) and JAYA are two state-of-the-art multi-objective optimization
techniques. This paper presents a review of TLBO and JAYA multi-objective optimization techniques with an emphasis on key insights into the methodology and
algorithms, followed by their application in different fields. It is observed that these
techniques yielded better performance than the existing ones.
Keywords Multi-objective optimization (MOO) · TLBO · JAYA · Evolutionary
algorithms
1 Introduction
Optimization is an important aspect to improve the quality, productivity and reduction
of the cost. Most of the literature is focused on single objective (SO) optimization, but
real-world problems are conflicting in nature. Hence, MOO comes into the picture.
Based on decision-making processes MOO classified as no preference-based, priori,
V. Rajashekar · Y. Shivraj Narayan (B)
VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad,
Telangana 500090, India
e-mail: shivrajyeole@vnrvjiet.in
© The Editor(s) (if applicable) and The Author(s), under exclusive license
to Springer Nature Singapore Pte Ltd. 2021
N. Gascoin and E. Balasubramanian (eds.), Innovative Design, Analysis
and Development Practices in Aerospace and Automotive Engineering, Lecture Notes
in Mechanical Engineering, https://doi.org/10.1007/978-981-15-6619-6_25
239