What is teaching learning based optimization algorithm?

Inspiration of the algorithm: Teaching learning-based optimization (TLBO) is a population-based meta-heuristic optimization technique that simulates the environment of a classroom to optimize a given objective function and it was proposed by R.V. Rao et al. in 2011.

How many phases in TLBO algorithm?

TLBO algorithm consists of two phases: Teacher phase and student phase.

Which of the following algorithm does not required algorithm specific parameters?

(2011) introduced the teaching-learning-based optimization (TLBO) algorithm which does not require any algorithm-specific parameters. The TLBO algorithm requires only common controlling parameters like population size and number of generations for its working.

What is Jaya algorithm?

Jaya Algorithm is a gradient-free optimization algorithm [1]. It can be used for Maximization or Minimization of a function. It is a population based method which repeatedly modifies a population of individual solutions and capable of solving both constrained and unconstrained optimization problems.

What is the uniqueness of Jaya algorithms?

It can be seen that the configuration of JAYA is straightforward and unique, and no extra parameters are required for the initialization in the JAYA; that is, JAYA is likewise unrestricted from algorithm-specific parameters.

Who proposed the Jaya algorithm?

JAYA algorithm is proposed by Rao [29] in 2016 and gained a considerable interest from a wide variety of research communities due to its impressive characteristics: It is simple in concepts and easy-to-use.

What is GREY Wolf algorithm?

Grey wolf optimization algorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. It is a large-scale search method centered on three optimal samples, and which is also the research object of many scholars.