Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing

Table 3

Pseudocode of devised MTOA.

Sl. No	Pseudocode of developed MTOA
1	Input: Total population and velocities
2	Output: Best solution,
3	Begin
4	Initialise the population of male and female mayflies and their velocities
5	Compute the fitness function using equation (16)
6	Determine the global best
7	While
8	Update the velocities and solutions of females and males
9	Compute the solutions
10	Ranking of mayflies
11	Mate the mayflies
12	Estimate the off-springs based on equations (46) and (47)
13	Divide the off-spring into male and female arbitrarily
14	Replace worst solutions with the new solution
15	Update and
16	End while
17	Return the best solution