I believe that if it is not simple to make someone (may be having a different area of expertise) understand any process/operation then perhaps I haven't understood it yet clearly. Hence, need suggestions from the respective researchers having experiences in PSO to shed light. Thanks in advance!
I have some experience with metaheuristics, and it is been great fun. The main drawback with them is that they tend to never terminate with an optimal solution. In fact, I think I have never seen any run with a metaheuristic that has terminated with an optimal solution. I find that to be a rather serious drawback - wouldn't you?
The most suitable method is to start with the algorithm. This will help someone from other area to understand the method and the process itself. Do not bother him with questions, simply the method. Then you can demonstrate simulation videos to explain this optimization technique.
Just follow the path traced by the migratory birds during seasonal variations.
Or, follow the flock of a bird when they return to their nests at the end of a day.
You should get the idea of PSO!
The following might help you:
i) They start to fly from different initial locations (position) with different initial velocities.
ii) They update their locations (position) and velocities depending on their learning from two different sources, i.e. from the individual learning experience and from the group learning experience.
iii) There is no individual leader in the flock.
Kindly refer to this chapter. I think it'll be helpful to understand Particle Swarm Optimizer easily.
Sharma S. (2021) QRP: QPSO Based Routing Protocol for Energy Efficiency in Wireless Body Area Networks. In: Chaubey N., Parikh S., Amin K. (eds) Computing Science, Communication and Security. COMS2 2021. Communications in Computer and Information Science, vol 1416. Springer, Cham. https://doi.org/10.1007/978-3-030-76776-1_14
Or refer to the same paper at the following address:
https://www.researchgate.net/publication/352166415_QRP_QPSO_Based_Routing_Protocol_for_Energy_Efficiency_in_Wireless_Body_Area_Networks
Chapter QRP: QPSO Based Routing Protocol for Energy Efficiency in Wi...
Particle swarm optimization (PSO) is a population-based stochastic approach for solving continuous and discrete optimization problems. In particle swarm optimization, simple software agents, called particles, move in the search space of an optimization problem.
For those interested in a simplified explanation of the PSO algorithm, please watch the video provided by Yarpiz: https://www.youtube.com/watch?v=sB1n9a9yxJk
Particle swarm optimization (PSO) is an artificial intelligence (AI) technique that can be used to find approximate solutions to extremely difficult or impossible numeric maximization and minimization problems.
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