Optimality Test cases of Particle Swarm optimization of single objective functions

Abstract

Optimization problems are classified into continuous , discrete , constrained , unconstrained deterministic, stochastic, single objective and multi-objective optimization problems. Deterministic, Heuristics and Meta-Heuristic technique , mostly dominate the solution set of small and medium scale problems, whereas for large data class optimization problems, Evolutionary techniques ( mostly derivative -free)  are used to address the near -optimal solution of these class of P, N-P, N-P Hard problems. In the present paper, evolutionary algorithmic approach without evolutionary operators , mimicking the biology of swarms , with artificial intelligence is introduced. The above search technique is called Particle swarm optimization (PSO) method, used in solving many N-P hard problems with respect to position, and  velocity vector approach for random search to obtain near optimal solution . The method is discussed in detail with flow chart and various optimization benchmark problems of single objective function are solved using the developed PSO Code and compared with global minima of benchmark problems.

Cite as
G.Lakshmi Kameswari. (2020). Optimality Test cases of Particle Swarm optimization of single objective functions. Journal of Applied Mathematics and Statistical Analysis, 1(1), 1–9. http://doi.org/10.5281/zenodo.4010485