A Comparative Study on the Performance of Bio-inspired Algorithms on Benchmarking and Real-World Optimization Problems

Biologically inspired computing, shortly known as bio-inspired computing (BiC), follows the models of biology to solve the problems by computing. The main objective of the study presented in this paper is to present the working principle of three BiC algorithms with different biological bases. The algorithms considered were genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA). These algorithms were implemented, to solve a set of benchmarking problems and a real-world image segmentation problem, and their performance were compared. The performance metrics used for the comparison were the solution obtained (So), number of generations (NoG), and the execution time (ExeTime). It was observed from the results, of benchmarking problems, that PSO has given better solutions followed by SA and GA. For the real-world problem, it was concluded with the results that GA has segmented the image better than SA and PSO.