A comparative study of Particle Swarm Optimization and Cuckoo Search techniques through problem-specific distance function

For the last two decades, nature inspired metaheuristic algorithms have shown their ubiquitous nature in almost every aspect, where computational intelligence is used. This paper intends to focus on the comparative study of two popular and robust bio mimic strategies used in computer engineering, namely Particle Swarm Optimization (PSO) and Cuckoo Search (CS). According to the results, CS outperforms PSO. The performance comparison of both algorithms is implemented in the form of problem specific distance functions rather than an algorithmic distance function. Also an attempt is taken to examine the claim that CS has the same effectiveness of finding the true global optimal solution as the PSO but with significantly better computational efficiency, which means less function evaluations.