  Nowadays, we are immersed in tens of newly-proposed evolutionary and
swam-intelligence metaheuristics, which makes it very difficult to choose a
proper one to be applied on a specific optimization problem at hand. On the
other hand, most of these metaheuristics are nothing but slightly modified
variants of the basic metaheuristics. For example, Differential Evolution (DE)
or Shuffled Frog Leaping (SFL) are just Genetic Algorithms (GA) with a
specialized operator or an extra local search, respectively. Therefore, what
comes to the mind is whether the behavior of such newly-proposed metaheuristics
can be investigated on the basis of studying the specifications and
characteristics of their ancestors. In this paper, a comprehensive evaluation
study on some basic metaheuristics i.e. Genetic Algorithm (GA), Particle Swarm
Optimization (PSO), Artificial Bee Colony (ABC), Teaching-Learning-Based
Optimization (TLBO), and Cuckoo Optimization algorithm (COA) is conducted,
which give us a deeper insight into the performance of them so that we will be
able to better estimate the performance and applicability of all other
variations originated from them. A large number of experiments have been
conducted on 20 different combinatorial optimization benchmark functions with
different characteristics, and the results reveal to us some fundamental
conclusions besides the following ranking order among these metaheuristics,
{ABC, PSO, TLBO, GA, COA} i.e. ABC and COA are the best and the worst methods
from the performance point of view, respectively. In addition, from the
convergence perspective, PSO and ABC have significant better convergence for
unimodal and multimodal functions, respectively, while GA and COA have
premature convergence to local optima in many cases needing alternative
mutation mechanisms to enhance diversification and global search.
