  Metaheuristics are popularly used in various fields, and they have attracted
much attention in the scientific and industrial communities. In recent years,
the number of new metaheuristic names has been continuously growing. Generally,
the inventors attribute the novelties of these new algorithms to inspirations
from either biology, human behaviors, physics, or other phenomena. In addition,
these new algorithms, compared against basic versions of other metaheuristics
using classical benchmark problems without shift/rotation, show competitive
performances. In this study, we exhaustively tabulate more than 500
metaheuristics. To comparatively evaluate the performance of the recent
competitive variants and newly proposed metaheuristics, 11 newly proposed
metaheuristics and 4 variants of established metaheuristics are comprehensively
compared on the CEC2017 benchmark suite. In addition, whether these algorithms
have a search bias to the center of the search space is investigated. The
results show that the performance of the newly proposed EBCM (effective
butterfly optimizer with covariance matrix adaptation) algorithm performs
comparably to the 4 well performing variants of the established metaheuristics
and possesses similar properties and behaviors, such as convergence, diversity,
exploration and exploitation trade-offs, in many aspects. The performance of
all 15 of the algorithms is likely to deteriorate due to certain
transformations, while the 4 state-of-the-art metaheuristics are less affected
by transformations such as the shifting of the global optimal point away from
the center of the search space. It should be noted that, except EBCM, the other
10 new algorithms proposed mostly during 2019-2020 are inferior to the well
performing 2017 variants of differential evolution and evolution strategy in
terms of convergence speed and global search ability on CEC 2017 functions.
