Comparative assessment of five metaheuristic methods on distinct problems

Year 2019, , 879 - 898, 29.09.2019

Ali Mortazavi

https://doi.org/10.24012/dumf.585790

Cited By: 7

Abstract

Metaheuristic algorithms belong to the non-gradient
based optimization methods. Accomplished studies in this area reveal that each
of these methods mostly has its own affirmative and inconvenient aspects. So
that, one might provide a high level of exploration while the other can perform
a great level of exploitation. Thus, selecting the proper and efficient
algorithm for a problem can highly affect both the convergence rate and the
accuracy level. There are several different metaheuristic algorithms have been
announced in the technical literature in the last decade. Therefore, performing
an objective comparative assessment over some of these methods can provide a
fundamental and fair attitude for researchers either to select an algorithm
which is more fitted with their target(s) or to develop even more efficient
methods. So, the current investigation deals with evaluating and comparing of
five different metaheuristic techniques emerged from ten years ago up to now.
The selected methods can be sorted chronologically as Firefly Algorithm (FA),
Teaching and Learning Based Algorithm (TLBO), Drosophila Food Search (DSO)
method, Ions Motion Optimization (IMO) and Butterfly Optimization Algorithm
(BOA). Different properties of these algorithms as convergence rate, diversity
variation, complexity and accuracy level of the final solutions are compared on
both constrained and non-constrained optimization problems include mathematical
functions, mechanical and structural problems. The results show that the cited
methods show different performance depending on the type of the optimization
problem but overally BOA and TLBO outperform the other algorithms on
non-constrained and constrained problems, respectively.

Keywords

Optimization techniques, Metaheuristic algorithms, Constrained optimization problems

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