Features of Firefly Algorithm Inceasing Females
Masaki Takeuchi

Haruna Matsushita

Yoko Uwate and Yoshifumi Nishio

Department of Electrical
Department of Electronics
Department of Electricaland Electronic Engineering,and Information Engineering,and Electronic Engineering,
Tokushima University
Kagawa University
Tokushima University
2-1 Minami-Josanjima,
2217-20 Hayashi-cho,
2-1 Minami-Josanjima,
Tokushima 770–8506, Japan
Takamatsu, Kagawa 761-0396, Japan
Tokushima 770–8506, Japan
E-mail: {masaki}@ee.tokushima-u.ac.jp E-mail: haruna@eng.kagawa-u.ac.jp E-mail: {uwate,nishio}@ee.tokushima-u.ac.jp

S UMMARY
The solution of optimization problems has recently become increasingly important. Most optimization problems are nonlinearwith many constraints. Consequently, optimization algorithms must be efficient to find optimal solutions. Stochastic algorithms,
one category of optimization algorithms, are efficient optimization algorithms. Stochastic algorithms have a deterministiccomponent and a random component. Almost all algorithms having only a deterministic component are local search algorithms,
for which there is a risk of being trapped at local optima. However, the ramdom conponet of stochastic algorithms makes it
possible to escape from such local optima.
One type of stochastic algorithm is swarm intelligence algorithms, which are based on the behavior of animals andinsects. Representative examples are particle swarm optimization (PSO) [1], ant colony optimization (ACO), and the Firefly
Algorithm (FA) [2]–[4].
In the conventional FA, all fireflies are unisex. However, in the real world, there are males and females. Animals having
variation among individuals have a greater chance of surviving than those without variation. In the case of solving optimization
problems, we also consider that veriation among individuals will lead to a veriety of solutions. These solutions may include theglobal optimal solution. Therefore, we have proposed a new FA that distinguishes the sex of fireflies [5]. This method is called
the firefly algorithm distinguishing between males and females (FADMF). In FADMF, the movements of males and females
are defined from their physical differences. Therefore, the movements of males and females are different. We investigate thefeatures of FADMF using 28 well-known benchmark functions.
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