Firefly algorithm (FA) is a recently proposed swarm intelligence optimization technique, which has shown good performance on many optimization problems. In the standard FA and its most variants, a firefly moves to other brighter fireflies. If the current firefly is brighter than another one, the current one will not be conducted any search. In this paper, we propose a new firefly algorithm (cal...

Firefly algorithm (FA) is recently developed nature-inspired metaheuristic based on the flashing patterns and behaviour of fireflies. Original FA was successfully applied to solve unconstrained optimization problems. This paper presents firefly algorithm to solve constrained optimization problems. For constraint handling, firefly algorithm uses certain feasibility-based rules in order to guide ...

Selecting approaches with appropriate accuracy and suitable speed for the purpose of making decision is one of the managers’ challenges. Also investing decision is one of the main decisions of managers and it can be referred to securities transaction in financial markets which is one of the investments approaches. When some assets and barriers of real world have been considered, optimization of...

Firefly Algorithm (FA) is a powerful swarm intelligence algorithm i spired by the flash phenomenon of the fireflies. However, it has weaknesses on optimizing high-dimensional problems. This paper presents an improved FA named Adaptive Firefly Algorithm (AFA). In the new algorithm, three strategies are proposed to improve its adaptability and overcome its weaknesses. The algorithm is tested on a...

In this work, firefly algorithm (FA) is used in training feed-forward neural networks (FNN) for classification purpose. In experiments, three well-known classification problems have been used to evaluate the performance of the proposed FA. The experimental results obtained by FA were compared with the results reported by artificial bee colony (ABC) algorithm and genetic algorithm (GA). Also, si...

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining th...

This paper presents an efficient meta-heuristic algorithm for optimization of double-layer scallop domes subjected to earthquake loading. The optimization is performed by a combination of harmony search (HS) and firefly algorithm (FA). This new algorithm is called harmony search firefly algorithm (HSFA). The optimization task is achieved by taking into account geometrical and material nonlinear...

In this study an efficient meta-heuristic is proposed for layout optimization of truss structures by combining cellular automata (CA) and firefly algorithm (FA). In the proposed meta-heuristic, called here as cellular automata firefly algorithm (CAFA), a new equation is presented for position updating of fireflies based on the concept of CA. Two benchmark examples of truss structures are presen...

The firefly algorithm (FA), which is usually used in optimization problems, is a stochastic, population-based algorithm inspired by the intelligent, collective behavior of fireflies in nature. In the standard FA, each firefly in each neighborhood is compared with other fireflies, and the less-bright firefly moves toward the brighter one (in the maximization optimization). In fact, in the standa...

we propose a novel hybrid algorithm named ACO-FA, which integrates the merits of ant colony optimization (ACO) with firefly algorithm (FA) to solve unconstrained optimization problems. The main feature of the hybrid algorithm is to hybridize the solution construction mechanism of the ACO with the FA. In our hybrid algorithm, the initial solutions are generated randomly from the search space, an...