Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracte...

—Many real world problems are mostly time varying optimization problems, which require special mechanisms to detect changes in environment and then response to them. In this paper, combination of learning automata and firefly algorithm in order to improve the performance of firefly algorithm in dynamic environments has been proposed. In the algorithm, the firefly algorithm has been equipped wit...

Firefly Algorithm (FA) is one of the recent swarm intelligence methods developed by Xin-She Yang in 2008 [12]. FA is a stochastic, nature-inspired, metaheuristic algorithm that can be applied for solving the hardest optimization problems. The main goal of this paper is to analyze the influence of changing some parameters of the FA when solving bound constrained optimization problems. One of the...

This work presents a study of a new binary coded firefly algorithm. The firefly algorithm is a novel nature-inspired metaheuristic, inspired by the social behavior of fireflies, which is being applied to solve many optimization problems. We test the proposed binary coded firefly algorithm solving the non-unicost set covering problem which is a well-known NP-hard discrete optimization problem wi...

Feed-forward neural networks are popular classification tools which are broadly used for early detection and diagnosis of breast cancer. In recent years, a great attention has been paid to bio-inspired optimization techniques due to its robustness, simplicity and efficiency in solving complex optimization problems. In this paper, it is intended to introduce a Genetic Algorithm based Firefly Alg...

Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by fireflies behavior in nature. Each firefly movement is based on absorption of the other one. In this paper to stabilize firefly’s movement, it is proposed a new behavior to direct fireflies movement to global best if there was no any better solution around them. In addition to increase convergence speed it ...

In this paper we propose a fast structure-preserving algorithm for computing the singular value decomposition of quaternion matrices. The algorithm is based on the structurepreserving bidiagonalization of the real counterpart for quaternion matrices by applying orthogonal JRS-symplectic matrices. The algorithm is efficient and numerically stable. 2014 Elsevier Inc. All rights reserved.

Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm furth...

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchro...

In this paper, we apply the quaternion framework for color images to a fragile watermarking algorithm with the objective of multimedia integrity protection (Quaternion Karhunen-Loève Transform Fragile Watermarking (QKLT-FW)). The use of quaternions to represent pixels allows to consider the color information in a holistic and integrated fashion. We stress out that, by taking advantage of the ho...