Firefly algorithm (FA) is an optimization technique inspired by an intelligent behavior of firefly swarms. This paper presents an application of FA to Gaussian process (GP)-based prediction of electric power damage caused by typhoons. The GP prior model for prediction is trained by a separable least-squares (LS) approach combining the linear LS method with FA. The proposed prediction system yie...

Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by the behavior of fireflies in nature. Though efficient, its parameters do not change during iterations, which is also true for particle swarm optimization. This paper propose a hybrid model to improve the FA algorithm by introducing learning automata to adjust firefly behavior, and using genetic algorithm to...

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly a...

Corresponding Author: Athraa Jasim Mohammed School of Computing, Universiti Utara Malaysia, Kedah, Malaysia Email: [email protected] Abstract: Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters. Various efforts have been put to address such drawback and this includes...

Color Image quantization (CQ) is an important problem in computer graphics, image and processing. The aim of quantization is to reduce colors in an image with minimum distortion. Clustering is a widely used technique for color quantization; all colors in an image are grouped to small clusters. In this paper, we proposed a new hybrid approach for color quantization using firefly algorithm (FA) a...

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...

As a new swarm intelligence optimization method, firefly algorithm shows good performance on many complex optimization problems. However, due to the fixed parameters of FA, it is difficult to adapt to environmental changing during the iteration process, and FA easily lose its diversity and lead to premature convergence. In this paper, an adaptive step firefly algorithm based on population diver...

Antenna array is the most important aspect to improve the communication process. The objective of this paper are to minimize the maximum side lobe level (SLL) and perform null steering for linear antenna arrays by controlling different parameters of the array elements (phase, position and amplitude). The Nature inspired optimization methods like Taguchi’s Optimization Method (TM), Self-Adaptive...

Recently huge amount of data is available in the field of medicine that helps the doctors in diagnosing diseases when analysed. Data mining techniques can be applied to these medical data to extract knowledge so that disease prediction becomes accurate and easier. In this work, cardiotocogram (CTG) data is analysed using Support Vector Machine (SVM) for predicting fetal risk. Opposition based f...

The speech signal enhancement is essential to obtain clean speech signal from noisy signal. For multimodal optimization, the natural-inspired algorithms such as Firefly Algorithm (FA) are better. The proposed algorithm contains preprocessing module, optimization module and spectral filtering module. Here, Loizou’s and Aurora databases are considered for signals. In this paper the Perceptional E...