Effectiveness of firefly algorithm based neural network in time series forecasting

Global optimization techniques such as Particle Swarm Optimizers (PSO) and Genetic Algorithm (GA) are now widely used for training Artificial Neural Networks (NN), particularly in time series forecasting problems. Firefly algorithm (FA) is a relatively new addition to the family of population based optimization technique that has shown promising result in a number of problems. In this work, we evaluate the effectiveness of FA trained NN in time series forecasting. In the experiments, three well known time series were used to evaluate the performance. Results obtained were compared with results from both PSO and Resilient Propagation (RPROP) trained NNs. FA based NN performed very well in forecasting all the time series considered, outperforming the bench-marks in two out of the three problems.Keywords: Time series, Artificial Neural Network, Firefly Algorithm, Particle Swarm Optimization, Overfittin

A requirement model of an adaptive emergency evacuation center management

One of natural disasters that pose a rising danger and has highest percentage of occurrences is flood. Previous studies on flood disaster have provided solutions to deal with this situation. However, they do not consider a scenario where evacuation centers are drowned due to heavy flood and these studies do not provide any requirement models which can be used as reference guides to build similar systems. This study proposes a requirement model for a decision aid model for evacuation center management which is capable of providing smart solutions for relocation of victims to other evacuation centers when they were almost drowned. The methodology used in this study consists of five phases: requirement gathering, conceptual design, development, verification, and preparing thesis & articles for publication. This study has produced a requirement model of the proposed system that consists of a use case diagram, use case specifications, class diagrams, and sequence diagrams, which has been reviewed by the experts by using inspection method. The prototype has been evaluated through a functional testing. The proposed requirement model can be used as a reference model for developers in producing similar evacuation center management system

The application of firefly algorithm in an adaptive emergency evacuation centre management (AEECM) for dynamic relocation of flood victims

Flood evacuation centre is defined as a temporary location or area of people from disaster particularly flood as a rescue or precautionary measure. Gazetted evacuation centres are normally located at secure places which have small chances from being drowned by flood.However, due to extreme flood several evacuation centres in Kelantan were unexpectedly drowned.Currently, there is no study done on proposing a decision support aid to reallocate victims and resources of the evacuation centre when the situation getting worsens.Therefore, this study proposes a decision aid model to be utilized in realizing an adaptive emergency evacuation centre management system. This study undergoes two main phases; development of algorithm and models, and development of a web-based and mobile app.The proposed model operates using Firefly multi-objective optimization algorithm that creates an optimal schedule for the relocation of victims and resources for an evacuation centre.The proposed decision aid model and the adaptive system can be applied in supporting the National Security Council’s respond mechanisms for handling disaster management level II (State level) especially in providing better management of the flood evacuating centres

Improved cuckoo search based neural network learning algorithms for data classification

Artificial Neural Networks (ANN) techniques, mostly Back-Propagation Neural Network (BPNN) algorithm has been used as a tool for recognizing a mapping function among a known set of input and output examples. These networks can be trained with gradient descent back propagation. The algorithm is not definite in finding the global minimum of the error function since gradient descent may get stuck in local minima, where it may stay indefinitely. Among the conventional methods, some researchers prefer Levenberg-Marquardt (LM) because of its convergence speed and performance. On the other hand, LM algorithms which are derivative based algorithms still face a risk of getting stuck in local minima. Recently, a novel meta-heuristic search technique called cuckoo search (CS) has gained a great deal of attention from researchers due to its efficient convergence towards optimal solution. But Cuckoo search is prone to less optimal solution during exploration and exploitation process due to large step lengths taken by CS due to Levy flight. It can also be used to improve the balance between exploration and exploitation of CS algorithm, and to increase the chances of the egg’s survival. This research proposed an improved CS called hybrid Accelerated Cuckoo Particle Swarm Optimization algorithm (HACPSO) with Accelerated particle Swarm Optimization (APSO) algorithm. In the proposed HACPSO algorithm, initially accelerated particle swarm optimization (APSO) algorithm searches within the search space and finds the best sub-search space, and then the CS selects the best nest by traversing the sub-search space. This exploration and exploitation method followed in the proposed HACPSO algorithm makes it to converge to global optima with more efficiency than the original Cuckoo Search (CS) algorithm. Finally, the proposed CS hybrid variants such as; HACPSO, HACPSO-BP, HACPSO-LM, CSBP, CSLM, CSERN, and CSLMERN are evaluated and compared with conventional Back propagation Neural Network (BPNN), Artificial Bee Colony Neural Network (ABCNN), Artificial Bee Colony Back propagation algorithm (ABC-BP), and Artificial Bee Colony Levenberg-Marquardt algorithm (ABC-LM). Specifically, 6 benchmark classification datasets are used for training the hybrid Artificial Neural Network algorithms. Overall from the simulation results, it is realized that the proposed CS based NN algorithms performs better than all other proposed and conventional models in terms of CPU Time, MSE, SD and accuracy

Adaptive emergency evacuation centre management for dynamic relocation of flood victims using firefly algorithm

Situations during bad flood demands a systematic management of evacuation centres. Flood evacuation centres which are gazeted as temporary locations to evacuate flood victims have chances from being drowned by flood. Problem occurs to relocate flood victims when the evacuations centres are flooded. Currently, there is no study done on proposing a decision support aid to reallocate victims and resources of the evacuation centre when the situation getting worsens. Therefore, this article proposes a decision aid model to be utilized in realizing an adaptive emergency evacuation centre management system. This study undergoes two main phases; development of algorithm and models, and development of a web-based and mobile app. The proposed model operates using Firefly multi-objective optimization algorithm that creates an optimal schedule for the relocation of victims and resources for an evacuation centre. The proposed decision aid model and the adaptive system can be applied in supporting the National Security Council’s respond mechanisms for handling disaster management level II (State level) especially in providing better management of the flood evacuating centres

Adaptive Emergency Evacuation Centre Management for Dynamic Relocation of Flood Victims using Firefly Algorithm

Situations during bad flood demands a systematic management of evacuation centres. Flood evacuation centres which are gazeted as temporary locations to evacuate flood victims have chances from being drowned by flood. Problem occurs to relocate flood victims when the evacuations centres are flooded. Currently, there is no study done on proposing a decision support aid to reallocate victims and resources of the evacuation centre when the situation getting worsens. Therefore, this article proposes a decision aid model to be utilized in realizing an adaptive emergency evacuation centre management system. This study undergoes two main phases; development of algorithm and models, and development of a web-based and mobile app. The proposed model operates using Firefly multi-objective optimization algorithm that creates an optimal schedule for the relocation of victims and resources for an evacuation centre. The proposed decision aid model and the adaptive system can be applied in supporting the National Security Council’s respond mechanisms for handling disaster management level II (State level) especially in providing better management of the flood evacuating centres

Document clustering based on firefly algorithm

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 the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization.This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering.We present two variants of FA; Weight- based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFAII).The difference between the two algorithms is that the WFAII, includes a more restricted condition in determining members of a cluster.The proposed FA methods are later evaluated using the 20Newsgroups dataset.Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. The obtained results demonstrated that the WFAII outperformed the WFA, PSO, K-means and FA-Kmeans. This result indicates that a better clustering can be obtained once the exploitation of a search solution is improved

Firefly Algorithm for adaptive emergency evacuation center management

Flood disaster is among the most devastating natural disasters in the world, claiming more lives and causing property damage.The pattern of floods across all continents has been changing, becoming more frequent, intense and unpredictable for local communities.Due to unforeseen scenarios, some evacuation centers that host the flood victims may also be drowned.Hence, prime decision making is required to relocate the victims and resources to a safer center.This study proposes a Firefly Algorithm (FA) to be employed in an emergency evacuation center management. Experimental analysis of a minimization problem was performed to compare the solutions produced by FA and the ones generated using Tabu Search. Results show that the proposed FA produced solutions with smaller utility value, hence indicating that it is better than the benchmark method

Adaptive firefly algorithm for hierarchical text clustering

Text clustering is essentially used by search engines to increase the recall and precision in information retrieval. As search engine operates on Internet content that is constantly being updated, there is a need for a clustering algorithm that offers automatic grouping of items without prior knowledge on the collection. Existing clustering methods have problems in determining optimal number of clusters and producing compact clusters. In this research, an adaptive hierarchical text clustering algorithm is proposed based on Firefly Algorithm. The proposed Adaptive Firefly Algorithm (AFA) consists of three components: document clustering, cluster refining, and cluster merging. The first component introduces Weight-based Firefly Algorithm (WFA) that automatically identifies initial centers and their clusters for any given text collection. In order to refine the obtained clusters, a second algorithm, termed as Weight-based Firefly Algorithm with Relocate (WFAR), is proposed. Such an approach allows the relocation of a pre-assigned document into a newly created cluster. The third component, Weight-based Firefly Algorithm with Relocate and Merging (WFARM), aims to reduce the number of produced clusters by merging nonpure clusters into the pure ones. Experiments were conducted to compare the proposed algorithms against seven existing methods. The percentage of success in obtaining optimal number of clusters by AFA is 100% with purity and f-measure of 83% higher than the benchmarked methods. As for entropy measure, the AFA produced the lowest value (0.78) when compared to existing methods. The result indicates that Adaptive Firefly Algorithm can produce compact clusters. This research contributes to the text mining domain as hierarchical text clustering facilitates the indexing of documents and information retrieval processes