Fireworks Algorithm

As a revolutionary swarm intelligence algorithm, fireworks algorithm (FWA) is designed to solve optimization problems. In this paper, the dynamic fireworks algorithm with covariance mutation (dynFWACM) is proposed. After applying the explosion operator, the mutation operator is introduced, which calculates the mean value and covariance matrix of the better sparks and produces sparks according with Gaussian distribution. DynFWACM is compared with the most advanced fireworks algorithms to proof its effectiveness. In addition, 15 functions of CEC 2015 competition on learning based realparameter single objective optimization are used to test the performance of our new proposed algorithm. The experimental results show that dynFWACM outperforms both AFWA and dynFWA, as well as the experimental results of the 15 functions given.

Swarm intelligence algorithms are inherently parallel since different individuals in the swarm perform independent computations at different positions simultaneously. Hence, these algorithms lend themselves well to parallel implementations thereby speeding up the optimization process. FireWorks Algorithm (FWA) is a recently proposed swarm intelligence algorithm for optimization. This work investigates the scalability of the parallelization of the Enhanced FireWorks Algorithm (EFWA), which is an improved version of FWA. We use the MapReduce platform for parallelizing EFWA, investigate its ability to scale, and report on the speedup obtained on different benchmark functions for increasing problem dimensions.

For cancer detection and tissue characterization, DCE-MRI segmentation and lesion detection is a critical image analysis task. To segment breast MR images for lesion detection, a hard-clustering technique with Grammatical Fireworks algorithm (GFWA) is proposed in this paper. GFWA is a Swarm Programming (SP) system for automatically generating computer programs in any language. GFWA is used to create the cluster core for clustering the breast MR images in this article. The presence of noise and intensity inhomogeneities in MR images complicates the segmentation process. As a result, the MR images are denoised at the start, and strength inhomogeneities are corrected in the preprocessing stage. The proposed GFWA-based clustering technique is used to segment the preprocessed MR images. Finally, from the segmented images, the lesions are removed. The proposed approach is tested on 5 patients’ 25 DCE-MRI slices. The proposed method’s experimental findings are compared to those of the Gram...

Prediction of stock index remains a challenging task of the financial time series prediction process. Though different non-linear prediction models are in use, their prediction accuracy does not improve beyond certain level. In order to improve the forecasting accuracy, this paper proposes a chemical reaction optimization based virtual data position (CROVDP) exploration method for financial time series prediction. From the given data points in the training set, CROVDPs are explored by the proposed method and incorporated to the original financial time series to enhance the prediction accuracy of a multilayer perceptron (MLP) based forecasting model. The effectiveness of the proposed method is compared with three deterministic methods such as linear, Lagrange and Taylor interpolation as well as two stochastic methods such as uniform and Gaussian method. To train and validate the models in presence of virtual data positions (VDP), ten real stock indices are used. Simulation results show that the models developed with the proposed CROVDP exploration method yields much better prediction accuracy than others.

Exchange rates are highly fluctuating by nature, thus difficult to forecast. Artificial neural networks (ANN) have proved to be better than statistical methods. Inadequate training data may lead the model to reach suboptimal solution resulting, poor accuracy as ANN-based forecasts are data driven. To enhance forecasting accuracy, we suggests a method of enriching training dataset through exploring and incorporating of virtual data points (VDPs) by an evolutionary method called as fireworks algorithm trained functional link artificial neural network (FWA-FLN). The model maintains the correlation between the current and past data, especially at the oscillation point on the time series. The exploring of a VDP and forecast of the succeeding term go consecutively by the FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other models trained similarly and produces far better prediction accuracy.

Recently, multimodal multiobjective optimization problems (MMOPs) have received increasing attention. Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible. Although some evolutionary algorithms for them have been proposed, they mainly focus on the convergence rate in the decision space while ignoring solutions diversity. In this paper, we propose a new multiobjective fireworks algorithm for them, which is able to balance exploitation and exploration in the decision space. We first extend a latest single-objective fireworks algorithm to handle MMOPs. Then we make improvements by incorporating an adaptive strategy and special archive guidance into it, where special archives are established for each firework, and two strategies (i.e., explosion and random strategies) are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives. Finally, we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems. Experimental results show that the proposed algorithm is superior to compared algorithms in solving them. Also, its runtime is less than its peers'.

Exchange rates are highly fluctuating by nature, thus difficult to forecast. Artificial neural networks (ANN) have proved to be better than statistical methods. Inadequate training data may lead the model to reach suboptimal solution resulting, poor accuracy as ANN-based forecasts are data driven. To enhance forecasting accuracy, we suggests a method of enriching training dataset through exploring and incorporating of virtual data points (VDPs) by an evolutionary method called as fireworks algorithm trained functional link artificial neural network (FWA-FLN). The model maintains the correlation between the current and past data, especially at the oscillation point on the time series. The exploring of a VDP and forecast of the succeeding term go consecutively by the FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other models trained similarly and produces far better prediction accuracy.

This thesis covers two types of contributions: formulation of network optimization problems and algorithms to solve these optimization problems. We propose resource assignment problem in Internet of Things network (IoTN) with three nodes: IoT, core cluster node (CCN) and base station (BS). The assignment of resources, such as CPU and memory, from IoTs to CCNs, and CCNs to BSs is a challenging task. The objective of the problem is to minimize the weighted sum of computational power at CCNs and transmission power between IoTs-CCNs and CCNs-BSs radio connections. We also propose a broadband wireless network (BWN) wherein the planning of BSs, relay stations (RSs), and their connections to subscribers minimizes the overall (i.e., weighted sum of the hardware and operational) cost of the network and reformulate a virtual machine (VM) placement to minimize power consumption in a datacenter. The (re)formulated problems are integer programming problem and finding optimal solutions for these problems by using exhaustive search is not practical due to demand of high computing resources. The practical approach is to minimize power in IoT network and VM placement, and plan broadband wireless network using population-based heuristic algorithms. We propose swarm intelligence-based algorithms, that is, two versions of the discrete fireworks algorithm (DFWA) and its variants. The performance of these new algorithms is compared against the low-complexity Biogeography-based Optimization (LC-BBO) algorithm, the Discrete Artificial Bee Colony (DABC) algorithm, and the Genetic Algorithm (GA). Our simulation results and statistical test demonstrate that the proposed algorithm can comparatively find good-quality solutions with moderate computing resources.

As a revolutionary swarm intelligence algorithm, fireworks algorithm (FWA) is designed to solve optimization problems. In this paper, the dynamic fireworks algorithm with covariance mutation (dynFWACM) is proposed. After applying the explosion operator, the mutation operator is introduced, which calculates the mean value and covariance matrix of the better sparks and produces sparks according with Gaussian distribution. DynFWACM is compared with the most advanced fireworks algorithms to proof its effectiveness. In addition, 15 functions of CEC 2015 competition on learning based realparameter single objective optimization are used to test the performance of our new proposed algorithm. The experimental results show that dynFWACM outperforms both AFWA and dynFWA, as well as the experimental results of the 15 functions given.

As a revolutionary swarm intelligence algorithm, fireworks algorithm (FWA) is designed to solve optimization problems. In this paper, the dynamic fireworks algorithm with covariance mutation (dynFWACM) is proposed. After applying the explosion operator, the mutation operator is introduced, which calculates the mean value and covariance matrix of the better sparks and produces sparks according with Gaussian distribution. DynFWACM is compared with the most advanced fireworks algorithms to proof its effectiveness. In addition, 15 functions of CEC 2015 competition on learning based realparameter single objective optimization are used to test the performance of our new proposed algorithm. The experimental results show that dynFWACM outperforms both AFWA and dynFWA, as well as the experimental results of the 15 functions given.

Since fireworks algorithm (FWA) debuted in 2010, a dozen proposals of improvement for FWA had been published in an effort to enhance, refine and optimize accuracy while minimizing calculation speed and volume. In this paper, we introduce the covariance mutation operator into cooperative framework fireworks algorithm (CoFFWA) to create cooperative framework fireworks algorithm with covariance mutation (CoFFWA-CM) in order to solve the Congress on Evolutionary Computation (CEC 2016) competition functions on single objective optimization. The experimental results are calculated on all 10-, 30-, 50- and 100-dimensional functions.

As a revolutionary swarm intelligence algorithm, fireworks algorithm (FWA) is designed to solve optimization problems. In this paper, the dynamic fireworks algorithm with covariance mutation (dynFWACM) is proposed. After applying the explosion operator, the mutation operator is introduced, which calculates the mean value and covariance matrix of the better sparks and produces sparks according with Gaussian distribution. DynFWACM is compared with the most advanced fireworks algorithms to proof its effectiveness. In addition, 15 functions of CEC 2015 competition on learning based real- parameter single objective optimization are used to test the performance of our new proposed algorithm. The experimental results show that dynFWACM outperforms both AFWA and dynFWA, as well as the experimental results of the 15 functions given.