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As a comparatively new algorithm of swarm intelligence, the dynamic search fireworks algorithm (dynFWA) imitates the explosion procedure of fireworks. With the goal of achieving global optimization and further boosting performance of dynFWA, adaptive parameters are added in this present study, called dynamic search fireworks algorithm with adaptive parameters (dynFWAAP). In this novel dynFWAAP, a self-adaptive method is used to tune the amplification coefficient Ca and the reduction coefficient Cr for fast convergence. To balance exploration and exploitation, the coefficient of amplitude α and the coefficient of sparks β are also adapted, and a new selection operator is proposed. Evaluated on twelve benchmark functions, it is evident from the experimental results that the dynFWAAP significantly outperformed the three variants of fireworks algorithms (FWA) based on solution accuracy and performed best in other four algorithms of swarm intelligence in terms of time cost and solution accuracy.

In this chapter, a hybrid bare bones fireworks algorithm (HBBFWA) is proposed and its application in solving the load flow problem of islanded microgrid is demonstrated. The hybridization is carried out by updating the positions of generated sparks with the help of grasshopper optimization algorithm (GOA) mimicking the swarming behavior of grasshoppers. The purpose of incorporating GOA with bare bones fireworks algorithm (BBFWA) is to enhance the global searching capability of conventional BBFWA for complex optimization problems. The proposed HBBFWA is applied to perform the load flow analysis of a modified IEEE 37-Bus system. The performance of the proposed HBBFWA is compared against the performance of BBFWA in terms of computational time, convergence speed, and number of iterations required for convergence of the load flow problem. Moreover, standard statistical analysis test such as the independent sample t-test is conducted to identify statistically significant differences between the two algorithms.

A new report on childhood obesity is published every so often. The bad habits of food and the increasingly sedentary life of children in a border society has caused an alarming increase in the cases of children who are overweight or obese. Formerly, it seemed to be a problem of countries with unhealthy eating habits, such as the United States or Mexico in Latin America, where junk food is part of the diet in childhood. However, obesity is a problem that we already have around the corner and that is not so difficult to fight in children. In the present research the development of an application that reduces the problem of the lack of movement in the childhood of a smart city is considered a future problem which it is the main contribution, coupled with achieving an innovative way of looking for an Olympic sport without the complexity of physically moving to a space with high maintenance costs and considering the adverse weather conditions.

This chapter presents Interval Type 2 Fuzzy Fireworks Algorithm for clustering (IT2FWAC). It is an optimization method for finding the optimal number of clusters based on the centroid features which uses the Fireworks Algorithm (FWA), but with a dynamic adjustment of parameters using an Interval Type 2 Fuzzy Inference System (IT2FIS). Three variations of the IT2FWAC are proposed to find the optimal number of clusters for different datasets: IT2FWAC -I, IT2FWAC -II, and IT2FWAC –III. They are explained in detail.

The Dynamic Search Fireworks Algorithm (dynFWA) is an effective algorithm for solving optimization problems. However, dynFWA is easy to fall into local optimal solutions prematurely and it also provides a slow convergence rate. To address these problems, an improved dynFWA (IdynFWA) is proposed in this chapter. In IdynFWA, the population is first initialized based on opposition-based learning. The adaptive mutation is proposed for the core firework (CF) which chooses whether to use Gaussian mutation or Levy mutation for the CF according to the mutation probability. A new selection strategy, namely disruptive selection, is proposed to maintain the diversity of the algorithm. The results show that the proposed algorithm achieves better overall performance on the standard test functions. Meanwhile, IdynFWA is used to optimize the Extreme Learning Machine (ELM), and a virtual machine fault warning model is proposed based on ELM optimized by IdynFWA. The results show that this model can achieve higher accuracy and better stability to some extent.

In recent years, computer technology and artificial intelligence have developed rapidly, and research in the field of mobile robots has continued to deepen with development of artificial intelligence. Path planning is an essential content of mobile robot navigation of computing a collision-free path between a starting point and a goal. It is necessary for mobile robots to move and maneuver in different kinds of environment with objects and obstacles. The main goal of path planning is to find the optimal path between the starting point and the target position in the minimal possible time. A new firework algorithm (FWA) integrated with a graph theory, Dijkstra's algorithm developed for autonomous robot navigation, is proposed in this chapter. The firework algorithm is improved by a local search procedure that a LIDAR-based local navigator algorithm is implemented for local navigation and obstacle avoidance. The grid map is utilized for real-time intelligent robot mapping and navigation. In this chapter, both simulation and comparison studies of an autonomous robot navigation demonstrate that the proposed model is capable of planning more reasonable and shorter, collision-free paths in non-stationary and unstructured environments compared with other approaches.

Financial time series are highly nonlinear and their movement is quite unpredictable. Artificial neural networks (ANN) have ample applications in financial forecasting. Performance of ANN models mainly depends upon its training. Though gradient descent-based methods are common for ANN training, they have several limitations. Fireworks algorithm (FWA) is a recently developed metaheuristic inspired from the phenomenon of fireworks explosion at night, which poses characteristics such as faster convergence, parallelism, and finding the global optima. This chapter intends to develop a hybrid model comprising FWA and ANN (FWANN) used to forecast closing prices series, exchange series, and crude oil prices time series. The appropriateness of FWANN is compared with models such as PSO-based ANN, GA-based ANN, DE-based ANN, and MLP model trained similarly. Four performance metrics, MAPE, NMSE, ARV, and R2, are considered as the barometer for evaluation. Performance analysis is carried out to show the suitability and superiority of FWANN.

This chapter proposes the application of a discrete version of the Fireworks Algorithm (FWA) and a novel PSO-FWA hybrid algorithm to optimize the energy efficiency of a metro railway line. This optimization consists in determining the optimal configuration of the Energy Storage Systems (ESSs) to install in a railway line, including their number, location, and power (kW). The installation of the ESSs will improve the energy efficiency of the system by incrementing the use of the regenerated energy produced by the trains in the braking phases, as the ESSs will store the excess of regenerated energy and return it to the system when necessary. The results for this complex optimization problem produced by the two algorithms are excellent and authors prove that the novel PSO-FWA algorithm proposed in this chapter outperforms the standard FWA.

As a recently developed swarm intelligence algorithm, fireworks algorithm (FWA) is an optimization algorithm with good convergence and extensible properties. Moreover, it is usually able to find the global solutions. The advantages of FWA are both optimization accuracy and convergence speed which endue the FWA with a promising prospect of application and extension. This chapter mainly focuses on the application of FWA in classification problems and the improvement of FWA. Many prior studies around FWA have been produced. The author here probes improvement of FWA and its application in classification. The chapter studies FWA around: (1) Application of FWA in classification problems; (2) Improvement of FWA's candidate solution generation strategy (CSGS), including the employment of self-adaptive mechanisms; (3) Improved SaFWA and classification model. For each part, the author conducts research through theory, experimentation, and results analysis.

This chapter briefly reviews the basic explosion mechanism used in the fireworks algorithm (FWA) and comprehensively investigates relevant research on explosion operations. Since the explosion mechanism is one of the most core operations directly affecting the performance of FWA, the authors focus on analyzing the FWA explosion operation and highlighting two novel explosion strategies: a multi-layer explosion strategy and a scouting explosion strategy. The multi-layer explosion strategy allows an individual firework to perform multiple explosions instead of the single explosion used in the original FWA, where each round of explosion can be regarded as a layer; the scouting explosion strategy controls an individual firework to generate spark individuals one by one instead of generating all spark individuals within the explosion amplitude at once. The authors then introduce several other effective strategies to further improve the performance of FWA by full using the information generated by the explosion operation. Finally, the authors list some open topics for discussion.

Fireworks Algorithm (FWA) has been applied to many fields in recent years, showing a strong ability to solve optimization problems. In this chapter, FWA is applied to some research hotspots in bioinformatics, such as biclustering of gene expression data, disease-gene prediction, and identification of LncRNA-protein interactions. This chapter briefly introduces some backgrounds of bioinformatics and related issues. Through corresponding bioinformatics' problems to optimization problems, some specific optimization functions are constructed and solved by the Fireworks Algorithm. The simulation results illustrate that the fireworks algorithm shows high performance and potential application value in the field of bioinformatics.

The analysis of measured data plays a significant role in enhancing nuclear nonproliferation mainly by inferring the presence of patterns associated with special nuclear materials. Among various types of measurements, gamma-ray spectra is the widest utilized type of data in nonproliferation applications. In this chapter, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. In particular, FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, where non-zero coefficients express the detected signatures. FWA is tested on a set of experimentally obtained measurements optimizing various objective functions—MSE, RMSE, Theil-2, MAE, MAPE, MAP—with results exhibiting its potential in providing highly accurate and precise signature detection. Furthermore, FWA is benchmarked against genetic algorithms and multiple linear regression, showing its superiority over those algorithms regarding precision with respect to MAE, MAPE, and MAP measures.

Particle Swarm Optimization (PSO) and Enhanced Fireworks Algorithm (EFWA) are two diverse optimization techniques of the Swarm Intelligence paradigm. The inspiration of the former comes from animate swarms like those of birds and fish efficiently hunting for prey, while that of the latter comes from inanimate swarms like those of fireworks illuminating the night sky. This novel study, aimed at extending the application of these two Swarm Intelligence techniques to supervised learning, compares and contrasts their performance in training a neural network to perform the task of classification on datasets. Both the techniques are found to be speedy and successful in training the neural networks. Further, their prediction accuracy is also found to be high. Except in the case of two datasets, the training and prediction accuracies of the Enhanced Fireworks Algorithm driven neural net are found to be superior to those of the Particle Swarm Optimization driven neural net.

De novo motif discovery is essential in understanding the cis-regulatory processes that play a role in gene expression. Finding unknown patterns of unknown lengths in massive amounts of data has long been a major challenge in computational biology. Because algorithms for motif prediction have always suffered of low performance issues, there is a constant effort to find better techniques. Evolutionary methods, including swarm intelligence algorithms, have been applied with limited success for motif prediction. However, recently developed methods, such as the Fireworks Algorithm (FWA) which simulates the explosion process of fireworks, may show better prospects. This paper describes a motif finding algorithm based on FWA that maximizes the Kullback-Leibler divergence between candidate solutions and the background noise. Following the terminology of FWA's framework, the candidate motifs are fireworks that generate additional sparks (i.e. derived motifs) in their neighborhood. During the iterations, better sparks can replace the fireworks, as the Fireworks Motif Finder (FW-MF) assumes a one occurrence per sequence mode. The results obtained on a standard benchmark for promoter analysis show that our proof of concept is promising.

Nowadays, Social networks and cloud services contain billions of users over the planet. Instagram, Facebook and other networks give the opportunity to share images. Users upload millions of pictures each day, including personal images. Another domain, which concerns medical studies, requires a highly sensitive medical images that retain personal details close to patients. Image perturbation have attracted a great deal of attention in the last few years. Many works concerning image ciphering and perturbing have been published. This paper deals with the problem of image perturbation for privacy preserving. The authors build three new systems that consist of hiding small details in pictures by rotating some pixels. Their models use two algorithms: the first one involves a simulation of the firework algorithm in which they place fireworks on selected pixels then represents sparks as rotation processes. The second system consists of a model of rotation based perturbation using iterated local search algorithm (ILS) with 2 optimization stages. Meanwhile, the third one consists of using the same principle of the previous system except by using the ILS algorithm with 3 optimization stages.

Fireworks Algorithm (FWA) is a recently developed Swarm Intelligence Algorithm (SIA), which has been successfully used in diverse domains. When applied to complicated problems, many function evaluations are needed to obtain an acceptable solution. To address this critical issue, a GPU-based variant (GPU-FWA) was proposed to greatly accelerate the optimization procedure of FWA. Thanks to the active studies on FWA and GPU computing, many advances have been achieved since GPU-FWA. In this paper, a novel GPU-based FWA variant, Attract-Repulse FWA (AR-FWA), is proposed. AR-FWA introduces an efficient adaptive search mechanism (AFW Search) and a non-uniform mutation strategy for spark generation. Compared to the state-of-the-art FWA variants, AR-FWA can greatly improve the performance on complicated multimodal problems. Leveraging the edge-cutting dynamic parallelism mechanism provided by CUDA, AR-FWA can be implemented on the GPU easily and efficiently.

Inspired by fireworks explosion at night, conventional fireworks algorithm (FWA) was developed in 2010. Since then, several improvements and applications were proposed to improve the efficiency of FWA. In this paper, the conventional fireworks algorithm is first summarized and three improved fireworks algorithms are provided. By changing the ways of calculating the numbers and amplitudes of sparks in fireworks' explosion, the improved FWA algorithms become more reasonable and explainable. In addition, the multi-objective fireworks algorithm and the graphic processing unit (GPU) based fireworks algorithm are also presented, particularly the GPU based fireworks algorithm is able to speed up the optimization process considerably. Extensive experiments on 13 benchmark functions demonstrate that the three improved fireworks algorithms significantly increase the accuracy of found solutions, yet decrease the running time dramatically. At last, some applications of fireworks algorithm are briefly described, while its shortcomings and future research directions are identified.