Search

The traditional fireworks algorithm (FWA) is easy to fall into the local extremum and the accuracy of the solution is not high as that the explosion amplitudes and the selection mechanism are inefficient. A FWA based on dynamic search and tournament selection is proposed. The algorithm redefines the original explosion amplitudes, introducing the concept of minimum explosion amplitudes. In order to make the explosion amplitudes calculated dynamically, the number of iterations and the maximum number of iterations are introduced into the updating formula. Meanwhile, without changing the physical meaning of the original algorithm, the formula preserves the fitness value. Updating the new explosion amplitudes by non-linear method, thereby, it achieves the goal of the global search in the early phases and the local search in the late part phases. At the same time, the tournament selection mechanism is used to select some excellent individuals from the population. And then it selects the best one to enter the offspring population until the new population size reaches the population size. It makes the result closer to the desired results and improves the accuracy of the solution. The proposed algorithm balances the global and local search abilities, making the calculation results more accurate. [ABSTRACT FROM AUTHOR]

The accurate and reliable prediction of future runoff is important to guarantee for strengthening water resource optimization and management. The novel contribution of this article is the development of a hybrid model (FWA-ANFIS), which is based on the improvement of the adaptive neuro-fuzzy inference system (ANFIS) with the fireworks algorithm (FWA). The dominant driving factors of runoff are selected from several hydro-meteorological indices (precipitation, soil moisture content, and evaporation) as predictors by correlation coefficient (CC) analysis, mutual information (MI) analysis, correlation analysis and principal component analysis (CC-PCA), mutual information and kernel principal component analysis (MI-KPCA), MI-PCA, and CC-KPCA. The FWA-ANFIS model is applied to the Beiru River, China, with data from 1985–2016 (1985–2012 for model training and 2013–2016 for model prediction). The standard ANFIS, the GA-ANFIS, the PSO-ANFIS, the FWA-ELM, the GA-ELM, and the PSO-ELM are utilized as compared prediction models on the identical dataset. The results indicate that CC-PCA outperforms the other methods regarding the selection of predictors, and FWA-ANFIS has the best performance in terms of the root mean square error, correlation coefficient, and coefficient of determination, followed by the GA-ANFIS, PSO-ANFIS, ANFIS, FWA-ELM, GA-ELM, and PSO-ELM models. Furthermore, the degrees of uncertainty of the models increase in the following order: FWA-ANFIS, GA-ANFIS, PSO-ANFIS, ANFIS, PSO-ELM, GA-ELM, and FWA-ELM. [ABSTRACT FROM AUTHOR]

In this paper, a fireworks algorithm based Gerchberg–Saxton (GS) algorithm for the design of diffractive optical element is developed, named FW-GS algorithm, in which, the explosion mechanism of the fireworks algorithm is introduced to efficiently find the initial phase, thereby ensuring a superior phase solution for the subsequent GS algorithm. By employing this approach to shape a Gaussian beam into a square beam, the results illustrated the notable improvement of 16.10 % in RMSE and 3.29 % in diffractive efficiency when compared to the traditional GS algorithm, while exhibiting stable optimization performance. Furthermore, the proposed algorithm demonstrated adaptability to various shaping targets, with RMSE improved by about 16 % and diffractive efficiency improved by more than 3 % for shaping to circular, annular, and rectangular beams. • A fireworks algorithm based Gerchberg–Saxton (GS) algorithm for the design of beam shaping diffractive optical element is developed, named FW-GS algorithm. • Compared with the traditional GS algorithm, the FW-GS algorithm optimized RMSE and diffractive efficiency when shaping Gaussian beam into square beam, while exhibiting stable optimization performance. • The FW-GS algorithm demonstrated adaptability to various shaping targets. [ABSTRACT FROM AUTHOR]

Many real-world problems can be abstracted as multimodal global optimization, which is one of the main challenges for optimization algorithms due to its complexity. The fireworks algorithm (FWA) is a swarm intelligence optimization algorithm that has been widely studied and applied by virtue of the synergistic property among fireworks. Current FWA variants have poor exploitation capability to handle some locally complex multimodal functions, which greatly limits the application of the FWA to practical problems. To solve the above problems, in this paper, we propose the multi-guiding spark fireworks algorithm (MGFWA) to solve multimodal functions by enhancing FWA exploitation capabilities. Three different strategies which are boosted guiding vector, multi-guiding sparks, and population-based random mapping are designed to boost the guiding vector, enrich the guiding spark diversity, and fix the mapping function, separately. The validity and parameter setting of MGFWA are theoretically analyzed. Experimentally, the results on the CEC2013 and CEC2017 single objective optimization benchmarks illustrate the remarkable performance of the MGFWA compared to other typical optimization algorithms and FWA variants. Moreover, the ablation study shows each of the three parts plays an important role in the algorithm and the efficiency experiment shows the MGFWA can improve the efficiency of guiding sparks by 10%. We believe that the MGFWA can be considered the SOTA variant of the FWA. • Utilizing boosted guiding vector, multi-guiding sparks, and population-based random mapping to enhance the exploitation capability of the fireworks algorithm. • State-of-the-art performance among fireworks algorithm variants. • Superior performance on multimodal global optimization among several typical swarm intelligence optimization algorithms and evolutionary algorithms. • Extensive experiments (about 60 test functions) and theoretical analyses to demonstrate the effectiveness. [ABSTRACT FROM AUTHOR]

A non-linear unrestraint optimization model of mine ventilation network was established which took the minimum total energy consumption of mine ventilation network as optimization objective. In order to improve optimization ability and convergence speed of the model, an opposition-based enhanced fireworks algorithm(OBEFWA) was proposed. Firstly, population initialization strategy based on opposition-based learning and uniform randomization is adopted, and uniform randomization population generated by the strategy is competed with opposition-based population, so that the optimal initial population is selected as starting point of subsequent search. Secondly, fireworks explosion radius is finely controlled, so that explosion radius of fireworks populations of different generations shows non-linear decline, and that of the same population generation is coordinated and distriblted according to their own fitness values. The minimum dynamic threshold is set to decrease waste of search resources. Finally, selection strategy of elite opposition-based learning is adopted to strengthen search for neighborhood of elite fireworks, so as to improve global exploration ability of the algorithm. The experimental results show that total energy consumption of mine ventilation network optimized by OBEFWA decreases about 23.2% which meets adjustment constraints and wind demand of actual ventilation network, and OBEFWA has better optimization effect than particle swarm optimization algorithm and enhanced fireworks algorithm.

Wireless energy replenishment using mobile wireless charging equipment (WCE) has been a hot topic recently in areas of wireless rechargeable sensor networks (WRSNs) and mobile computing. Previous studies of multi-node charging planning usually assumed that the WCE was equipped with sufficient energy to accomplish the charging task. However, the energy of the WCE is limited in practice. Therefore, a multi-node charging planning algorithm with energy-limited WCE in WRSNs is proposed in this paper. Moreover, three charging planning models and their corresponding charging planning algorithms are proposed based on the different real conditions of the WCE: sufficient moving energy and limited charging energy, limited moving energy and sufficient charging energy, as well as both limited moving energy and charging energy. Furthermore, a hybrid simulated annealing discrete fireworks algorithm (HSA-DFWA) is proposed due to the NP-Hard complexity of the charging planning problem. The experiments show that compared to DFWA, MM-ANT, E-greedy, and P-greedy algorithms, the HSA-DFWA outperforms these algorithms empirically.

Aiming at the characteristics of MM* model fault diagnosis, a fireworks algorithm based on a dual population strategy is designed. The dual population of the algorithm is operated independently in parallel, and cooperative operator and optimal operator are cross-executed in the iterative process. The cooperative operator enables two populations to exchange effective information, avoiding the premature maturity of the algorithm. The optimal operator helps to strengthen the global search power of the algorithm and improve the convergence rate of the algorithm. At the same time, the constraint equation is designed, a new fitness function is proposed, and the mutation operator and selection strategy are optimized. The experimental comparison shows that the algorithm improves the efficiency and accuracy of system-level fault diagnosis and has good practicability. Finally, the correctness of the algorithm is proved by theory, and the time complexity of the algorithm is analyzed.

Measurement error occurs if the requirements of undistorted measurements cannot be satisfied by dynamic component characteristics of the sensor. Amplified time constant and longer thermal response time of armored thermocouple is caused by its additional protective cover structure and the inherent thermal inertia of the internal temperature measuring element. Therefore, there is hysteresis dynamic characteristic in actual measurement of sensor. This paper proposes an accurate compensation of armored thermocouple based on fireworks algorithm that balances the contradiction between the accuracy of the existing solution and the complexity of the solution, corrects the measured thermocouple temperature signal. Weight factor is innovatively introduced to design the fitness function of fireworks algorithm, and the iterative process is sectionally executed that achieves the improvement of time constant and thermal response time of armored thermocouple. Finally, results are given in the simulation section. Dynamic compensation time is reduced from 537ms to 130ms, which is 75.79% lower than before compensation result. Moreover, the thermal response time is reduced from 345ms to 110ms, which is 68.16% lower than the before compensation result. From the experimental results, we can observe that the proposed method in this paper can broaden the working bandwidth of the thermocouple sensor through establishing dynamic mathematical model of thermocouple. After compensation, the dynamic characteristics of armored thermocouple are close to bare bead thermocouple without protective sleeve. The proposed method can replace easily damaged bare bead thermocouple to realize accurate measurement in harsh temperature measurement environments without need frequent replacement, which also improves economic benefits. [ABSTRACT FROM AUTHOR]

In recent years, the fireworks algorithm (FWA) has attracted more and more attention due to its strong ability to solve optimization problems. However, the global performance of FWA is significantly affected by the explosion amplitude. In this paper, a dynamic fireworks algorithm with particle swarm optimization (DFWPSO) is developed to improve the global performance of FWA. In DFWPSO, a dynamic explosion amplitude mechanism based on the evolution speed of population, which is dynamically adjusted by evaluating the evolution speed of fitness in each iteration process, is designed to control the global and local searching information. Moreover, a new nonlinear minimal amplitude check strategy based on function decreasing is designed to obtain appropriate amplitude. Furthermore, a new firework updating mechanism based on particle swarm optimization (PSO) is implemented to accelerate the convergence of algorithm and cut down on computing resources. In addition, the selection operator of FWA is abandoned and all fireworks are updated by velocity and current location in each iteration process. To verify the performance of the proposed DFWPSO algorithm, three groups of the benchmark functions are used and tested for experiments. Compared with other variants of FWA and PSO variants, results show that the proposed algorithm performs competitively and effectively. [ABSTRACT FROM AUTHOR]

Functional module detection in protein-protein interaction (PPI) network is one important content of the proteomics research in the post-genomic era. Nowadays the swarm intelligence and evolutionary based approaches have become effective ways for detecting functional modules. This paper proposes a novel hybrid approach of fireworks algorithm and differential evolution strategies for functional module detection in PPI networks (called HFADE-FMD). HFADE-FMD first initializes each firework individual into a candidate functional module partition based on label propagation according to the topological and functional information between protein nodes. Then HFADE-FMD uses the explosion operator of firework algorithm, and mutation, crossover and selection strategies of differential evolution algorithm to iteratively search for better functional module partitions. To verify the performance of HFADE-FMD, this paper compared it with ten competitive methods on four public PPI datasets. The experimental results show that HFADE-FMD achieves prominent performance with respective to Recall, Sn, PPV, and ACC metrics while performing well in terms of Precision and F-measure metrics. Thus, it is able to more accurately detect functional modules and help biologists to find some novel biological insights. [ABSTRACT FROM AUTHOR]

To solve the problems of the waste of resources caused by the small explosion amplitude (even close to 0) of the best firework in the conventional fireworks algorithm (FWA), and the relatively weak local search ability caused by the minimal explosion amplitude check method in the enhanced fireworks algorithm (EFWA), this paper proposes two improved strategies for FWA. First, based on the heuristic information of the distance between the best firework and other fireworks, an adaptive explosion amplitude strategy is proposed to search the local area accurately at the final phase of the FWA. Second, the highly random Lévy flight strategy is adopted instead of the Gaussian sparks strategy to generate mutation sparks in the EFWA to enhance the diversity of local search. Simulation results on 12 standard benchmark functions and their shifted functions indicate that the proposed algorithm improves the optimization precision and obtains better performance in high-dimensional complex optimization problems compared with the EFWA, the hybrid fireworks algorithm with differential evolution operator (FWADE), and the particle swarm optimization algorithm (PSO). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Capturing the complex correlations among the data on the crude oil price time series is challenging, hence accurate prediction of it is difficult. Contrast to multilayer artificial neural network, Pi-Sigma neural network (PSNN) is characterized with stronger approximation ability, fast learning and higher fault tolerance. Fireworks algorithm (FWA) is a new metaheuristic motivated from the occurrence of fireworks explosion, characterized with fast convergence capability, parallelism and finds global optima. This article intends to achieve the synergetic effect of both on hybridizing them. It uses FWA for optimization of weight and bias of PSNN (FWA-PSNN) and overcomes the limitations of gradient based training. It has single hidden layer structure and trainable weight, hence fast and robust. The FWA-PSNN is evaluated on prediction of crude oil price time series. Extensive simulation results, performance analysis, and statistical significance test suggested the suitability of FWA-PSNN.

In recent years, lots of multifactorial optimization evolutionary algorithms have been developed to optimize multiple tasks simultaneously, which improves the overall efficiency using implicit genetic complementarity between different tasks. In this paper, a novel multitask fireworks algorithm is proposed with novel transfer sparks to solve multitask optimization problems. For each task, some transfer sparks would be generated with adaptive length and promising direction vector, which are very helpful to transfer useful genetic information between different tasks. Finally, the proposed algorithm is compared against some chosen state-of-the-art evolutionary multitasking algorithms. The experimental results show that the proposed algorithm provides better performance on several single objectives and multiobjective MTO test suites.

Abstract To give full play to the high efficiency and parallelism of multi-processor systems, the fireworks algorithm (FWA) is improved, and a multi-processor task scheduling algorithm based on improved FWA, named IMFWA, is proposed. IMFWA maps continuous space to discrete space by designing the fireworks location coding method, improves the Gaussian mutation process, and sets adaptive dimensions to accelerate the convergence speed of the algorithm. At the same time, in order to reduce the time complexity of the algorithm and shorten the time finding the optimal task scheduling sequence, the fitness-based tournament selection strategy is used instead of the rule based on Euclidean distance. Finally, IMFWA is compared with the basic fireworks algorithm and the genetic algorithms on the Matlab platform for performance analysis. The results show that the IMFWA has advantages in the convergence speed, and the negative impact of the number of tasks is also lower than the fireworks algorithm and genetic algorithm.

For problems of difficulty in obtaining global optimal solution and low efficiency when heuristic method and intelligent optimization method were used to solve problem of distribution network reconfiguration, and requirements of rapidity and stability of mine-used distribution network reconfiguration, an improved fireworks algorithm was proposed to solve mine-used distribution network reconfiguration. Taking the minimum distribution network loss as objective function, explosion operator and termination strategy are improved by introducing self-adaptive coefficient, so as to reduce iterations and improve search efficiency. Combing with the optimal fireworks fine-tuning strategy, a large number of identical sparks can be avoid during the optimal fireworks explosion, which effectively improves sparks diversity. The experimental results show that the improved fireworks algorithm has higher stability and repeatability, faster search speed and good global search ability, and network loss is obviously reduced by use of the algorithm to solve problem of distribution network reconfiguration.

Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of kernel principal component analysis (KPCA), kernel extreme learning machine (KELM) and fireworks algorithm (FWA) is proposed. First, KPCA modal is used to reduce the dimension of the feature, thus redundant input samples are merged. Next, FWA is employed to optimize the parameters C and σ of KELM. Lastly, the load forecasting modal of KPCA-FWA-KELM is established. The relevant data of a distributed energy system in Beijing, China, is selected for training test to verify the effectiveness of the proposed method. The results show that the new hybrid KPCA-FWA-KELM method has superior performance, robustness and versatility in load prediction of distributed energy systems.

The fireworks algorithm, which is inspired from the phenomenon of fireworks explosion, is a special kind of swarm intelligence algorithm proposed in 2010. Since then, it has been attracting more and more research interest and has been widely employed in many real-world problems due to its unique search manner and high efficiency. In this article, we present a comprehensive review of its advances and applications. We begin with an introduction to the original fireworks algorithm. Then we review its algorithmic research work for single objective and multi-objective optimization problems. After that, we present the theoretical analyses of the fireworks algorithm. Finally, we give a brief overview of its applications and implementations. Hopefully, this article could provide a useful road map for researchers and practitioners who are interested in this algorithm and inspire new ideas for its further development. [ABSTRACT FROM AUTHOR]

Aiming at the problem of slow speed on convergence and poor separation perfomance for the method of traditional independent component analysis (ICA). The strategy on chaotic maps strategy are combined with an adaptive explosion radius, and a kind of chaotic adaptive fireworks algorithm (CAFWA) based blind source separation (BSS) method is proposed, and applied into the sorting problem of radar emitter mixed signal. The chaotic maps strategy can distribute the initial value more evenly in the solution space, and the explosion radius can be changed adaptively according to the advantages and disadvantages of fitness, which ensures the precision of local search for the proposed algorithm and satisfies the diversity of universal search. The experimental results show that the proposed algorithm can sort observation signals well in both noise-free and noise conditions, and has faster constriction speed and better sorting function than the traditional algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Exchange rates are highly fluctuating by nature; thus, they are difficult to forecast. Artificial neural networks (ANNs) have proven to be better than statistical methods. Inadequate training data may lead the model to reach sub-optimal solutions, resulting in poor accuracy (as ANN-based forecasts are data-driven). To enhance forecasting accuracy, we suggests a method of enriching training datasets through exploring and incorporating virtual data points (VDPs) by an evolutionary method called the fireworks algorithm-trained functional link artificial neural network (FWA-FLN). The model maintains a correlation between current and past data, especially at the oscillation point on the time series. The exploration of a VDP and forecast of the succeeding term go consecutively by 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 similarly trained models and produces far better prediction accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the characteristics of nonlinear and multi-phase in the batch process, a self-adaptive multi-phase batch process fault diagnosis method is proposed in this paper. Firstly, kernel entropy component analysis (KECA) method is used to achieve multi-phase partition adaptively, which makes the process data mapped into the high-dimensional feature space and then constructs the core entropy and the angular structure similarity. Then a multi-phase KECA failure monitoring model is developed by using the angular structure similarity as the statistic, which is based on the partitioned phases and the effective failure features by the KECA feature extraction method. A multi-phase batch process fault diagnosis method, which applies the multi-class support vector machines (MSVM) and fireworks algorithm (FWA), is proposed to recognize each sub-phase fault diagnosis automatically. The effectiveness and advantages of the proposed multi-phase fault diagnosis method are illustrated with a case study on a fed-batch penicillin fermentation process. [ABSTRACT FROM AUTHOR]

Capturing the complex correlations among the data on the crude oil price time series is challenging, hence accurate prediction of it is difficult. Contrast to multilayer artificial neural network, Pi-Sigma neural network (PSNN) is characterized with stronger approximation ability, fast learning and higher fault tolerance. Fireworks algorithm (FWA) is a new metaheuristic motivated from the occurrence of fireworks explosion, characterized with fast convergence capability, parallelism and finds global optima. This article intends to achieve the synergetic effect of both on hybridizing them. It uses FWA for optimization of weight and bias of PSNN (FWA-PSNN) and overcomes the limitations of gradient based training. It has single hidden layer structure and trainable weight, hence fast and robust. The FWA-PSNN is evaluated on prediction of crude oil price time series. Extensive simulation results, performance analysis, and statistical significance test suggested the suitability of FWA-PSNN. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The relay reliability has an impact on the reliability of the entire electric vehicle system. This paper contributes to propose the improving fireworks algorithm optimizing the grey neural network model to predict the relay lifetime. This paper shows how the mutation operation and mapping operation in the fireworks algorithm are used to improve the convergence ability and running speed; the convergence performance and running speed of improved fireworks algorithm are tested with standard test function and compared with fireworks algorithm; and the grey neural network model–improved fireworks algorithm is used to predict the relay life and compared with grey model, grey neural network, and grey neural network model–fireworks algorithm. The results show that the convergence accuracy of the improved fireworks algorithm is better than the fireworks algorithm. The running time of improved fireworks algorithm is the shortest; the improved fireworks algorithm–grey neural network model has the best prediction effect and the root mean square error value is 6.75% smaller than the fireworks algorithm–grey neural network model. [ABSTRACT FROM AUTHOR]

Unnatural control chart patterns (CCPs) can be associated with the quality problems of the production process. It is quite critical to detect and identify these patterns effectively based on process data. Various machine learning techniques to CCPs recognition have been studied on the process only suffer from basic CCPs of unnatural patterns. Practical production process data may be the combination of two or more basic patterns simultaneously in reality. This paper proposes a mixture CCPs recognition method based on fusion feature reduction (FFR) and fireworks algorithm-optimized multiclass support vector machine (MSVM). FFR algorithm consists of three main sub-networks: statistical and shape features, features fusion and kernel principal component analysis feature dimensionality reduction, which make the features more effective. In MSVM classifier algorithm, the kernel function parameters play a very significant role in mixture CCPs recognition accuracy. Therefore, fireworks algorithm is proposed to select the two-dimensional parameters of the classifier. The results of the proposed algorithm are benchmarked with popular genetic algorithm and particle swarm optimization methods. Simulation results demonstrate that the proposed method can gain the higher recognition accuracy and significantly reduce the running time. [ABSTRACT FROM AUTHOR]

In recent years, lots of multifactorial optimization evolutionary algorithms have been developed to optimize multiple tasks simultaneously, which improves the overall efficiency using implicit genetic complementarity between different tasks. In this paper, a novel multitask fireworks algorithm is proposed with novel transfer sparks to solve multitask optimization problems. For each task, some transfer sparks would be generated with adaptive length and promising direction vector, which are very helpful to transfer useful genetic information between different tasks. Finally, the proposed algorithm is compared against some chosen state-of-the-art evolutionary multitasking algorithms. The experimental results show that the proposed algorithm provides better performance on several single objectives and multiobjective MTO test suites. [ABSTRACT FROM AUTHOR]

As a new variant of swarm intelligence algorithm, fireworks algorithm (FWA) exhibits promising performance on a wide set of optimization problems, for which the fireworks algorithm has been concentrated on and investigated by researchers recently. This paper aims to improve the performance of the FWA by exploiting updating information of the core firework to guide the algorithm's searching process. Based on this mentality, this paper ameliorated the explosion strategy of core firework of dynamic fireworks algorithm (dynFWA). The proposed algorithm, named dynPgFWA in this paper, improved FWA from two aspects: amplifying the explosion amplitude on the direction on which core firework is updated, and making more sparks which are generated by core firework distributed on this direction to enhance the algorithm's searching ability on updating direction. A numerical experiment on CEC2015 and CEC2017 test suite was implemented to verify the performance of the proposed algorithm. Results of the experiment indicated that dynPgFWA outperformed the compared evolutionary algorithms in the quality of solutions. [ABSTRACT FROM AUTHOR]

Fireworks algorithm (FWA) is a swarm intelligent algorithm inspired by fireworks explosion. Based on the analysis of the shortcoming of Gaussian mutant operator, a fireworks algorithm based on differential mutant operator (DEFWA) is proposed, and a dynamic spark explosion strategy is adopted for optimal fireworks. Experiments on the benchmark function set show that the proposed DEFWA based algorithm outperforms several improved fireworks algorithms in the solution accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To promote energy saving, high efficiency and quality production in mechanical manufacturing industry, a large number of studies have been conducted on parameters optimisation, especially for turning. However, most of the approaches have focused on single objective model and unidirectional turning (UDT). This paper presents a multi-objective parameter optimisation method for energy consumption, cutting time and surface roughness using new process of forward-and-reverse multidirectional turning (MDT). Firstly, the composition characteristics of cutting parameters for energy consumption, cutting time and surface roughness of MDT are analyzed to establish the corresponding models through nonlinear regression fitting, respectively. Then taking the minimum energy consumption, cutting time and surface roughness as the optimisation objectives, the improved fireworks algorithm can generate nearly 100 groups of optimal solutions. The most suitable processing parameters solution needs to be selected according to different machining requirements. In application scenarios, most relative errors of the models are within 10%. Comparing the machining performance of MDT and UDT under the same cutting parameters, the processing efficiency is increased by 25.97% and energy consumption is reduced by 12.38%, using the MDT. This study provides a multi-objective optimisation approach and models for MDT to reduce energy consumption, improve production efficiency and quality. [Display omitted] • Proposing a multi-objective optimisation method for multidirectional turning. • Establishing multi-objective optimisation models. • Addressing solutions based on improved fireworks algorithm considering energy, efficiency and quality. • Analyzing the practicability of the optimisation models and method in the case. [ABSTRACT FROM AUTHOR]

The financial industry has been becoming more and more dependent on advanced computing technologies in order to maintain competitiveness in a global economy. Hence, the stock price prediction problem using data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. Fuzzy logic (FL) and Artificial Neural Network (ANN) present an exciting and promising technique with a wide scope for the applications of prediction. There is a growing interest in both fields of fuzzy logic computing and the financial world in the use of fuzzy logic to predict future changes in prices of stocks, exchange rates, commodities, and other financial time series. Fuzzy logic provides a way to draw definite conclusions from vague, ambiguous or imprecise information. Artificial Neural Network is one of data mining techniques being widely accepted in the business area due to its ability to learn and detect relationships among nonlinear variables. The ANN outperforms statistical regression models and also allows deeper analysis of large data sets, especially those that have the tendency to fluctuate within a short of time period. In this paper, we investigate the ability of Fuzzy logic and multilayer perceptron (MLP), which is a kind of the ANN, to tackle the financial time series stock forecasting problem. The proposed approaches were tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the comparison between those techniques is performed to examine their effectiveness.

Modern real world science and engineering problems can be classified as multi-objective optimisation problems which demand for expedient and efficient stochastic algorithms to respond to the optimization needs. This paper presents an object-oriented software application that implements a firework optimization algorithm for function optimization problems. The algorithm, a kind of parallel diffuse optimization algorithm is based on the explosive phenomenon of fireworks. The algorithm presented promising results when compared to other population or iterative based meta-heuristic algorithm after it was experimented on five standard benchmark problems. The software application was implemented in Java with interactive interface which allow for easy modification and extended experimentation. Additionally, this paper validates the effect of runtime on the algorithm performance.

How to choose a chaotic sequence to construct a chaotic S-box is a key factor to determine the performance of the S-box. In this paper, a construction method for chaotic S-box based on fireworks algorithm is proposed, in which the fireworks algorithm is used to search for the optimal solution in the solution space of the Lorenz chaotic system. The performances of S-box are tested through the nonlinearity, differential uniformity, strict avalanche criterion, output bits independence criterion and bijectivity, and compared with the existing classical S-boxes based on the Lorenz chaotic system. The results indicate that the S-box in this paper has more excellent cryptographic characteristics, which will provide a new idea for the efficient generation of chaotic S-box. [ABSTRACT FROM AUTHOR]