Improved Fireworks Algorithm Research Articles

Three-Phase Symmetric Distribution Network Fast Dynamic Reconfiguration Based on Timing-Constrained Hierarchical Clustering Algorithm

This paper develops a novel dynamic three-phase symmetric distribution network reconfiguration (DNR) approach based on hierarchical clustering with timing constraints, which can divide the time period according to the time-varying symmetric load demand and symmetric distributed generations (DGs) output condition for a given time interval. The significance of the proposed technique is that by approximating the cluster center as the load status and DGs output status of the corresponding period, in this way, the intractable dynamic reconfiguration problem can be recast as multiple single-stage static three-phase symmetric DNR problems, which can effectively reduce the complexity of the three-phase symmetric dynamic reconfiguration. Furthermore, an improved fireworks algorithm considering heuristic rules (H-IFWA) is proposed and investigated to efficiently manage each single-stage static three-phase symmetric DNR problem. In order to avoid trapping into a local optimum or to facilitate the computational performance, the power moment method and the coding method based on heuristic rules are employed to reduce the solution space. The effectiveness of the proposed H-IFWA is validated on the IEEE 33, 119-bus system and a practical-scale Taiwan power company (TPC) 84-bus test system with DGs.

Hybrid FWPS cooperation algorithm based unmanned aerial vehicle constrained path planning

This paper considers the unmanned aerial vehicle (UAV) global path planning as an optimization problem with multiple constraints and proposes an improved fireworks algorithm (FWA) and particle swarm optimization (PSO) cooperation algorithm to generate an optimal path. The objective function of the UAV flight path is modeled to have the shortest length satisfying strict multiple threat area constraints. The α constrained method using the level comparison strategy is integrated into both FWA and PSO to enhance their superior constraint-handling ability. To increase the population diversity, the whole population is divided to fireworks and particles, which perform search operation in parallel. A new mutation strategy in the fireworks is adopted to avoid falling into the local optimum. Information sharing mechanism between fireworks and particles is established to make the population achieve the excellent global optimization performance. Several numerical simulations are carried out and the results show that our proposed algorithm performs well in obtaining high quality solutions and handling constraints.

A time-division distribution strategy for the two-echelon vehicle routing problem with demand blowout

<p style='text-indent:20px;'>Based on the rapid development of e-commerce, major promotional events and holidays can lead to explosive growth in market demand and place significant pressure on distribution systems. In this study, we considered a distribution system in which products are first transported to transfer satellites from a central depot and then delivered to customers from the transfer satellites. We modeled this distribution problem as a two-echelon vehicle routing problem with demand blowout (2E-VRPDB). We adopt a time-division distribution strategy to address massive delivery demand in two phases by offering incentives to customers who accept flexible delivery dates. We propose a hybrid fireworks algorithm (HFWA) to solve the 2E-VRPDB model. This model fuses an optimal cutting algorithm with an improved fireworks algorithm. To demonstrate the effectiveness and efficiency of the proposed HFWA, we conducted comparative analysis on a genetic algorithm and ant colony algorithm using a VRP example set. Finally, we applied the proposed model and HFWA to solve a distribution problem for the Jingdong Mall in Chengdu, China. The computational results demonstrate that the proposed approach can effectively reduce logistical costs and maintain a high service level.</p>

Development of a new inversion method for detecting spatiotemporal characteristics of coal mines based on earth observation technology

We propose a novel methodology for detecting the spatiotemporal characteristics of coal mines based on earth observation technology. Based on the dynamic probability integral model, the spatiotemporal relationship model between underground mining and ground surface response is constructed by fitting dynamic probability integral parameters. Considering the highly non-linearity of the constructed model, an improved fireworks algorithm (IFWA) is introduced to solve the target parameters. Taking the surface above a specific working panel of Guqiao Coal Mine in Huainan, China as the experimental area, based on the short-term surface monitoring data of the observation station, the proposed method was applied to detect the spatiotemporal characteristics of underground mining in six periods. The results show that the detected spatiotemporal characteristics were consistent with the measured ones. However, the large fluctuation of the dynamic probability integral parameters, and subcritical extraction may affect the method. In the simulation experiment, the model error of the construction method for multiple periods obtained was in the range of 0–21.0%, with an average of 5.0%. Furthermore, the sensitivity of the inverted spatiotemporal characteristic parameters to the input probability integral parameters was studied, and the results show that when error percentages of less than 20%, the inversion of target parameters are closer to the simulation (the mean relative errors is approximately 7.7%). Due to the advantages of short-term earth observation, this study provides a practical, convenient method for detecting spatiotemporal characteristics of underground mining, and avoids time-consuming and laborious long-term monitoring.

An Optimal Over-frequency Generator Tripping Strategy for Regional Power Grid with High Penetration Level of Renewable Energy

This paper proposes an optimal over-frequency generator tripping strategy aiming at implementing the least amount of generator tripping for the regional power grid with high penetration level of wind/photovoltaic (PV), to handle the over-frequency problem in the sending-end power grid under large disturbances. A steady-state frequency abnormal index is defined to measure the degrees of generator over-tripping and under-tripping, and a transient frequency abnormal index is presented to assess the system abnormal frequency effect during the transient process, which reflects the frequency security margin during the generator tripping process. The scenario-based analysis method combined with the non-parametric kernel density estimation method is applied to model the uncertainty of the outgoing power caused by the stochastic fluctuations of wind/PV power and loads. Furthermore, an improved fireworks algorithm is utilized for the solution of the proposed optimization model. Finally, the simulations are performed on a real-sized regional power grid in Southern China to verify the effectiveness and adaptability of the proposed model and method.

Reserve constrained dynamic economic dispatch in multi-area power systems: An improved fireworks algorithm

The multi-area economic dispatch (MAED) plays a major role in an interconnected power system to determine the generation dispatch strategy and satisfy the load demands as well as the technical constraints. Tie-line capacity between different areas, upper/lower generation limits, generator ramp rates, transmission losses, valve point effect, disjoint prohibited zones as well as spinning reserve are considered as the MAED problem constraints. Due to hourly load variations, the MAED problem should be solved dynamically to coincide the realistic conditions which build up the multi-area dynamic economic dispatch (MADED) problem. Considering these constraints besides the intertemporal constraints which depends to the optimal conditions of each hour with respect to other time steps may increase the problem complexity. In this regard, an improved version of fireworks algorithm which is equipped with two effective cross-generation mutation strategies is deployed to handle the non-linear, non-smooth, non-convex, multi-dimensional and multi-constraints MADED problem. Furthermore, a new constraint handling scheme is developed to correct the potential solutions in feasible search space. Finally, two small and large systems are used to investigate the capability of the developed algorithm in finding the global or near-global optima.

On the seismic collapse capacity of optimally designed steel braced frames

The main purpose of this study is to respond to an important question in the field of structural engineering: is the seismic collapse capacity of optimally designed concentrically steel braced frames acceptable or not? The present work includes two phases: performance-based design optimization and seismic collapse safety assessment. In the first phase, three nature-inspired metaheuristic algorithms, namely improved fireworks algorithm, center of mass optimization, and enhanced colliding-bodies optimization are employed to carry out the optimization task. In the second phase, seismic collapse capacity of the optimally designed concentrically steel braced frames is evaluated by performing incremental dynamic analysis and generating fragility curves. Two design examples of 5- and 10-story concentrically steel braced frames with two different topologies of braces are presented. The numerical results indicate that the center of mass optimization algorithm outperforms the other algorithms. However, all of the optimal designs found by all algorithms are of acceptable seismic collapse safety.

Estimation of parameters of probability integral method model based on improved fireworks algorithm

How to accurately estimate the probability integral parameters based on the measured data has always been a difficult point in the application of the probability integral method. This paper introduced FWA into the solution of the probability integral method for the first time. Based on the analysis of strengths and weaknesses of FWA, IFWA (improved fireworks algorithm) was proposed. Then, the IFWA-based inversion model for probability integral parameters (MIFWA) was constructed by the fusion the mining subsidence law and probability integral method. Experiments show that MIFWA has better performance in anti-errors of the observation and the geological mining generalization.

Electric Vehicle Relay Lifetime Prediction Model Using the Improving Fireworks Algorithm–Grey Neural Network Model

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.

An improved fireworks algorithm for the constrained single-row facility layout problem

The single-row facility layout problem (SRFLP) decides upon the arrangement of facilities in a straight row so as to minimise the material handling cost. Generally, this problem allows the placement of the facilities in any location in any order without any restriction, but some practical SRFLP cases are required to meet various types of constraints imposed on the placement of its facilities in the engineering of the placement of factory machinery and equipment. According to this new perspective, we studied the constrained SRFLP (cSRFLP) in this paper. In this problem, a few facilities need to be placed in certain positions or specified orders with or without the requirement to be located next to each other. To address this novel layout problem, related constraints are defined and classified, and novel mixed-integer programming models for the problem are formulated. Subsequently, the CPLEX software is employed to solve this problem. Then a constrained improved fireworks algorithm is presented, which is discretised to solve the considered cSRFLP. Our computational experiments demonstrate that the proposed algorithm performs better than the existing algorithms in solving the problem.

Hybrid Flow Shop Scheduling Problems Using Improved Fireworks Algorithm for Permutation

Prior studies are lacking which address permutation flow shop scheduling problems and hybrid flow shop scheduling problems together to help firms find the optimized scheduling strategy. The permutation flow shop scheduling problem and hybrid flow shop scheduling problems are important production scheduling types, which widely exist in industrial production fields. This study aimed to acquire the best scheduling strategy for making production plans. An improved fireworks algorithm is proposed to minimize the makespan in the proposed strategies. The proposed improved fireworks algorithm is compared with the fireworks algorithm, and the improvement strategies include the following: (1) A nonlinear radius is introduced and the minimum explosion amplitude is checked to avoid the waste of optimal fireworks; (2) The original Gaussian mutation operator is replaced by a hybrid operator that combines Cauchy and Gaussian mutation to improve the search ability; and (3) An elite group selection strategy is adopted to reduce the computing costs. Two instances from the permutation flow shop scheduling problem and hybrid flow shop scheduling problems were used to evaluate the improved fireworks algorithm’s performance, and the computational results demonstrate the improved fireworks algorithm’s superiority.

Cost-driven scheduling of service processes in hybrid cloud with VM deployment and interval-based charging

Cost-driven scheduling of service processes mainly focuses on task allocation to achieve the cost minimization from a user’s perspective. As an important challenge, cost-driven scheduling of service processes in hybrid cloud has been widely studied. However, existing researches seldom precisely formulate the virtual machine resources and their capacity constraints in hybrid cloud, which may reduce the elasticity or lead to unfeasible solutions. Besides, they do not comprehensively consider the interval-based charging, which cannot precisely calculate and reduce the cost. In this paper, we propose a more practical model for service processes scheduling in hybrid cloud, in which resource constraints are defined more precisely without reducing the elasticity by virtual machine deployment, and a comprehensive interval-based charging including billing cycle and sustained-use discount is considered. This problem is solved by an improved fireworks algorithm based approach, in which specific representation, decoding and fireworks updating strategies are proposed for the mechanisms of virtual machine deployment and interval-based charging. Experiments are conducted using service processes with different problem sizes, and results show that our approach can find scheduling solutions with lower costs than existing algorithms.

A Novel Fireworks Algorithm for the Protein-Ligand Docking on the AutoDock

The AutoDock is a widely used protein-ligand docking simulation platform which is a simulator to bring the field of computer-aided drug design (CADD) conveniences. The protein-ligand docking problem is of great significance to design more effective and ideal drugs. In order to solve the protein-ligand docking problem more effectively, we propose an improved fireworks algorithm called FWADOCK. FWADOCK utilizes the position updating information of fireworks with the best fitness value to determine the shape of the fireworks and the distribution of sparks. During the process of searching for the optimal solution, it is also considered that the diversity of understanding and how to avoid premature convergence of the algorithm. The proposed algorithm is tested on some benchmark test cases of protein-ligand docking problems and compared with some other related algorithms from different perspectives on the AutoDock platform. The results show that FWADOCK has a competitive performance in solving the protein-ligand docking problem.

Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network

Abstract To determine whether China can achieve the commitment of reducing carbon emission intensity in 2030, a general regression neural network (GRNN) forecasting model based on improved fireworks algorithm (IFWA) optimization is constructed to forecast total carbon emissions (TCE) and carbon emissions intensity (CEI) in 2016–2040. Random forests (RF) method is used to select the important carbon emissions influencing factors to reduce data redundancy. The superiority of IFWA-GRNN forecasting model is verified by historical data from 1990 to 2015. The basic as usual (BAU), policy tightening (PT) and market allocation (ML) scenarios are set to forecast the TCE and CEI. The results show that under the BAU scenario, China’s CEI reduction commitments in 2020 (40%–45%) can be achieved, but the commitment in 2030 (60%–65%) cannot be achieved. Under the PT and ML scenarios, China can achieve its CEI commitments in 2030, and the TCE will decrease gradually after reaching its peak in 2030. Under the existing macro development planning and policy intensity in China, there are still certain pressures to achieve CEI reduction targets. It is necessary to implement policy adjustment and market mechanism incentives for both energy supply and consumption, optimize power supply structure, promote electric energy substitution, and accelerate the construction of a unified national electricity market, carbon market, etc.

A Spectrum Allocation Algorithm Based on Non-cooperative Game

Aiming at the problem of spectrum allocation among cognitive users, this paper proposes a spectrum allocation algorithm under non-cooperative game conditions, and presents a non-cooperative game Nash equilibrium algorithm, which can be iterated faster. Under the model and system settings, the improved fireworks algorithm is used to solve the spectrum allocation problem among cognitive users, and the utility function of cognitive users is improved to ensure the fairness of spectrum allocation.

Community Detection Using Fireworks Optimization Algorithm

Since community detection is an important tool for understanding the complex structure of social networks, an improved fireworks algorithm is proposed in this paper. The algorithm generates the initial population with the Affinity Propagation approach to have high initialization quality. The algorithm optimizes the modularity density as objective function by calculating the amplitude, the number of sparks and exploring the sparks. One firework is mutated twice, randomly and according to the label of its neighbors. Experiments on both real and synthetic networks show that the proposed algorithm achieves more accurate results in terms of modularity and normalized mutual information.

Improved fireworks algorithm with information exchange for function optimization

The fireworks algorithm, which is inspired by the explosion of fireworks, is a new swarm-based meta-heuristic algorithm for global optimization. This work proposes an improved fireworks optimization algorithm (IFWA) based on the enhanced fireworks algorithm (EFWA). Three aspects of improvement are presented after an analysis of the drawbacks of EFWA. These improvements are a new explosion scheme, GS-Gaussian explosion operator, and deep information exchange strategy. The proposed IFWA is tested on 23 benchmark function optimization problems and a real engineering problem, namely, optimal controller design for automotive active suspension. Optimization results prove that IFWA has competitive advantage compared with EFWA and other popular meta-heuristic algorithms and demonstrates the potential to solve real problems effectively.

An improved fireworks algorithm for the capacitated vehicle routing problem

The capacitated vehicle routing problem (CVRP), which aims at minimizing travel costs, is a well-known NP-hard combinatorial optimization. Owing to its hardness, many heuristic search algorithms have been proposed to tackle this problem. This paper explores a recently proposed heuristic algorithm named the fireworks algorithm (FWA), which is a swarm intelligence algorithm. We adopt FWA for the combinatorial CVRP problem with several modifications of the original FWA: it employs a new method to generate “sparks” according to the selection rule, and it uses a new method to determine the explosion amplitude for each firework. The proposed algorithm is compared with several heuristic search methods on some classical benchmark CVRP instances. The experimental results show a promising performance of the proposed method. We also discuss the strengths and weaknesses of our algorithm in contrast to traditional algorithms.

Integration of renewable energy sources in dynamic economic load dispatch problem using an improved fireworks algorithm

In electrical power system, economic load dispatch is a generic operation for optimal sharing of generation units to meet the system load. With the rapid development of the renewable infrastructure and wide encouragement for green energy have emerged hybrid generating systems in power systems. However, there continuous ever-increasing production is creating challenges as well as implicating economic factor also in operation. A collective cost function is considered with the conventional thermal generators along with the consideration of renewable energy sources to envisage the economic factor. For these renewable sources, like wind and solar, the proportional cost, their uncertainty and variability by overestimation and underestimation cost are considered. To achieve this economic day-ahead scheduling, dynamic operation in time scale of 1 h interval is performed. The stochastic nature of wind and solar is modelled by Weibull and Beta distributions, respectively. Moreover, economic optimisation is obtained by a newly developed algorithm called improved fireworks algorithm with non-uniform operator (IFWA-NMO). This introduces adaptive dimension strategy, limiting mapping operator and non-uniform operator. The effectiveness of proposed IFWA-NMO is investigated on standard dynamic economic load dispatch (DELD) system and also employed to solve conventional DELD with wind-solar system.

An improved fireworks algorithm for discrete sizing optimization of steel skeletal structures

ABSTRACTThe fireworks algorithm (FWA), a newly introduced metaheuristic algorithm, has exhibited remarkable performance in a variety of optimization problems. However, FWA still has some shortcomings that need to be addressed. In the present study, an improved fireworks algorithm (IFWA) is proposed to mitigate the drawbacks of the original algorithm. In the IFWA, the possibility of interaction among different solutions during the optimization process is provided. Moreover, a strategy is considered to decrease the computational effort of the algorithm. The IFWA is used to deal with the discrete structural optimization problems of steel trusses and frames. The results demonstrate the efficiency of the proposed IFWA compared with other well-known metaheuristics in the literature, in terms of the optimum solutions and the convergence rate.