scholarly journals Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
Thang Trung Nguyen ◽  
Nguyen Vu Quynh ◽  
Le Van Dai
Keyword(s):  
Objective Function ◽  
Firefly Algorithm ◽  
Optimal Solution ◽  
Optimal Operation ◽  
Random Numbers ◽  
Fuel Cost ◽  
Step Size ◽  
Spinning Reserve ◽  
Solution Quality ◽  
Improved Firefly Algorithm

This paper presents a novel improved firefly algorithm (IFA) to deal the problem of the optimal operation of thermal generating units (OOTGU) with the purpose of reducing the total electricity generation fuel cost. The proposed IFA is developed based on combining three improvements. The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). The effect of each proposed improvement on IFA is investigated by executing five benchmark functions and two different systems. The performance of IFA is investigated on six other study cases consisting of different types of objective function and complex level of constraints. The objective function considers single fuel with quadratic form and nonconvex form, and multifuels with the sum of several quadratic and nonconvex functions while a set of constraints taken into account are power loss, prohibited zone, ramp rate limit, spinning reserve, and all constraints in transmission power networks. The obtained results indicate the proposed improvements in terms of high optimal solution quality, stabilization of search ability, and fast convergence compared with FA. In addition, the comparisons with other methods also lead to a conclusion that the proposed method is a very promising optimization tool for systems with quadratic fuel cost function and with complicated constraints.

scholarly journals An Energy Optimal Dispatching Model of an Integrated Energy System Based on Uncertain Bilevel Programming

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 477 ◽  
Author(s):  
Xueying Song ◽  
Hongyu Lin ◽  
Gejirifu De ◽  
Hanfang Li ◽  
Xiaoxu Fu ◽  
...  
Keyword(s):  
Objective Function ◽  
Decision Maker ◽  
Bilevel Programming ◽  
Firefly Algorithm ◽  
Operating Cost ◽  
Energy System ◽  
Optimal Operation ◽  
Decision Makers ◽  
Improved Firefly Algorithm ◽  
Integrated Energy System

An integrated energy system (IES) involving a large number of decision-makers causes problems of bad coordination between energy sub-networks and the IES and it is not able to fully consider the multi-energy complementarity among multiple decision-makers. In this context, firstly, this paper constructs an energy optimal dispatching model of an IES based on uncertain bilevel programming. The upper model takes the transformation matrix of energy hubs as the upper decision-maker, taking the minimum operation cost of the IES in the form of confidence as the objective function; the lower model takes each optimal operation plan of the electric power sub-network, the thermal energy sub-network, and the gas energy sub-network as the lower decision-makers, aiming at the operation economy of each sub-network and considering their operation as necessary constraints. Secondly, a firefly algorithm with chaotic search and an improved light intensity coefficient is designed to improve the proposed model. An empirical analysis was conducted on a pilot area of an integrated energy system in Hebei Province. The results show the following: (1) The typical daily operating cost of the integrated energy system in winter is lower than that in summer; (2) under the same load level, the typical winter and summer running costs of the integrated energy system are lower than that of the traditional microgrid; (3) compared with the particle swarm optimization algorithm, the improved firefly algorithm proposed in the paper has obvious advantages both in terms of running cost and solution time; and (4) when the confidence of the objective function and the constraints increases, the operating cost of various schemes also increase. The above results validate the effectiveness of the energy optimal dispatching model of the IES and the economy of the system operation under the multiple decision-maker hierarchy.

A multi-objective optimization operation strategy for ice-storage air-conditioning system based on improved firefly algorithm

2021 ◽  
pp. 014362442110455
Author(s):  
Xinwei Zhou ◽  
Junqi Yu ◽  
Wanhu Zhang ◽  
Anjun Zhao ◽  
Min Zhou
Keyword(s):  
Energy Saving ◽  
Economic Benefit ◽  
Air Conditioning ◽  
Firefly Algorithm ◽  
Optimal Operation ◽  
Ice Storage ◽  
Operation Strategy ◽  
Multi Objective Optimization ◽  
Air Conditioning System ◽  
Improved Firefly Algorithm

Reasonable distribution of cooling load between chiller and ice tank is the key to realize the economical and energy-saving operation of ice-storage air-conditioning (ISAC) system. A multi-objective optimization model based on improved firefly algorithm (IFA) was established in this study to fully exploit the energy-saving potential and economic benefit of the ISAC system. The proposed model took the partial load rate of each chiller and the cooling ratio of the ice tank as optimization variables, and the lowest energy consumption loss rate and the lowest operating cost of the ISAC system were calculated. Chaotic logic self-mapping was used to initialize population to avoid falling into local optimum, and Cauchy mutation was used to increase the population’s diversity to improve the algorithm’s global search ability. The experimental results show that compared with the operation strategy based on constant proportion, particle swarm optimization (PSO) algorithm, and firefly algorithm (FA), the optimal operation strategy based on IFA can achieve more significant energy-saving and economic benefits. Meanwhile, the convergence accuracy and stability of the algorithm are significantly improved. Practical application: The optimized operation strategy of the ice-storage air-conditioning system can reduce energy loss and operating costs. The traditional operation strategies have the problems of low optimization precision and poor optimization effect. Therefore, this study presents an optimal operation strategy based on IFA. The convergence accuracy and stability of the algorithm are increased after the algorithm is improved. The operation strategy can get the maximum energy-saving effect and economic benefit of the ISAC system.

scholarly journals Joint Channel Assignment and Bandwidth Reservation using Improved FireFly Algorithm (IFA) in WMN

2021 ◽  
Author(s):  
narayanarao narayanarao ◽  
A. Rajasekhar Reddy
Keyword(s):  
Objective Function ◽  
Channel Assignment ◽  
Firefly Algorithm ◽  
Path Selection ◽  
Bandwidth Reservation ◽  
Probability Metrics ◽  
Channel Efficiency ◽  
Improved Firefly Algorithm ◽  
Flow Conservation ◽  
Joint Channel

Abstract In WMN, at the time of network consignment and bandwidth registration, the active network consignment method did not take into consideration the intrusion, congestion load and bandwidth necessities as a whole. The significance centred bandwidth registration methods result in famishment of slightest significance congestion. Hence in this paper, we propose a Joint Channel Assignment and Bandwidth Reservation using Improved FireFly Algorithm (IFA) in WMN. Initially the priority of each node is determined based on the channel usage, future interference and link congestion probability metrics. The bandwidth is allocated straight, comparative to the nodule significance and entire quantity of congestion movements incomplete on the demanded nodule. For channel assignment and path selection, the improved FireFly Algorithm (IFA) is used. The objective function of IFA is determined in terms of link capacity, interference and flow conservation constraints. Then the channels and the path which minimize the objective function are selected by applying IFA. By simulation results we show that the proposed technique minimizes the traffic and enhances the channel efficiency.

scholarly journals Improved Solutions for the Optimal Coordination of DOCRs Using Firefly Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Muhammad Sulaiman ◽  
Waseem ◽  
Shakoor Muhammad ◽  
Asfandyar Khan
Keyword(s):  
Objective Function ◽  
Firefly Algorithm ◽  
Operating Time ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Nonlinear Constrained Optimization ◽  
Design Variables ◽  
Highly Nonlinear ◽  
Overcurrent Relays ◽  
Optimal Coordination

Nature-inspired optimization techniques are useful tools in electrical engineering problems to minimize or maximize an objective function. In this paper, we use the firefly algorithm to improve the optimal solution for the problem of directional overcurrent relays (DOCRs). It is a complex and highly nonlinear constrained optimization problem. In this problem, we have two types of design variables, which are variables for plug settings (PSs) and the time dial settings (TDSs) for each relay in the circuit. The objective function is to minimize the total operating time of all the basic relays to avoid unnecessary delays. We have considered four models in this paper which are IEEE (3-bus, 4-bus, 6-bus, and 8-bus) models. From the numerical results, it is obvious that the firefly algorithm with certain parameter settings performs better than the other state-of-the-art algorithms.

scholarly journals IFA-EO: An Improved Firefly Algorithm Hybridized With Extremal Optimization for Continuous Unconstrained Optimization Problems

2021 ◽  
Author(s):  
Min-Rong Chen ◽  
Liu-Qing Yang ◽  
Guo-Qiang Zeng ◽  
Kang-Di Lu ◽  
Yi-Yuan Huang
Keyword(s):  
Firefly Algorithm ◽  
Optimization Problems ◽  
Local Optimum ◽  
Step Size ◽  
Extremal Optimization ◽  
Unconstrained Optimization Problems ◽  
Improved Firefly Algorithm ◽  
Attraction Model ◽  
Complex Optimization ◽  
Adaptive Step

Abstract As one of the evolutionary algorithms, firefly algorithm (FA) has been widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and is easily trapped into local optimum. To tackle these defects, this paper proposes an improved FA combined with extremal optimization (EO), named IFA-EO, where three strategies are incorporated. First, to balance the tradeoff between exploration ability and exploitation ability, we adopt a new attraction model for FA operation, which combines the full attraction model and the single attraction model through the probability choice strategy. In the single attraction model, small probability accepts the worse solution to improve the diversity of the offspring. Second, the adaptive step size is proposed based on the number of iterations to dynamically adjust the attention to the exploration model or exploitation model. Third, we combine an EO algorithm with powerful ability in local-search into FA. Experiments are tested on two group popular benchmarks including complex unimodal and multimodal functions. Our experimental results demonstrate that the proposed IFA-EO algorithm can deal with various complex optimization problems and has similar or better performance than the other eight FA variants, three EO-based algorithms, and one advanced differential evolution variant in terms of accuracy and statistical results.

Task Scheduling in Cloud Computing with Improved Firefly Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 3189-3193 ◽  
Author(s):  
Li Feng Zhao ◽  
Sheng Han Zhou ◽  
Wen Bing Chang
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Experimental Analysis ◽  
Firefly Algorithm ◽  
Objective Function Value ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Computing Model ◽  
Improved Firefly Algorithm ◽  
Global Optimal

The paper improved a task scheduling in cloud computing based on intelligence firefly algorithm. Firstly, the study finds the better solution of cloud computing task scheduling with intelligence firefly algorithm. Then the better solution was turned into the improved firefly algorithm to find the global optimal solution through improved firefly information communications. The Experimental Analysis result suggest the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution time of objective function value is shorten.

scholarly journals Minimizing Electricity Fuel Cost of Thermal Generating Units by Using Improved Firefly Algorithm

2019 ◽  
Vol 51 (1) ◽  
pp. 133
Author(s):  
Thang Trung Nguyen ◽  
Bao Quoc Nguyen ◽  
Phuong Duy Nguyen ◽  
Chiem Trong Hien
Keyword(s):  
Firefly Algorithm ◽  
Fuel Cost ◽  
Improved Firefly Algorithm

scholarly journals An improved firefly algorithm with dynamic self-adaptive adjustment

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0255951
Author(s):  
Yu Li ◽  
Yiran Zhao ◽  
Yue Shang ◽  
Jingsen Liu
Keyword(s):  
High Performance ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Flower Pollination Algorithm ◽  
Step Size ◽  
Improved Firefly Algorithm ◽  
Random Step ◽  
Self Adaptive

The firefly algorithm (FA) is proposed as a heuristic algorithm, inspired by natural phenomena. The FA has attracted a lot of attention due to its effectiveness in dealing with various global optimization problems. However, it could easily fall into a local optimal value or suffer from low accuracy when solving high-dimensional optimization problems. To improve the performance of the FA, this paper adds the self-adaptive logarithmic inertia weight to the updating formula of the FA, and proposes the introduction of a minimum attractiveness of a firefly, which greatly improves the convergence speed and balances the global exploration and local exploitation capabilities of FA. Additionally, a step-size decreasing factor is introduced to dynamically adjust the random step-size term. When the dimension of a search is high, the random step-size becomes very small. This strategy enables the FA to explore solution more accurately. This improved FA (LWFA) was evaluated with ten benchmark test functions under different dimensions (D = 10, 30, and 100) and with standard IEEE CEC 2010 benchmark functions. Simulation results show that the performance of improved FA is superior comparing to the standard FA and other algorithms, i.e., particle swarm optimization, the cuckoo search algorithm, the flower pollination algorithm, the sine cosine algorithm, and other modified FA. The LWFA also has high performance and optimal efficiency for a number of optimization problems.

Sign in / Sign up

Export Citation Format

Share Document