Similar articles

Abstract: A single strategy used in the firefly algorithm (FA) cannot effectively solve the complex optimal scheduling problem. Thus, we propose the FA with division of roles (DRFA). Herein, fireflies are divided into leaders, developers, and followers, while a learning strategy is assigned to each role: the leader chooses the greedy cauchy mutation; the developer chooses two leaders randomly and uses the elite neighborhood search strategy for local development; the follower randomly selects two excellent particles for global exploration. To improve the efficiency of the fixed step size used in FA, a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages. Role division can balance the development and exploration ability of the algorithm. The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems. The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of optimal scheduling of cascade reservoirs.

面向复杂优化调度的角色分工萤火虫算法

[1]Alomoush W, Omar K, Alrosan A, et al., 2020. Firefly photinus search algorithm. J King Univ-Comput Inform Sci, 32(5):599-607.

[2]Arunachalam S, AgnesBhomila T, Ramesh Babu M, 2014. Hybrid particle swarm optimization algorithm and firefly algorithm based combined economic and emission dispatch including valve point effect. Proc 5^th Int Conf on Swarm, Evolutionary, and Memetic Computing, p.647-660.

[3]Aydilek IB, 2018. A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Soft Comput, 66:232-249.

[4]Brest J, Maučec MS, 2011. Self-adaptive differential evolution algorithm using population size reduction and three strategies. Soft Comput, 15(11):2157-2174.

[5]Chen Q, Liu B, Zhang Q, et al., 2015. Problem definitions and evaluation criteria for CEC 2015 Special Session on Bound Constrained Single-Objective Computationally Expensive Numerical Optimization. Proc IEEE Congress on Evolutionary Computation, p.84-88.

[6]Cook SA, 1971. The complexity of theorem-proving procedures. Proc 3^rd Annual ACM Symp on Theory of Computing, p.151-158.

[7]Cui ZH, Cao Y, Cai XJ, et al., 2019. Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things. J Parall Distrib Comput, 132:217-229.

[8]Fan TH, Yao ZF, Han LZ, et al., 2021. Density peaks clustering based on k-nearest neighbors sharing. Concurr Comput Pract Exp, 33(5):e5993.

[9]Fister I, Fister IJr, Yang XS, et al., 2013. A comprehensive review of firefly algorithms. Swarm Evol Comput, 13:34-46.

[10]Fister IJr, Yang XS, Fister I, et al., 2012. Memetic firefly algorithm for combinatorial optimization. Mathematics, 2012:75-86.

[11]Gao WF, Chan FTS, Huang LL, et al., 2015. Bare bones artificial bee colony algorithm with parameter adaptation and fitness-based neighborhood. Inform Sci, 316:180-200.

[12]García S, Molina D, Lozano M, et al., 2009. A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization. J Heurist, 15(6):617-644.

[13]Gope S, Goswami AK, Tiwari PK, et al., 2016. Rescheduling of real power for congestion management with integration of pumped storage hydro unit using firefly algorithm. Int J Electr Power Energy Syst, 83:434-442.

[14]Guo BY, Zhuang ZJ, Pan JS, et al., 2021. Optimal design and simulation for PID controller using fractional-order fish migration optimization algorithm. IEEE Access, 9:8808-8819.

[15]Jadon SS, Bansal JC, Tiwari R, et al., 2015. Accelerating artificial bee colony algorithm with adaptive local search. Memet Comput, 7(3):215-230.

[16]Kassandra T, Rojali, Suhartono D, 2018. Resource-constrained project scheduling problem using firefly algorithm. Proc Comput Sci, 135:534-543.

[17]Kennedy J, Eberhart R, 1995. Particle swarm optimization. Proc Int Conf on Neural Networks, p.1942-1948.

[18]Kiran MS, Hakli H, Gunduz M, et al., 2015. Artificial bee colony algorithm with variable search strategy for continuous optimization. Inform Sci, 300:140-157.

[19]Kora P, Krishna KSR, 2016. Hybrid firefly and particle swarm optimization algorithm for the detection of bundle branch block. Int J Cardiov Acad, 2(1):44-48.

[20]Liang JJ, Qin AK, Suganthan PN, et al., 2006. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput, 10(3):281-295.

[21]Lloyd JE, 1971. Bioluminescent communication in insects. Ann Rev Entomol, 16:97-122.

[22]Lv L, Zhao J, 2018. The firefly algorithm with Gaussian disturbance and local search. J Signal Process Syst, 90(8-9):1123-1131.

[23]Lv L, Zhao J, Wang JY, et al., 2019. Multi-objective firefly algorithm based on compensation factor and elite learning. Fut Gener Comput Syst, 91:37-47.

[24]Lv L, Wang JY, Wu RX, et al., 2021. Density peaks clustering based on geodetic distance and dynamic neighbourhood. Int J Bio-Inspir Comput, 17(1):24-33.

[25]Meng ZY, Pan JS, Xu HR, 2016. QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm: a cooperative swarm based algorithm for global optimization. Knowl-Based Syst, 109:104-121.

[26]Moeini R, Babaei M, 2020. Hybrid SVM-CIPSO methods for optimal operation of reservoir considering unknown future condition. Appl Soft Comput, 95:106572.

[27]Ohba N, 2004. Flash communication systems of Japanese fireflies. Integr Comp Biol, 44(3):225-233.

[28]Pan JS, Liu NX, Chu SC, et al., 2020. An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems. Inform Sci, 561:304-325.

[29]Pan JS, Sun XX, Chu SC, et al., 2021. Digital watermarking with improved SMS applied for QR code. Eng Appl Artif Intell, 97:104049.

[30]Ritthipakdee A, Thammano A, Premasathian N, et al., 2017. Firefly mating algorithm for continuous optimization problems. Comput Intell Neurosci, 2017:8034573.

[31]Song PC, Chu SC, Pan JS, et al., 2020. Phasmatodea population evolution algorithm and its application in length-changeable incremental extreme learning machine. Proc 2^nd Int Conf on Industrial Artificial Intelligence, p.1-5.

[32]Sun H, Deng ZC, Zhao J, et al., 2019. Hybrid mean center opposition-based learning particle swarm optimization. Acta Electron Sin, 47(9):1809-1818 (in Chinese).

[33]Takeuchi M, Matsushita H, Uwate Y, et al., 2015. Firefly algorithm distinguishing between males and females for minimum optimization problems. Proc IEEE Workshop on Nonlinear Circuit Networks, p.50-51.

[34]Tian AQ, Chu SC, Pan JS, et al., 2020. A compact pigeon-inspired optimization for maximum short-term generation mode in cascade hydroelectric power station. Sustainability, 12(3):767.

[35]Wang CF, Song WX, 2019. A novel firefly algorithm based on gender difference and its convergence. Appl Soft Comput, 80:107-124.

[36]Wang GG, Cai XJ, Cui ZH, et al., 2020. High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. IEEE Trans Emerg Top Comput, 8(1):20-30.

[37]Wang H, Sun H, Li CH, et al., 2013. Diversity enhanced particle swarm optimization with neighborhood search. Inform Sci, 223:119-135.

[38]Wang H, Wang WJ, Sun H, et al., 2016. Firefly algorithm with random attraction. Int J Bio-Inspir Comput, 8(1):33-41.

[39]Wang H, Zhou XY, Sun H, et al., 2017a. Firefly algorithm with adaptive control parameters. Soft Comput, 21(17):5091-5102.

[40]Wang H, Cui ZH, Sun H, et al., 2017b. Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft Comput, 21(18):5325-5339.

[41]Wang Y, Cai ZX, Zhang QF, 2011. Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput, 15(1):55-66.

[42]Wu HS, Xue JJ, Xiao RB, et al., 2020. Uncertain bilevel knapsack problem based on an improved binary wolf pack algorithm. Front Inform Technol Electron Eng, 21(9):1356-1368.

[43]Wu N, 2020. Research on Scheduling Optimization Models and Corresponding Algorithms for Container Terminal under Abnormal Working Conditions. PhD Thesis, Dalian Maritime University, Dalian, China (in Chinese).

[44]Xiao RB, Wang YC, 2018. Labour division in swarm intelligence for allocation problems: a survey. Int J Bio-Inspir Comput, 12(2):71-86.

[45]Xiao RB, Zhang YF, Huang ZD, 2015. Emergent computation of complex systems: a comprehensive review. Int J Bio-Inspir Comput, 7(2):75-97.

[46]Xu JG, Dai GZ, Wang HA, 2004. An overview of theories and methods of production scheduling. J Comput Res Dev, 41(2):257-267 (in Chinese).

[47]Yang XS, 2008. Nature-Inspired Metaheuristic Algorithms. Luniver Press, Frome, UK.

[48]Yang XS, 2010. Engineering Optimization: an Introduction with Metaheuristic Applications. John Wiley & Sons, Hoboken, US.

[49]Yu BH, Wang JW, Li CL, et al., 2004. DP with successive approximation for solving hydropower unit commitment problem. Centr China Electr Power, 17(6):1-3 (in Chinese).

[50]Yu SH, Su SB, Lu QP, et al., 2014. A novel wise step strategy for firefly algorithm. Int J Comput Math, 91(12):2507-2513.

[51]Yu SH, Zhu SL, Ma Y, et al., 2015. A variable step size firefly algorithm for numerical optimization. Appl Math Comput, 263:214-220.

[52]Zhang HW, Xie JW, Lu WL, et al., 2017. A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar. Front Inform Technol Electron Eng, 18(11):1806-1816.

[53]Zhang JQ, Sanderson AC, 2009. JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput, 13(5):945-958.

[54]Zhang MQ, Wang H, Cui ZH, et al., 2018. Hybrid multi-objective cuckoo search with dynamical local search. Memet Comput, 10(2):199-208.

[55]Zhao J, Fan TH, Lü L, et al., 2017a. Adaptive intelligent single particle optimizer based image de-noising in shearlet domain. Intell Autom Soft Comput, 23(4):661-666.

[56]Zhao J, Lv L, Wang H, et al., 2017b. Particle swarm optimization based on vector Gaussian learning. KSII Trans Intern Inform Syst, 11(4):2038-2057.

[57]Zhao J, Xie ZF, Lü L, et al., 2018. Firefly algorithm with deep learning. Acta Electron Sin, 46(11):2633-2641 (in Chinese).

[58]Zhao J, Chen WP, Ye J, et al., 2020. Firefly algorithm based on level-based attracting and variable step size. IEEE Access, 8:58700-58716.

[59]Zhao J, Yao ZF, Lü L, et al., 2021. Density peaks clustering based on mutual neighbor degree. Contr Dec, 36(3):543-552.

[60]Zhou XY, Wang H, Wang MW, et al., 2017. Enhancing the modified artificial bee colony algorithm with neighborhood search. Soft Comput, 21(10):2733-2743.

[61]Zou DX, Wang GG, Pan G, et al., 2016. A modified simulated annealing algorithm and an excessive area model for floorplanning using fixed-outline constraints. Front Inform Technol Electron Eng, 17(11):1228-1244.