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Wan Fang
CNKI
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Wuhan University
Latest Article
A Novel Improved Bird Swarm Algorithm for Solving Bound Constrained Optimization Problems
Time:2019-8-28  
WANG Yuhe, WAN Zhongping†, PENG Zhenhua
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, Hubei, China
Abstract:
Bird swarm algorithm (BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimization problems. To enhance the performance of BSA, handling boundary constraints are applied to fix the candidate solutions that are out of boundary or on the boundary in iterations, which can boost the diversity of the swarm to avoid the premature problem. On the other hand, we accelerate the foraging behavior by adjusting the cognitive and social components the sin cosine coefficients. Simulation results and comparison based on sixty benchmark functions demonstrate that the improved BSA has superior performance over the BSA in terms of almost all functions.
Key words:improved bird swarm algorithm; handling boundary constraints; foraging behavior; heuristic algorithm
CLC number:O 224
References:
[1]	Kuo H C, Lin C H. A directed genetic algorithm for global optimization [J]. Applied Math Computation, 2013, 219(14): 7348-7364.
[2]	Das S, Suganthan P N. Differential evolution: A survey of the state-of-the-art [J]. IEEE T Evolut Comput, 2011, 15(1): 4-31.
[3]	Bratton D, Kennedy J. Defining a standard for particle swarm optimization [J]. 2007 IEEE Swarm Intelligence Symposium, 2007, 107(1): 120-127.
[4]	Karaboga D, Akay B. A comparative study of artificial bee colony algorithm [J]. Applied Mathematics & Computation, 2009, 214(1): 108-132.
[5]	Dorigo M, Stützle T. The ant colony optimization metaheu-ristic [J]. New Ideas in Optimization, 2009, 28(3): 25-64.
[6]	Gao X Z, Wu Y, Zenger K, et al. Artificial fish swarm algo-rithm: A survey of the state-of-the-art, hybridization, combi-nation and indicative applications [J]. Artificial Intelligence Review, 2014, 42(4): 965-997.
[7]	Yang X S. A new metaheuristic bat-inspired algorithm [J]. Computer Knowledge & Technology, 2010, (284): 65-74.
[8]	Gandomi A H, Alavi A H. Krill herd algorithm: A new bio-inspired optimization algorithm [J]. Communications in Nonlinear Science & Numerical Simulation, 2012, 17(12): 4831-4845.
[9]	Wang Z W, Wang G M, Wan Z P. A novel hybrid vortex search and artificial bee colony algorithm for numerical op-timization problems [J]. Wuhan University Journal of Natural Sciences, 2017, 22(4): 295-306.
[10]	Meng X B, Gao X Z, Lu L, et al. A new bio-inspired opti-mization algorithm: bird swarm algorithm [J]. Journal of Experimental & Theoretical Artificial Intelligence, 2016, 28(4): 673-687.
[11]	Xu C, Yang R. Parameter estimation for chaotic systems using improved bird swarm algorithm [J]. Modern Physics Letters B, 2017, 31(36): 1750346. DOI: http://dx.doi.org/10. 1142/S021 7984917503468.
[12]	Jian C, Li M, Kuang X. Edge cloud computing service composition based on modified bird swarm optimization in the internet of things [J]. Cluster Computing, 2018, (12): 1-9.
[13]	Wang X, Deng Y, Duan H. Edge-based target detection for unmanned aerial vehicles using competitive bird swarm al-gorithm [J]. Aerospace Science & Technology, 2018. DOI: https:// doi.org/10.1016/j.ast.2018.04.047.
[14]	Zhang W J, Xie X F, Bi D C. Handling boundary constraints for numerical optimization by particle swarm flying in peri-odic search space [C] // Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat No04TH8753). Portland: IEEE, 2004, 2: 2307-2311 . 
[15]	Trelea I C. The particle swarm optimization algorithm: Convergence analysis and parameter selection [J]. Infor-mation Processing Letters, 2003, 85(6): 317-325.
[16]	Souravlias D, Parsopoulos K E. Particle swarm optimization with neighborhood-based budget allocation [J]. International Journal of Machine Learning & Cybernetics, 2014, 44(3): 1-27.
[17]	Chen K, Zhou F, Yin L, et al. A hybrid particle swarm opti-mizer with sine cosine acceleration coefficients [J]. Infor-mation Sciences, 2018, 422: 218-241.
[18]	Ratnaweera A, Halgamuge S K, Watson H C. Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients [M]. Piscataway: IEEE, 2004.
[19]	Johnzen C. Cuckoo search: Recent advances and applications [J]. Neural Computing & Applications, 2014, 24(1): 169-174.
[20]	Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm [J]. Journal of Global Optimization, 2007, 39(3): 459-471.
[21]	Ortiz-boyer D, Hervas-martinez C, Garcia-pedrajas N. CIXL2: A crossover operator for evolutionary algorithms based on population features [J]. AI Access Foundation, 2005, (24): 1-48.
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