A new and efficient firefly algorithm for numerical optimization problems

Firefly algorithm (FA) is an excellent global optimizer based on swarm intelligence. Some recent studies show that FA was used to optimize various engineering problems. However, there are some drawbacks for FA, such as slow convergence rate and low precision solutions. To tackles these issues, a new and efficient FA (namely NEFA) is proposed. In NEFA, three modified strategies are employed. First, a new attraction model is used to determine the number of attracted fireflies. Second, a new search operator is designed for some better fireflies. Third, the step factor is dynamically updated during the iterations. Experiment verification is carried out on ten famous benchmark functions. Experimental results demonstrate that our new approach NEFA is superior to three other different versions of FA.

[1]  Hui Wang,et al.  Firefly algorithm with random attraction , 2016, Int. J. Bio Inspired Comput..

[2]  Jaehong Lee,et al.  An adaptive hybrid evolutionary firefly algorithm for shape and size optimization of truss structures with frequency constraints , 2018 .

[3]  Farid Nouioua,et al.  Quantum-inspired firefly algorithm with particle swarm optimization for discrete optimization problems , 2016, Soft Comput..

[4]  Yu Xue,et al.  A hybrid multi-objective firefly algorithm for big data optimization , 2017, Appl. Soft Comput..

[5]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[6]  Cyril Fonlupt,et al.  A set of new compact firefly algorithms , 2017, Swarm Evol. Comput..

[7]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[8]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Iztok Fister,et al.  Memetic firefly algorithm for combinatorial optimization , 2012, 1204.5165.

[10]  Zhihua Cui,et al.  Bat algorithm with triangle-flipping strategy for numerical optimization , 2018, Int. J. Mach. Learn. Cybern..

[11]  Hong-Bin Shen,et al.  A Non-homogeneous Firefly Algorithm and Its Convergence Analysis , 2016, J. Optim. Theory Appl..

[12]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010 .

[13]  Nawaf N. Hamadneh,et al.  Continuous versions of firefly algorithm: a review , 2017, Artificial Intelligence Review.

[14]  Hui Wang,et al.  Gaussian Bare-Bones Differential Evolution , 2013, IEEE Transactions on Cybernetics.

[15]  Jinjun Chen,et al.  Hybrid multi-objective cuckoo search with dynamical local search , 2018, Memetic Comput..

[16]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[17]  Hui Wang,et al.  Firefly algorithm with neighborhood attraction , 2017, Inf. Sci..

[18]  Zhijian Wu,et al.  Enhancing particle swarm optimization using generalized opposition-based learning , 2011, Inf. Sci..

[19]  Hui Wang,et al.  Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism , 2017, Soft Comput..

[20]  Hui Wang,et al.  Firefly algorithm with adaptive control parameters , 2016, Soft Computing.

[21]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[22]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[23]  Zhijian Wu,et al.  Multi-strategy ensemble artificial bee colony algorithm , 2014, Inf. Sci..

[24]  Cemal Köse,et al.  A modified firefly algorithm for global minimum optimization , 2018, Appl. Soft Comput..

[25]  Mingwen Wang,et al.  Enhancing the modified artificial bee colony algorithm with neighborhood search , 2017, Soft Comput..

[26]  Songwei Huang,et al.  Modified firefly algorithm based multilevel thresholding for color image segmentation , 2017, Neurocomputing.

[27]  Hui Wang,et al.  Diversity enhanced particle swarm optimization with neighborhood search , 2013, Inf. Sci..

[28]  Janez Brest,et al.  A comprehensive review of firefly algorithms , 2013, Swarm Evol. Comput..