A Hybrid Meta-Heuristic Method Based on Firefly Algorithm and Krill Herd

This study proposes a new firefly-inspired krill herd (FKH) optimization method based on integration of firefly and krill herd algorithms. FKH introduces an attractiveness and light intensity updating (ALIU) operator originally used in firefly algorithm into the krill herd method. This is basically done to improve local search technique and promote the diversity of the population to avoid a premature convergence. Moreover, an elitism strategy is adopted to maintain the optimal krill with the best fitness when updating the krill. The performance of the FKH method is verified using fifteen different benchmark functions. The results indicate that FKH performs more accurate and effective than the basic krill herd and other optimization algorithms.

[1]  Seyed Mohammad Mirjalili,et al.  Evolutionary population dynamics and grey wolf optimizer , 2015, Neural Computing and Applications.

[2]  Luo Liu,et al.  A hybrid meta-heuristic DE/CS Algorithm for UCAV path planning , 2012 .

[3]  Andrew Lewis,et al.  Biogeography-based optimisation with chaos , 2014, Neural Computing and Applications.

[4]  Amir Hossein Gandomi,et al.  Multi-stage genetic programming: A new strategy to nonlinear system modeling , 2011, Inf. Sci..

[5]  Luo Liu,et al.  Hybridizing harmony search with biogeography based optimization for global numerical optimization , 2013 .

[6]  Fei Xue,et al.  Optimal parameter settings for bat algorithm , 2015, Int. J. Bio Inspired Comput..

[7]  Yong Xia,et al.  Cavitary nodule segmentation in computed tomography images based on self-generating neural networks and particle swarm optimisation , 2015, Int. J. Bio Inspired Comput..

[8]  Amir Hossein Gandomi,et al.  A hybrid method based on krill herd and quantum-behaved particle swarm optimization , 2015, Neural Computing and Applications.

[9]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

[10]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[11]  Dexuan Zou,et al.  An improved differential evolution algorithm for the task assignment problem , 2011, Eng. Appl. Artif. Intell..

[12]  Minghao Yin,et al.  Animal migration optimization: an optimization algorithm inspired by animal migration behavior , 2014, Neural Computing and Applications.

[13]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[14]  Amir Hossein Gandomi,et al.  Chaotic cuckoo search , 2015, Soft Computing.

[15]  A. Gandomi,et al.  A novel improved accelerated particle swarm optimization algorithm for global numerical optimization , 2014 .

[16]  Amir Hossein Gandomi,et al.  Hybridizing harmony search algorithm with cuckoo search for global numerical optimization , 2014, Soft Computing.

[17]  Xiangtao Li,et al.  An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure , 2013, Adv. Eng. Softw..

[18]  Xiao Zhi Gao,et al.  Fusion of clonal selection algorithm and differential evolution method in training cascade-correlation neural network , 2009, Neurocomputing.

[19]  Jianhua Wu,et al.  Solving 0-1 knapsack problem by a novel global harmony search algorithm , 2011, Appl. Soft Comput..

[20]  Amir Hossein Gandomi,et al.  Hybrid krill herd algorithm with differential evolution for global numerical optimization , 2014, Neural Computing and Applications.

[21]  Leandro dos Santos Coelho,et al.  Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems , 2018, Int. J. Bio Inspired Comput..

[22]  Minghao Yin,et al.  Application of Differential Evolution Algorithm on Self-Potential Data , 2012, PloS one.

[23]  Dexuan Zou,et al.  A novel global harmony search algorithm for reliability problems , 2010, Comput. Ind. Eng..

[24]  Xiangtao Li,et al.  Enhancing the performance of cuckoo search algorithm using orthogonal learning method , 2013, Neural Computing and Applications.

[25]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[26]  Gai-Ge Wang,et al.  A New Improved Firefly Algorithm for Global Numerical Optimization , 2014 .

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

[28]  Amir Hossein Alavi,et al.  Krill herd: A new bio-inspired optimization algorithm , 2012 .

[29]  Xiangtao Li,et al.  Self-adaptive constrained artificial bee colony for constrained numerical optimization , 2012, Neural Computing and Applications.

[30]  Xin-She Yang,et al.  Binary bat algorithm , 2013, Neural Computing and Applications.

[31]  Xiaodong Li,et al.  Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale , 2009 .

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

[33]  Amir Hossein Gandomi,et al.  Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect , 2012, Appl. Soft Comput..

[34]  Seyed Mohammad Mirjalili,et al.  Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.

[35]  Amir Hossein Alavi,et al.  An effective krill herd algorithm with migration operator in biogeography-based optimization , 2014 .

[36]  Xiangtao Li,et al.  Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm , 2014 .

[37]  A. Gandomi,et al.  Mixed variable structural optimization using Firefly Algorithm , 2011 .

[38]  Xiangtao Li,et al.  A perturb biogeography based optimization with mutation for global numerical optimization , 2011, Appl. Math. Comput..

[39]  Simon Fong,et al.  A heuristic optimization method inspired by wolf preying behavior , 2015, Neural Computing and Applications.

[40]  Xiangtao Li,et al.  Multi-operator based biogeography based optimization with mutation for global numerical optimization , 2012, Comput. Math. Appl..

[41]  Yu Liu,et al.  A New Bio-inspired Algorithm: Chicken Swarm Optimization , 2014, ICSI.

[42]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[43]  Andrew Lewis,et al.  Let a biogeography-based optimizer train your Multi-Layer Perceptron , 2014, Inf. Sci..

[44]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

[45]  Amir Hossein Gandomi,et al.  Stud krill herd algorithm , 2014, Neurocomputing.

[46]  Amir Hossein Gandomi,et al.  Bat algorithm for constrained optimization tasks , 2012, Neural Computing and Applications.

[47]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[48]  Xin Wang,et al.  A novel global harmony search algorithm for task assignment problem , 2010, J. Syst. Softw..

[49]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[50]  Peter J. Fleming,et al.  The Stud GA: A Mini Revolution? , 1998, PPSN.

[51]  Amir Hossein Gandomi,et al.  Chaotic Krill Herd algorithm , 2014, Inf. Sci..

[52]  Andrew Lewis,et al.  A comparison of multi-objective optimisation metaheuristics on the 2D airfoil design problem , 2013 .

[53]  Amir Hossein Gandomi,et al.  A new improved krill herd algorithm for global numerical optimization , 2014, Neurocomputing.

[54]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[55]  Gaige Wang,et al.  Dynamic Deployment of Wireless Sensor Networks by Biogeography Based Optimization Algorithm , 2012, J. Sens. Actuator Networks.

[56]  Leandro dos Santos Coelho,et al.  Binary optimization using hybrid particle swarm optimization and gravitational search algorithm , 2014, Neural Computing and Applications.

[57]  Minghao Yin,et al.  Multiobjective Binary Biogeography Based Optimization for Feature Selection Using Gene Expression Data , 2013, IEEE Transactions on NanoBioscience.

[58]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[59]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[60]  Amir Hossein Alavi,et al.  Levy-flight krill herd algorithm , 2013 .

[61]  Jianbin Qiu,et al.  Model Reduction for Discrete-Time Markovian Jump Systems with Deficient Mode Information , 2013 .

[62]  Yu Liu,et al.  A new bio-inspired optimisation algorithm: Bird Swarm Algorithm , 2016, J. Exp. Theor. Artif. Intell..

[63]  Qidi Wu,et al.  Bat algorithm with Gaussian walk , 2014, Int. J. Bio Inspired Comput..