A Novel Swarm Intelligence Based Optimization Method: Harris' Hawk Optimization

Swarm intelligence is a modern optimization technique, and one of the most promising techniques for solving optimization problems. In this paper, a new swarm intelligence based algorithm namely, Harris’ Hawk Optimizer (HHO) is proposed. The algorithm mimics the cooperative hunting behaviour of Harris’ hawks. The algorithm is analysed for twenty five well known benchmark functions. Performance of HHO is compared with Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO) and The Whale Optimization Algorithm (WOA). HHO is implemented and results present HHO as one of the efficient optimization methods.

[1]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

[2]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[3]  Zelda B. Zabinsky,et al.  A Numerical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems , 2005, J. Glob. Optim..

[4]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[5]  Harish Sharma,et al.  Balanced artificial bee colony algorithm , 2013, Int. J. Artif. Intell. Soft Comput..

[6]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[7]  Enrique Alba,et al.  The exploration/exploitation tradeoff in dynamic cellular genetic algorithms , 2005, IEEE Transactions on Evolutionary Computation.

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[9]  Singiresu S. Rao.  Engineering Optimization : Theory and Practice , 2010 .

[10]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

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

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

[13]  J. Bednarz,et al.  Cooperative Hunting Harris' Hawks (Parabuteo unicinctus) , 1988, Science.

[14]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[15]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[16]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[17]  Andries Petrus Engelbrecht,et al.  Measuring exploration/exploitation in particle swarms using swarm diversity , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[18]  Ingo Rechenberg,et al.  Evolution Strategy: Nature’s Way of Optimization , 1989 .