Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms

In recent years, metaheuristic algorithms have revolutionized the world with their better problem solving capacity. Any metaheuristic algorithm has two phases: exploration and exploitation. The ability of the algorithm to solve a difficult optimization problem depends upon the efficacy of these two phases. These two phases are tied with a bridging mechanism, which plays an important role. This paper presents an application of chaotic maps to improve the bridging mechanism of Grasshopper Optimisation Algorithm (GOA) by embedding 10 different maps. This experiment evolves 10 different chaotic variants of GOA, and they are named as Enhanced Chaotic Grasshopper Optimization Algorithms (ECGOAs). The performance of these variants is tested over ten shifted and biased unimodal and multimodal benchmark functions. Further, the applications of these variants have been evaluated on three-bar truss design problem and frequency-modulated sound synthesis parameter estimation problem. Results reveal that the chaotic mechanism enhances the performance of GOA. Further, the results of the Wilcoxon rank sum test also establish the efficacy of the proposed variants.

[1]  Seyed Mohammad Mirjalili,et al.  Whale optimization approaches for wrapper feature selection , 2018, Appl. Soft Comput..

[2]  Akash Saxena,et al.  Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine , 2017, Journal of environmental and public health.

[3]  Rajesh Kumar,et al.  Binary Fireworks Algorithm Based Thermal Unit Commitment , 2015, Int. J. Swarm Intell. Res..

[4]  Abdolreza Hatamlou,et al.  Black hole: A new heuristic optimization approach for data clustering , 2013, Inf. Sci..

[5]  Akash Saxena,et al.  Performance Evaluation of Antlion Optimizer Based Regulator in Automatic Generation Control of Interconnected Power System , 2016 .

[6]  Konstantinos G. Margaritis,et al.  On benchmarking functions for genetic algorithms , 2001, Int. J. Comput. Math..

[7]  Sankalap Arora,et al.  Chaotic grey wolf optimization algorithm for constrained optimization problems , 2018, J. Comput. Des. Eng..

[8]  Bijaya K. Panigrahi,et al.  Binary Grey Wolf Optimizer for large scale unit commitment problem , 2018, Swarm Evol. Comput..

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

[10]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[11]  Aboul Ella Hassanien,et al.  MOGOA algorithm for constrained and unconstrained multi-objective optimization problems , 2017, Applied Intelligence.

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

[13]  Honglun Wang,et al.  Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm , 2017 .

[14]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

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

[16]  Xin-She Yang,et al.  Firefly algorithm with chaos , 2013, Commun. Nonlinear Sci. Numer. Simul..

[17]  Alireza Askarzadeh,et al.  A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm , 2016 .

[18]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

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

[20]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[21]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

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

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

[24]  Anders Lagerkvist,et al.  Stabilization of Pb- and Cu-contaminated soil using coal fly ash and peat. , 2007, Environmental pollution.

[25]  Amir Hossein Gandomi,et al.  Chaotic bat algorithm , 2014, J. Comput. Sci..

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

[27]  Kusum Deep,et al.  A novel Random Walk Grey Wolf Optimizer , 2019, Swarm Evol. Comput..

[28]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[29]  P. N. Suganthan,et al.  Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems , 2011 .

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

[31]  Ibrahim Eksin,et al.  A new optimization method: Big Bang-Big Crunch , 2006, Adv. Eng. Softw..

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

[33]  Amir Hossein Gandomi,et al.  Chaotic gravitational constants for the gravitational search algorithm , 2017, Appl. Soft Comput..

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

[35]  Xin-She Yang.  Test Problems in Optimization , 2010, 1008.0549.

[36]  Xin-She Yang,et al.  Chaos-enhanced accelerated particle swarm optimization , 2013, Commun. Nonlinear Sci. Numer. Simul..