Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems

Abstract This paper presents a novel meta-heuristic algorithm named Chameleon Swarm Algorithm (CSA) for solving global numerical optimization problems. The base inspiration for CSA is the dynamic behavior of chameleons when navigating and hunting for food sources on trees, deserts and near swamps. This algorithm mathematically models and implements the behavioral steps of chameleons in their search for food, including their behavior in rotating their eyes to a nearly 360°scope of vision to locate prey and grab prey using their sticky tongues that launch at high speed. These foraging mechanisms practiced by chameleons eventually lead to feasible solutions when applied to address optimization problems. The stability of the proposed algorithm was assessed on sixty-seven benchmark test functions and the performance was examined using several evaluation measures. These test functions involve unimodal, multimodal, hybrid and composition functions with different levels of complexity. An extensive comparative study was conducted to demonstrate the efficacy of CSA over other meta-heuristic algorithms in terms of optimization accuracy. The applicability of the proposed algorithm in reliably addressing real-world problems was demonstrated in solving five constrained and computationally expensive engineering design problems. The overall results of CSA show that it offered a favorable global or near global solution and better performance compared to other meta-heuristics.

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

[2]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[4]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[5]  David H. Wolpert,et al.  Remarks on a recent paper on the "no free lunch" theorems , 2001, IEEE Trans. Evol. Comput..

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

[7]  Heba Al-Hiary,et al.  Modeling the Tennessee Eastman chemical process reactor using bio-inspired feedforward neural network (BI-FF-NN) , 2019 .

[8]  Heba Al-Hiary,et al.  A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm , 2020, Neural Computing and Applications.

[9]  Anabela Afonso,et al.  Overview of Friedman’s Test and Post-hoc Analysis , 2015, Commun. Stat. Simul. Comput..

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

[11]  Gaurav Dhiman,et al.  Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications , 2017, Adv. Eng. Softw..

[12]  Xin-She Yang,et al.  A framework for self-tuning optimization algorithm , 2013, Neural Computing and Applications.

[13]  Ponnuthurai N. Suganthan,et al.  Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[14]  Lin Xiao,et al.  Complex-Valued Discrete-Time Neural Dynamics for Perturbed Time-Dependent Complex Quadratic Programming With Applications , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Tunjo Perić,et al.  Multi-objective programming methodology for solving economic diplomacy resource allocation problem , 2019, Croatian Operational Research Review.

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

[17]  Marjan Mernik,et al.  A parameter control method of evolutionary algorithms using exploration and exploitation measures with a practical application for fitting Sovova's mass transfer model , 2013, Appl. Soft Comput..

[18]  P. Aerts,et al.  The mechanics of prey prehension in chameleons. , 2000, The Journal of experimental biology.

[19]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

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

[21]  Gülay Tezel,et al.  Artificial algae algorithm (AAA) for nonlinear global optimization , 2015, Appl. Soft Comput..

[22]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[23]  Heba Al-Hiary,et al.  Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study , 2020 .

[24]  S. SreeRanjiniK.,et al.  Expert Systems With Applications , 2022 .

[25]  Irakli Basheleishvili,et al.  Dynamic Programming and Greedy Algorithm Strategy for Solving Several Classes of Graph Optimization Problems , 2019 .

[26]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[27]  Vijay Kumar,et al.  Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems , 2019, Knowl. Based Syst..

[28]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[29]  A. L. Sangal,et al.  Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization , 2020, Eng. Appl. Artif. Intell..

[30]  Heba Al-Hiary,et al.  A novel lifetime scheme for enhancing the convergence performance of salp swarm algorithm , 2020, Soft Comput..

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

[32]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for CEC 2015 Special Session on Bound Constrained Single-Objective Computationally Expensive Numerical Optimization , 2015 .

[33]  Carlos A. Coello Coello,et al.  Useful Infeasible Solutions in Engineering Optimization with Evolutionary Algorithms , 2005, MICAI.

[34]  Amar Ramdane-Cherif,et al.  A comprehensive survey of Crow Search Algorithm and its applications , 2020, Artificial Intelligence Review.

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

[36]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[37]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

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

[39]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[40]  Mohammad Hadi Afshar,et al.  Application of cellular automata to size and topology optimization of truss structures , 2012 .

[41]  Vijander Singh,et al.  A novel nature-inspired algorithm for optimization: Squirrel search algorithm , 2019, Swarm Evol. Comput..

[42]  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.

[43]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

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

[45]  E. Sandgren,et al.  Nonlinear Integer and Discrete Programming in Mechanical Design Optimization , 1990 .

[46]  Seyedali Mirjalili,et al.  Equilibrium optimizer: A novel optimization algorithm , 2020, Knowl. Based Syst..

[47]  Abhishek Gupta,et al.  Optimization Algorithms Combining (Meta)heuristics and Mathematical Programming and Its Application in Engineering , 2018, Mathematical Problems in Engineering.