A New Optimization Algorithm Based on Search and Rescue Operations

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.

[1]  Yujun Zheng.  Water wave optimization: A new nature-inspired metaheuristic , 2015, Comput. Oper. Res..

[2]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[3]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

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

[5]  I. Couzin,et al.  Effective leadership and decision-making in animal groups on the move , 2005, Nature.

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

[7]  V. Braibant,et al.  Structural optimization: A new dual method using mixed variables , 1986 .

[8]  Amir Hossein Gandomi,et al.  A multi-stage particle swarm for optimum design of truss structures , 2013, Neural Computing and Applications.

[9]  Wali Khan Mashwani,et al.  Reflected Adaptive Differential Evolution with Two External Archives for Large-Scale Global Optimization , 2016 .

[10]  S. O. Degertekin,et al.  Sizing truss structures using teaching-learning-based optimization , 2013 .

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

[12]  Harish Garg,et al.  An efficient biogeography based optimization algorithm for solving reliability optimization problems , 2015, Swarm Evol. Comput..

[13]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[14]  G. G. Wang,et al.  Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points , 2003 .

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

[16]  Seyed Jalaleddin Mousavirad,et al.  Human mental search: a new population-based metaheuristic optimization algorithm , 2017, Applied Intelligence.

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

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

[19]  Jui-Sheng Chou,et al.  Modified firefly algorithm for multidimensional optimization in structural design problems , 2016, Structural and Multidisciplinary Optimization.

[20]  Min-Yuan Cheng,et al.  Symbiotic Organisms Search: A new metaheuristic optimization algorithm , 2014 .

[21]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[22]  Ali Kaveh,et al.  A new metaheuristic for continuous structural optimization: water evaporation optimization , 2016 .

[23]  A. Kaveh,et al.  A new meta-heuristic method: Ray Optimization , 2012 .

[24]  Giovanni Iacca,et al.  A CMA-ES super-fit scheme for the re-sampled inheritance search , 2013, 2013 IEEE Congress on Evolutionary Computation.

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

[26]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[27]  J. Michael Herrmann,et al.  A Review of No Free Lunch Theorems, and Their Implications for Metaheuristic Optimisation , 2018 .

[28]  Liang Gao,et al.  Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems , 2015, Expert Syst. Appl..

[29]  T. Bakhshpoori,et al.  An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm , 2014 .

[30]  Yousef Hosseinzadeh,et al.  A Cultural Algorithm for Optimal Design of Truss Structures , 2015 .

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

[32]  Mauricio Zambrano-Bigiarini,et al.  Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements , 2013, 2013 IEEE Congress on Evolutionary Computation.

[33]  Bingo Wing-Kuen Ling,et al.  Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing , 2017 .

[34]  Andries Petrus Engelbrecht,et al.  A self-adaptive heterogeneous pso for real-parameter optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[35]  Hossein Nezamabadi-pour,et al.  A comprehensive survey on gravitational search algorithm , 2018, Swarm Evol. Comput..

[36]  Ali Kaveh,et al.  A SIMPLIFIED DOLPHIN ECHOLOCATION OPTIMIZATION METHOD FOR OPTIMUM DESIGN OF TRUSSES , 2014 .

[37]  Richard Henry Major.  Early Voyages to Terra Australis, Now Called Australia: EXTRACT FROM THE BOOK OF DISPATCHES FROM BATAVIA , 2010 .

[38]  R. Venkata Rao,et al.  Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems , 2012, Inf. Sci..

[39]  Ponnuthurai N. Suganthan,et al.  Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..

[40]  Varun Punnathanam,et al.  Yin-Yang-pair Optimization: A novel lightweight optimization algorithm , 2016, Eng. Appl. Artif. Intell..

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

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

[43]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[44]  Zong Woo Geem,et al.  A survey on applications of the harmony search algorithm , 2013, Eng. Appl. Artif. Intell..

[45]  Manoj Kumar,et al.  Genetic Algorithm: Review and Application , 2010 .

[46]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

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

[48]  Luca Maria Gambardella,et al.  A survey on metaheuristics for stochastic combinatorial optimization , 2009, Natural Computing.

[49]  Xin-She Yang,et al.  Application of the flower pollination algorithm in structural engineering , 2016 .

[50]  Hae Chang Gea,et al.  STRUCTURAL OPTIMIZATION USING A NEW LOCAL APPROXIMATION METHOD , 1996 .

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

[52]  Mohammed Azmi Al-Betar,et al.  Adaptive pair bonds in genetic algorithm: An application to real-parameter optimization , 2015, Appl. Math. Comput..

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

[54]  Jian Chai,et al.  Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty , 2017, Appl. Soft Comput..

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

[56]  GaoLiang,et al.  Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems , 2015 .

[57]  Yueh-Min Huang,et al.  A quantum-inspired Tabu search algorithm for solving combinatorial optimization problems , 2013, Soft Computing.

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

[59]  Mustafa Sonmez,et al.  Artificial Bee Colony algorithm for optimization of truss structures , 2011, Appl. Soft Comput..

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

[61]  S. O. Degertekin.  Improved harmony search algorithms for sizing optimization of truss structures , 2012 .

[62]  PunnathanamVarun,et al.  Yin-Yang-pair Optimization , 2016 .

[63]  Ali Kaveh,et al.  Colliding bodies optimization: A novel meta-heuristic method , 2014 .

[64]  Xiaodong Li,et al.  Investigation of self-adaptive differential evolution on the CEC-2013 real-parameter single-objective optimization testbed , 2013, 2013 IEEE Congress on Evolutionary Computation.