Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications

Abstract This work proposes a novel optimization algorithm which can be used to solve a wide range of mathematical optimization problems where the global minimum or maximum is required. The new algorithm is based on random search and classical simulated annealing algorithm (it mimics the modern process of producing high-quality steel) and is designated dynamic differential annealed optimization (DDAO). The proposed algorithm was benchmarked for 51 test functions. The dynamic differential annealed optimization algorithm has been compared to a large number of highly cited optimization algorithms. Over numerical tests, DDAO has outperformed some of these algorithms in many cases and shown high performance. Constrained path planning and spring design problem were selected as a practical engineering optimization problem. DDAO converged to the global minimum of problems efficiently, and for spring design problem DDAO has found the best feasible solution than what is found by many algorithms.

[1]  D. Karaboga,et al.  A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm , 2004 .

[2]  Hamdan Daniyal,et al.  Barnacles Mating Optimizer: A new bio-inspired algorithm for solving engineering optimization problems , 2020, Eng. Appl. Artif. Intell..

[3]  Leandro dos Santos Coelho,et al.  A population-based simulated annealing algorithm for global optimization , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

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

[5]  Arshad Ahmad,et al.  A new optimization method: Electro-Search algorithm , 2017, Comput. Chem. Eng..

[6]  Ricardo Tanscheit,et al.  PSO+: A new particle swarm optimization algorithm for constrained problems , 2019, Appl. Soft Comput..

[7]  Thierry Iung,et al.  New developments of advanced high-strength steels for automotive applications , 2018, Comptes Rendus Physique.

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

[9]  Vahid Khatibi Bardsiri,et al.  Poor and rich optimization algorithm: A new human-based and multi populations algorithm , 2019, Eng. Appl. Artif. Intell..

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

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

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

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

[14]  S. Datta,et al.  Designing dual-phase steels with improved performance using ANN and GA in tandem , 2019, Computational Materials Science.

[15]  Michael N. Vrahatis,et al.  Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems , 2005, ICNC.

[16]  Hossam Faris,et al.  Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems , 2017, Knowl. Based Syst..

[17]  Roger J.-B. Wets,et al.  Minimization by Random Search Techniques , 1981, Math. Oper. Res..

[18]  Xin-She Yang,et al.  Variants of the Flower Pollination Algorithm: A Review , 2018 .

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

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

[21]  Victor Hugo C. de Albuquerque,et al.  Control of singularity trajectory tracking for robotic manipulator by genetic algorithms , 2019, J. Comput. Sci..

[22]  Huiling Chen,et al.  Slime mould algorithm: A new method for stochastic optimization , 2020, Future Gener. Comput. Syst..

[23]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[24]  Issam Zidi,et al.  A New Approach Based On the Hybridization of Simulated Annealing Algorithm and Tabu Search to Solve the Static Ambulance Routing Problem , 2019, KES.

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

[27]  Hui Zhao,et al.  A novel nature-inspired algorithm for optimization: Virus colony search , 2016, Adv. Eng. Softw..

[28]  Mauro A.S.S. Ravagnani,et al.  Heat exchanger network synthesis combining Simulated Annealing and Differential Evolution , 2019, Energy.

[29]  Hazim Nasir Ghafil Inverse Acceleration Solution for Robot Manipulators using Harmony Search Algorithm , 2016 .

[30]  Seyedali Mirjalili,et al.  Ant Colony Optimisation , 2018, Studies in Computational Intelligence.

[31]  D. Kumar OPTIMIZATION METHODS , 2007 .

[32]  Leandro dos Santos Coelho,et al.  Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization , 2016, Expert Syst. Appl..

[33]  Seung-Eock Kim,et al.  Reliability-based design optimization of nonlinear inelastic trusses using improved differential evolution algorithm , 2018, Adv. Eng. Softw..

[34]  L. A. Gallego,et al.  An improved simulated annealing–linear programming hybrid algorithm applied to the optimal coordination of directional overcurrent relays , 2020 .

[35]  Harish Sharma,et al.  Hybrid Artificial Bee Colony algorithm with Differential Evolution , 2017, Appl. Soft Comput..

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

[37]  O. Kolednik,et al.  On the microstructure control of the bendability of advanced high strength steels , 2018, Materials Science and Engineering: A.

[38]  Nurettin Cetinkaya,et al.  A new meta-heuristic optimizer: Pathfinder algorithm , 2019, Appl. Soft Comput..

[39]  Ali Mortazavi,et al.  Solution of structural and mathematical optimization problems using a new hybrid swarm intelligence optimization algorithm , 2019, Adv. Eng. Softw..

[40]  Erik Valdemar Cuevas Jiménez,et al.  An improved Simulated Annealing algorithm based on ancient metallurgy techniques , 2019, Appl. Soft Comput..

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

[42]  Pradeep Jangir,et al.  Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems , 2016, Applied Intelligence.

[43]  Xin-She Yang,et al.  Firefly Algorithm: Recent Advances and Applications , 2013, ArXiv.

[44]  I ScottKirkpatrick Optimization by Simulated Annealing: Quantitative Studies , 1984 .

[45]  Asim Imdad Wagan,et al.  A new metaheuristic optimization algorithm inspired by human dynasties with an application to the wind turbine micrositing problem , 2020, Appl. Soft Comput..

[46]  Mitsuo Gen,et al.  Find-Fix-Finish-Exploit-Analyze (F3EA) meta-heuristic algorithm: An effective algorithm with new evolutionary operators for global optimization , 2019, Comput. Ind. Eng..

[47]  Young-Min Kim,et al.  Simple method for tailoring the optimum microstructures of high-strength low-alloyed steels by the use of constitutive equation , 2019, Materials Science and Engineering: A.

[48]  Carlos A. Coello Coello,et al.  An updated survey of GA-based multiobjective optimization techniques , 2000, CSUR.

[49]  Xin-She Yang,et al.  Nature-Inspired Algorithms and Applied Optimization , 2018 .

[50]  Nikos D. Lagaros,et al.  Pity beetle algorithm - A new metaheuristic inspired by the behavior of bark beetles , 2018, Adv. Eng. Softw..

[51]  Farhad Soleimanian Gharehchopogh,et al.  Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems , 2018, Appl. Soft Comput..

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

[53]  Balamurugan Gopalsamy,et al.  Genetic Algorithm based Kinematic Synthesis of an Eight Bar Flap Deployment Mechanism in a Typical Transport Aircraft , 2018 .

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

[55]  Junjie Yang,et al.  Hierarchy Particle Swarm Optimization Algorithm (HPSO) and Its Application in Multi-Objective Operation of Hydropower Stations , 2011, 2011 3rd International Workshop on Intelligent Systems and Applications.

[56]  Károly Jármai,et al.  Comparative study of particle swarm optimization and artificial bee colony algorithms , 2018 .

[57]  Manuele Bicego,et al.  Orienteering-based informative path planning for environmental monitoring , 2019, Eng. Appl. Artif. Intell..

[58]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.

[59]  Ardeshir Bahreininejad,et al.  Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems , 2013, Appl. Soft Comput..

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

[61]  V. Thirunavukkarasu,et al.  Design Optimization of Mechanical Components Using an Enhanced Teaching-Learning Based Optimization Algorithm with Differential Operator , 2014 .

[62]  Ibrahim Berkan Aydilek A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems , 2018, Appl. Soft Comput..

[63]  Harish Sharma,et al.  Spider Monkey Optimization algorithm for numerical optimization , 2014, Memetic Computing.

[64]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..

[65]  Károly Jármai,et al.  Research and Application of Industrial Robot Manipulators in Vehicle and Automotive Engineering, a Survey , 2018 .

[66]  Padmavathi Kora,et al.  Hybrid Firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block , 2016 .

[67]  Shahram Pezeshk,et al.  School based optimization algorithm for design of steel frames , 2018, Engineering Structures.

[68]  N. Sadati,et al.  Hybrid Particle Swarm-Based-Simulated Annealing Optimization Techniques , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[69]  Károly Jármai,et al.  Optimization for Robot Modelling with MATLAB , 2020 .

[70]  Tapabrata Ray,et al.  Society and civilization: An optimization algorithm based on the simulation of social behavior , 2003, IEEE Trans. Evol. Comput..

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

[72]  Chen Hu,et al.  Stochastic gradient particle swarm optimization based entry trajectory rapid planning for hypersonic glide vehicles , 2018 .

[73]  Zhengyi Jiang,et al.  Thermomechanical processing of advanced high strength steels , 2018 .

[74]  Zhijiang Shao,et al.  Simultaneous dynamic optimization: A trajectory planning method for nonholonomic car-like robots , 2015, Adv. Eng. Softw..

[75]  María Dolores Rodríguez-Moreno,et al.  TERRA: A path planning algorithm for cooperative UGV-UAV exploration , 2019, Eng. Appl. Artif. Intell..

[76]  Christopher Hutchinson,et al.  Advanced high strength steel (AHSS) development through chemical patterning of austenite , 2018 .

[77]  Hossam Faris,et al.  Harris hawks optimization: Algorithm and applications , 2019, Future Gener. Comput. Syst..

[78]  Wenjian Luo,et al.  Differential evolution with dynamic stochastic selection for constrained optimization , 2008, Inf. Sci..

[79]  Felix Martinez-Rios,et al.  A new hybridized algorithm based on Population-Based Simulated Annealing with an experimental study of phase transition in 3-SAT , 2017, ICCSCI.

[80]  G. Hossein Behforooz A comparison of theE(3) and not-a-knot cubic splines , 1995 .

[81]  Yong Wang,et al.  Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization , 2010, Appl. Soft Comput..

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

[83]  Alessandro Gasparetto,et al.  Optimal trajectory planning for industrial robots , 2010, Adv. Eng. Softw..

[84]  Hyung Keun Park,et al.  Bayesian approach in predicting mechanical properties of materials: Application to dual phase steels , 2019, Materials Science and Engineering: A.

[85]  Xianlei Hu,et al.  Experiment on properties differentiation in tailor rolled blank of dual phase steel , 2019, Materials Science and Engineering: A.

[86]  Caro Lucas,et al.  A novel numerical optimization algorithm inspired from weed colonization , 2006, Ecol. Informatics.

[87]  A. Kaveh,et al.  A novel meta-heuristic optimization algorithm: Thermal exchange optimization , 2017, Adv. Eng. Softw..

[88]  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.

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

[90]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[91]  Abdellatif El Afia,et al.  A Self Controlled Simulated Annealing Algorithm using Hidden Markov Model State Classification , 2019, Procedia Computer Science.

[92]  Scott Kirkpatrick,et al.  Optimization by simulated annealing: Quantitative studies , 1984 .

[93]  M. O. Tokhi,et al.  Hybridizing invasive weed optimization with firefly algorithm for unconstrained and constrained optimization problems , 2017 .

[94]  Amir H. Gandomi,et al.  Marine Predators Algorithm: A nature-inspired metaheuristic , 2020, Expert Syst. Appl..

[95]  Amauri Garcia,et al.  An artificial immune system algorithm applied to the solution of an inverse problem in unsteady inward solidification , 2018, Adv. Eng. Softw..

[96]  Saoussen Krichen,et al.  A Hybrid Simulated Annealing Approach for the Patient Bed Assignment Problem , 2019, KES.

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

[98]  Lin Wang,et al.  New fruit fly optimization algorithm with joint search strategies for function optimization problems , 2019, Knowl. Based Syst..

[99]  Zong Woo Geem,et al.  A comparison study of harmony search and genetic algorithm for the max-cut problem , 2018, Swarm Evol. Comput..

[100]  Leandro dos Santos Coelho,et al.  Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems , 2010, Expert Syst. Appl..

[101]  Carlos A. Coello Coello,et al.  A simple multimembered evolution strategy to solve constrained optimization problems , 2005, IEEE Transactions on Evolutionary Computation.