Artificial electric field algorithm with inertia and repulsion for spherical minimum spanning tree

Artificial electric field algorithm (AEFA) is a potential global optimization algorithm proposed in recent years and has been successfully applied to various engineering optimizations. However, precocious convergence tends to occur when solving complex engineering optimization problems. To avoid premature convergence to some extent, an artificial electric field algorithm with inertia and repulsion (IRAEFA) is proposed. The IRAEFA algorithm introduces the inertia mechanism and the repulsion between charges, expands the search space, increases the diversity of population, balances the exploration and development ability of the algorithm, and avoids the algorithm falling into the local optimal solution. Finally, the IRAEFA algorithm is used to solve the spherical mining spanning tree (MST) problem, and the results obtained are compared and analyzed with the results of other well-known metaheuristics optimization algorithms. Experimental results show that the proposed algorithm has better performance than other algorithms in solving spherical MST problems.

[1]  Ehsan Valian,et al.  A cuckoo search algorithm by Lévy flights for solving reliability redundancy allocation problems , 2013 .

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

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

[4]  Rinkle Rani,et al.  An Improved Artificial Electric Field Algorithm for Multi-Objective Optimization , 2020, Processes.

[5]  M. C. Crabb Counting nilpotent endomorphisms , 2006, Finite Fields Their Appl..

[6]  Fariborz Jolai,et al.  Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm , 2016, J. Comput. Des. Eng..

[7]  Yongquan Zhou,et al.  A Novel Discrete Cuckoo Search Algorithm for Spherical Traveling Salesman Problem , 2013 .

[8]  M. Khishe,et al.  Chimp optimization algorithm , 2020, Expert Syst. Appl..

[9]  J. Beardwood,et al.  The shortest path through many points , 1959, Mathematical Proceedings of the Cambridge Philosophical Society.

[10]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[11]  Xin Chen,et al.  A hybrid algorithm combining glowworm swarm optimization and complete 2-opt algorithm for spherical travelling salesman problems , 2017, Appl. Soft Comput..

[12]  Bo Xing,et al.  Fruit Fly Optimization Algorithm , 2014 .

[13]  L Janjanam,et al.  Volterra filter modelling of non-linear system using Artificial Electric Field algorithm assisted Kalman filter and its experimental evaluation. , 2020, ISA transactions.

[14]  Huimin Zhao,et al.  An Enhanced MSIQDE Algorithm With Novel Multiple Strategies for Global Optimization Problems , 2022, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

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

[16]  Ying Tan,et al.  Fireworks Algorithm for Optimization , 2010, ICSI.

[17]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

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

[19]  Chao-Hsien Chu,et al.  Genetic algorithms for communications network design - an empirical study of the factors that influence performance , 2001, IEEE Trans. Evol. Comput..

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

[21]  Xiaodong Wu,et al.  Small-World Optimization Algorithm for Function Optimization , 2006, ICNC.

[22]  Frank Harary,et al.  Graph Theory , 2016 .

[23]  A. Kaveh,et al.  A new optimization method: Dolphin echolocation , 2013, Adv. Eng. Softw..

[24]  M.H. Tayarani-N,et al.  Magnetic Optimization Algorithms a new synthesis , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

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

[26]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

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

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

[29]  Jing Wang,et al.  Swarm Intelligence in Cellular Robotic Systems , 1993 .

[30]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[31]  Mustafa Servet Kiran,et al.  TSA: Tree-seed algorithm for continuous optimization , 2015, Expert Syst. Appl..

[32]  Barry Webster,et al.  A Local Search Optimization Algorithm Based on Natural Principles of Gravitation , 2003, IKE.

[33]  Victor O. K. Li,et al.  A social spider algorithm for global optimization , 2015, Appl. Soft Comput..

[34]  Vitaly Osipov,et al.  The Filter-Kruskal Minimum Spanning Tree Algorithm , 2009, ALENEX.

[35]  Richard A. Formato,et al.  CENTRAL FORCE OPTIMIZATION: A NEW META-HEURISTIC WITH APPLICATIONS IN APPLIED ELECTROMAGNETICS , 2007 .

[36]  Ying-Tung Hsiao,et al.  A novel optimization algorithm: space gravitational optimization , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[37]  Anupam Yadav,et al.  AEFA: Artificial electric field algorithm for global optimization , 2019, Swarm Evol. Comput..

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

[39]  Anupam Yadav,et al.  Artificial electric field algorithm for engineering optimization problems , 2020, Expert Syst. Appl..

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

[41]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

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

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

[44]  Ying Tan,et al.  Advances in Swarm Intelligence, First International Conference, ICSI 2010, Beijing, China, June 12-15, 2010, Proceedings, Part II , 2010, ICSI.

[45]  N. Siddique,et al.  Central Force Optimization , 2017 .

[46]  Bilal Alatas,et al.  ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization , 2011, Expert Syst. Appl..

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

[48]  Serdar Ekinci,et al.  Opposition-based artificial electric field algorithm and its application to FOPID controller design for unstable magnetic ball suspension system , 2020 .

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

[50]  Anupam Yadav,et al.  Discrete artificial electric field algorithm for high-order graph matching , 2020, Appl. Soft Comput..

[51]  Rainer Storn,et al.  Differential Evolution Research – Trends and Open Questions , 2008 .

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

[53]  Zhang Li Research on minimum spanning tree based on prim algorithm , 2009 .

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

[55]  Hamed Shah-Hosseini,et al.  Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation , 2011, Int. J. Comput. Sci. Eng..

[56]  Amir H. Gandomi,et al.  The Arithmetic Optimization Algorithm , 2021, Computer Methods in Applied Mechanics and Engineering.

[57]  Serdar Korukoglu,et al.  Genetic Algorithm Based Solution for TSP on a Sphere , 2009 .

[58]  Erkan Ülker,et al.  The application of ant colony optimization in the solution of 3D traveling salesman problem on a sphere , 2017 .

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

[60]  Yongquan Zhou,et al.  Discrete greedy flower pollination algorithm for spherical traveling salesman problem , 2017, Neural Computing and Applications.