Nghiên cứu phân tích khám phá và ổn định của thuật toán điện trường nhân tạo

Abraham A, Konar A, Samal NR, Das S (2007) Stability analysis of the ant system dynamics with non-uniform pheromone deposition rules. In: 2007 IEEE congress on evolutionary computation. IEEE, pp 1103–1108 Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609 Askari Q, Saeed M, Younas I (2020) Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Syst Appl 161:113702 Bansal JC, Gopal A, Nagar AK (2018) Stability analysis of artificial bee colony optimization algorithm. Swarm Evol Comput 41:9–19 Biswas A, Das S, Abraham A, Dasgupta S (2010) Stability analysis of the reproduction operator in bacterial foraging optimization. Theor Comput Sci 411(21):2127–2139 Bonyadi MR, Michalewicz Z (2014) A locally convergent rotationally invariant particle swarm optimization algorithm. Swarm Intell 8(3):159–198 Bonyadi MR, Michalewicz Z (2015a) Analysis of stability, local convergence, and transformation sensitivity of a variant of the particle swarm optimization algorithm. IEEE Trans Evol Comput 20(3):370–385 Bonyadi MR, Michalewicz Z (2015b) Stability analysis of the particle swarm optimization without stagnation assumption. IEEE Trans Evol Comput 20(5):814–819 Canayaz M, Karcı A (2015) Investigation of cricket behaviours as evolutionary computation for system design optimization problems. Measurement 68:225–235 Chen T, Chen H (2009) Mixed–discrete structural optimization using a rank-niche evolution strategy. Eng Optim 41(1):39–58 Cleghorn CW, Engelbrecht AP (2014) A generalized theoretical deterministic particle swarm model. Swarm Intell 8(1):35–59 Dasgupta S, Das S, Biswas A, Abraham A (2009) On stability and convergence of the population-dynamics in differential evolution. Ai Commun 22(1):1–20 Deb K, Srinivasan A (2006) Innovization: Innovating design principles through optimization. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM, pp 1629–1636 Dhiman G, Kumar V (2019) Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowl-Based Syst 165:169–196 Dorigo M, Birattari M (2010) Ant colony optimization. Springer, Berlin Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm–a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166 Gandomi AH, Yang X-S, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89(23-24):2325–2336 Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68 Ghorbani F, Nezamabadi PH (2012) On the convergence analysis of gravitational search algorithm Gopal A, Bansal JC (2016) Stability analysis of differential evolution. In: 2016 international workshop on computational intelligence (IWCI). IEEE, pp 221–223 Guedria NB (2016) Improved accelerated pso algorithm for mechanical engineering optimization problems. Appl Soft Comput 40:455–467 Jiang S, Wang Y, Ji Z (2014) Convergence analysis and performance of an improved gravitational search algorithm. Appl Soft Comput 24:363–384 Kamboj VK, Nandi A, Bhadoria A, Sehgal S (2020) An intensify harris hawks optimizer for numerical and engineering optimization problems. Appl Soft Comput 89:106018 Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes university, engineering faculty, computer Ě Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84 Kaveh A, Eslamlou AD (2020) Water strider algorithm: A new metaheuristic and applications. In: Structures, vol 25. Elsevier, pp 520–541 Kennedy J (2010) Particle swarm optimization. In: Encyclopedia of machine learning, pp 760–766 Liu Q (2015) Order-2 stability analysis of particle swarm optimization. Evoluti Comput 23 (2):187–216 Meng OK, Pauline O, Kiong SC (2020) A carnivorous plant algorithm for solving global optimization problems. Appl Soft Comput :106833 Meng X. -B., Gao XZ, Liu Y, Zhang H (2015) A novel bat algorithm with habitat selection and doppler effect in echoes for optimization. Expert Syst Appl 42(17-18):6350–6364 Mirjalili S (2015) Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based Syst 89:228–249 Mirjalili S (2016a) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073 Mirjalili S (2016b) Sca: a sine cosine algorithm for solving optimization problems. Knowl-based Syst 96:120–133 Mirjalili S, Gandomi AH, Mirjalili S, Saremi S, Faris H, Mirjalili S (2017) Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191 Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67 Mirjalili S, Mirjalili S, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513 Mirjalili S, Mirjalili S, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61 Osyczka A (2002) Evolutionary algorithms for single and multicriteria design optimization Passino KM (2010) Bacterial foraging optimization. Int J Swarm Intell Res (IJSIR) 1(1):1–16 Połap D, Woźniak M (2021) Red fox optimization algorithm. Expert Syst Appl 166:114107 Poli R (2009) Mean and variance of the sampling distribution of particle swarm optimizers during stagnation. IEEE Trans Evol Comput 13(4):712–721 Rao RV, Savsani VJ, Vakharia D (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315 Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232–2248 Reed M (2012) Methods of modern mathematical physics: Functional analysis. Elsevier, Amsterdam Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359 Thanedar P, Vanderplaats G (1995) Survey of discrete variable optimization for structural design. J Struct Eng 121(2):301–306 Van den Bergh F, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inf Sci 176(8):937– 971 Van den Bergh F, Engelbrecht AP (2010) A convergence proof for the particle swarm optimiser. Fundamenta Informaticae 105(4):341–374 Wang Z, Luo Q, Zhou Y (2020), Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems. Eng Comput Wilke DN, Kok S, Groenwold AA (2007) Comparison of linear and classical velocity update rules in particle swarm optimization: notes on diversity. Int J Numer Methods Eng 70(8):962–984 Wu G, Mallipeddi R, Suganthan PN (2017) Problem definitions and evaluation criteria for the cec 2017 competition on constrained real-parameter optimization. National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu South Korea and Nanyang Technological University, Singapore, Technical Report Yadav A, Deep K (2013) Shrinking hypersphere based trajectory of particles in pso. Appl Math Comput 220:246–267 Yadav A, Deep K, Kim JH, Nagar AK (2016) Gravitational swarm optimizer for global optimization. Swarm Evol Comput 31:64–89 Yadav A et al (2019) Aefa: Artificial electric field algorithm for global optimization. Swarm Evol Comput 48:93–108 Yadav A et al (2020a) Discrete artificial electric field algorithm for high-order graph matching. Appl Soft Comput :106260 Yadav A, Kumar N et al (2020b) Artificial electric field algorithm for engineering optimization problems. Expert Syst Appl 149:113308 Yadav A, Kumar N, Kim et al (2020c) Development of discrete artificial electric field algorithm for quadratic assignment problems. In: International conference on harmony search algorithm. Springer, pp 411–421 Yalcin Y, Pekcan O (2783) Nuclear fission nuclear fusion algorithm for global optimization a modified big bang big crunch algorithm. Neural Comput and Applic 32(7):2751 Yang X-S, Hossein Gandomi A (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483 Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102 Yu C, Heidari AA, Chen H (2020) A quantum-behaved simulated annealing enhanced moth-flame optimization method. Appl Math Model Zhang Z, Ding S, Jia W (2019) A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Eng Appl Artif Intel 85:254–268