[1] KHALID O W, ISA N A M, SAKIM H A M. Emperor penguin optimizer: A comprehensive review based on state-of-the-art meta-heuristic algorithms. Alexandria Engineering Journal, 2023, 63:487-526.
[2] BENI G, WANG J. Swarm intelligence in cellular robotic systems. Robots and Biological Systems: Towards a New Bionics?: Proceedings of the 1989 NATO Advanced Workshop on Robots and Biological Systems, 1989, Jun 26-30, Toscana, Italy. NATO ASI Series 102. Berlin, Germany: Springer, 1993: 703-712.
[3] LYNN N, SUGANTHAN P N. Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm and Evolutionary Computation, 2015, 24: 11-24.
[4] KENNEDY J, EBERHART R. Particle swarm optimization. Proceedings of the 1995 International Conference on Neural Networks (ICNN’95): Vol.4, 1995, Nov 27- Dec 1, Perth, Australia. Piscataway, NJ, USA: IEEE, 1995: 1942-1948.
[5] GANDOMI A H, YANG X S, TALATAHARI S, et al. Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 2013, 18(1) :89-98.
[6] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer. Advances in Engineering Software, 2014, 69: 46-61.
[7] MIRJALILI S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 2015, 89: 228-249.
[8] CHOPRA N, MOHSIN A M. Golden jackal optimization: A novel nature-inspired optimizer for engineering applications. Expert Systems with Applications, 2022, 198: Article 116924.
[9] WANG J B, YANG B, CHEN Y J, et al. Novel phasianidae inspired peafowl (Pavo muticus/cristatus) optimization algorithm: Design, evaluation, and SOFC models parameter estimation. Sustainable Energy Technologies and Assessments, 2022, 50: Article 101825.
[10] XUE J K, SHEN B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. The Journal of Supercomputing, 2023, 79: 7305-7336.
[11] ABDEL-BASSET M, MOHAMED R, JAMEEL M, et al. Spider wasp optimizer: a novel meta-heuristic optimization algorithm. Artificial Intelligence Review, 2023, 56: 11675-11738.
[12] WANG L Y, CAO Q J, ZHANG Z X, et al. Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 2022, 114: Article 105082.
[13] CAMP M J, RACHLOW J L, SHIPLEY L A, et al. Grazing in sagebrush rangelands in western North America: Implications for habitat quality for a sagebrush specialist, the pygmy rabbit. The Rangeland Journal, 2014, 36 (2): 151-159.
[14] WOLPERT D H, MACREADY W G. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82.
[15] LI C H, LUO G C, QIN K, et al. An image encryption scheme based on chaotic tent map. Nonlinear Dynamics, 2017, 87: 127-133.
[16] FARAMARZI A, HEIDARINEJAD M, MIRJALILI S, et al. Marine predators algorithm: A nature-inspired metaheuristic. Expert Systems with Applications, 2020, 152: Article 113377.
[17] HEIDARI A A, MIRJALILI S, FARIS H, et al. Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 2019, 97: 849-872.
[18] ZHAO W G, WANG L Y, MIRJALILI S. Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Computer Methods in Applied Mechanics and Engineering, 2022, 388: Article 114194.
[19] RENAULT E. Improving swarming using genetic algorithms. Innovations in Systems and Software Engineering, 2020, 16: 143-159.
[20] MIRJALILI S, MIRJALILI S M, HATAMLOU A. Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 2016, 27(2): 495-513.
[21] JORDEHI A R, JASNI J. Parameter selection in particle swarm optimization: a survey. Journal of Experimental & Theoretical Artificial Intelligence, 2013, 25(4): 527-542.
[22] KATOCH S, CHAUHAN S S, KUMAR V. A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 2021, 80(5): 8091-8126.
[23] MIRJALILI S. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 2016, 27(4): 1053-1073. |