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Types of Optimizers
Publication pending.
F. Afshinmanesh, A. Marandi and A. Rahimi-Kian. A Novel Binary Particle Swarm Optimization Method Using Artificial Immune System. IEEE International Conference on Smart Technologies (2005).
H. Mühlenbein. The equation for response to selection and its use for prediction. Evolutionary Computation (1997).
P. Civicioglu. Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation (2013).
R. Storn. On the usage of differential evolution for function optimization. Proceedings of North American Fuzzy Information Processing (1996).
A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. Natural Computing Series (2013).
T. Bäck and H.–P. Schwefel. An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation (1993).
M. Ghaemi and M. Feizi-Derakhshi. Forest Optimization Algorithm. Expert Systems with Applications (2014).
M. Mitchell. An introduction to genetic algorithms. MIT Press (1998).
M. Omran and M. Mahdavi. Global-best harmony search. Applied Mathematics and Computation (2008).
Z. Guo, S. Wang, X. Yue and H. Yang. Global harmony search with generalized opposition-based learning. Soft Computing (2017).
J. Koza. Genetic programming: On the programming of computers by means of natural selection (1992).
A. Moraglio, K. Krawiec, and C. G. Johnson. Geometric semantic genetic programming. Lecture Notes in Computer Science (2012).
G. H. de Rosa, J. P. Papa, and L. P. Papa. Feature selection using geometric semantic genetic programming. Proceedings of the Genetic and Evolutionary Computation Conference Companion (2017).
Z. W. Geem, J. H. Kim, and G. V. Loganathan. A new heuristic optimization algorithm: Harmony search. Simulation (2001).
M. Mahdavi, M. Fesanghary, and E. Damangir. An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation (2007).
A. R. Mehrabian and C. Lucas. A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics (2006).
D. Zou, L. Gao, J. Wu and S. Li. Novel global harmony search algorithm for unconstrained problems. Neurocomputing (2010).
Q.-K. Pan, P. Suganthan, M. Tasgetiren and J. Liang. A self-adaptive global best harmony search algorithm for continuous optimization problems. Applied Mathematics and Computation (2010).
F. Merrikh-Bayat. The runner-root algorithm: A metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature. Applied Soft Computing (2015).
Y. Shi. Brain Storm Optimization Algorithm. International Conference in Swarm Intelligence (2011).
A. Kulkarni, G. Krishnasamy and A. Abraham. Cohort Intelligence: A Socio-inspired Optimization Method. Computational Intelligence and Complexity (2017).
A. Mortazavi, V. Toğan and A. Nuhoğlu. Interactive search algorithm: A new hybrid metaheuristic optimization algorithm. Engineering Applications of Artificial Intelligence (2018).
H. Bouchekara. Most Valuable Player Algorithm: a novel optimization algorithm inspired from sport. Operational Research (2017).
J. Zhang et al. Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling (2018).
A. Tharwat and T. Gabel. Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Computing and Applications (2019).
L. Abualigah et al. The Arithmetic Optimization Algorithm. Computer Methods in Applied Mechanics and Engineering (2021).
R. Y. Rubinstein. Optimization of Computer simulation Models with Rare Events. European Journal of Operations Research (1997).
F. Demir et al. A survival classification method for hepatocellular carcinoma patients with chaotic Darcy optimization method based feature selection. Medical Hypotheses (2020).
J. Bergstra and Y. Bengio. Random search for hyper-parameter optimization. Journal of machine learning research (2012).
S. Skiena. The Algorithm Design Manual (2010).
P. Godfrey, R. Shipley and J. Gryz. Algorithms and Analyses for Maximal Vector Computation. The VLDB Journal (2007).
W. Zhao, L. Wang and Z. Zhang. Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications (2019).
L. Abualigah et al. Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering (2021).
J. Pierezan and L. Coelho. Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems. IEEE Congress on Evolutionary Computation (CEC) (2018).
G. Dhiman and V. Kumar. Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems (2018).
C. Villaseñor et al. Germinal center optimization algorithm. International Journal of Computational Intelligence Systems (2018).
S. Mirjalili, S. Mirjalili and A. Lewis. Grey Wolf Optimizer. Advances in Engineering Software (2014).
A. Heidari et al. Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems (2019).
M. Yazdani and F. Jolai. Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm. Journal of Computational Design and Engineering (2016).
M. Jain, S. Maurya, A. Rani and V. Singh. Owl search algorithm: A novelnature-inspired heuristic paradigm for global optimization. Journal of Intelligent & Fuzzy Systems (2018).
A. Mohamed et al. Parasitism – Predation algorithm (PPA): A novel approach for feature selection. Ain Shams Engineering Journal (2020).
P. Savsani and V. Savsani. Passing vehicle search (PVS): A novel metaheuristic algorithm. Applied Mathematical Modelling (2016).
D. Polap and M. Woźniak. Red fox optimization algorithm. Expert Systems with Applications (2021).
P. Pijarski and P. Kacejko. A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG). Engineering Optimization (2019).
W. Zhao, L. Wang and Z. Zhang. A novel atom search optimization for dispersion coefficient estimation in groundwater. Future Generation Computer Systems (2019).
A. Hatamlou. Black hole: A new heuristic optimization approach for data clustering. Information Sciences (2013).
H. Abedinpourshotorban et al. Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation (2016).
A. Faramarzi et al. Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems (2020).
A. Tabari and A. Ahmad. A new optimization method: Electro-Search algorithm. Computers & Chemical Engineering (2017).
E. Rashedi, H. Nezamabadi-Pour and S. Saryazdi. GSA: a gravitational search algorithm. Information Sciences (2009).
F. Hashim et al. Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems (2019).
H. Shareef, A. Ibrahim and A. Mutlag. Lightning search algorithm. Applied Soft Computing (2015).
M.-H. Tayarani and M.-R. Akbarzadeh. Magnetic-inspired optimization algorithms: Operators and structures. Swarm and Evolutionary Computation (2014).
S. Mirjalili, S. M. Mirjalili and A. Hatamlou. Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications (2016).
A. Khachaturyan, S. Semenovsovskaya and B. Vainshtein. The thermodynamic approach to the structure analysis of crystals. Acta Crystallographica (1981).
A. Kaveh and A. Dadras. A novel meta-heuristic optimization algorithm: Thermal exchange optimization. Advances in Engineering Software (2017).
A. Kaveh. Tug of War Optimization. Advances in Metaheuristic Algorithms for Optimal Design of Structures (2016).
H. Eskandar. Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures (2012).
Z. Bayraktar et al. The wind driven optimization technique and its application in electromagnetics. IEEE transactions on antennas and propagation (2013).
A. Kaveh and T. Bakhshpoori. Water Evaporation Optimization: A novel physically inspired optimization algorithm. Computers & Structures (2016).
Y.-J. Zheng. Water wave optimization: A new nature-inspired metaheuristic. Computers & Operations Research (2015).
D. Karaboga and B. Basturk. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization (2007).
A. Nickabadi, M. M. Ebadzadeh and R. Safabakhsh. A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing (2011).
L. Cheng, W. Xue-han and Y. Wang. Artificial flora (AF) optimization algorithm. Applied Sciences (2018).
X.-S. Yang. A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (2010).
S. Arora and S. Singh. Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing (2019).
V. Hayyolalam and A. Kazem. Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence (2020).
X.-S. Yang and D. Suash. Cuckoo search via Lévy flights. World Congress on Nature & Biologically Inspired Computing (2009).
A. Askarzadeh. A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers & Structures (2016).
G.-G. Wang, S. Deb and L. Coelho. Elephant Herding Optimization. International Symposium on Computational and Business Intelligence (2015).
X.-S. Yang. Firefly algorithms for multimodal optimization. International symposium on stochastic algorithms (2009).
W.-T. Pan. A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems (2012).
X.-S. Yang. Flower pollination algorithm for global optimization. International conference on unconventional computing and natural computation (2012).
G. Azizyan et al. Flying Squirrel Optimizer (FSO): A novel SI-based optimization algorithm for engineering problems. Iranian Journal of Optimization (2019).
S. Saremi, S. Mirjalili and A. Lewis. Grasshopper Optimisation Algorithm: Theory and application. Advances in Engineering Software (2017).
J.-S. Chou and D.-N. Truong. A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation (2020).
A. Gandomi and A. Alavi. Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation (2012).
S. Mirjalili. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems (2015).
W. Zhao, Z. Zhang and L. Wang. Manta Ray Foraging Optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence (2020).
Publication pending.
H. Duan and P. Qiao. Pigeon-inspired optimization:a new swarm intelligence optimizerfor air robot path planning. International Journal of Intelligent Computing and Cybernetics (2014).
J. Kennedy, R. C. Eberhart and Y. Shi. Swarm intelligence. Artificial Intelligence (2001).
M. Roder, G. H. de Rosa, L. A. Passos, A. L. D. Rossi and J. P. Papa. Harnessing Particle Swarm Optimization Through Relativistic Velocity. IEEE Congress on Evolutionary Computation (2020).
H. Lu and W. Chen. Self-adaptive velocity particle swarm optimization for solving constrained optimization problems. Journal of global optimization (2008).
S. H. S. Moosavi and V. K. Bardsiri. Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence (2017).
S. Mirjalili. SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based Systems (2016).
S. Shadravan, H. Naji and V. Bardsiri. The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence (2019).
M.-Y. Cheng and D. Prayogo. Symbiotic Organisms Search: A new metaheuristic optimization algorithm. Computers & Structures (2014).
S. Mirjalili et al. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software (2017).
C. Bae et al. A new simplified swarm optimization (SSO) using exchange local search scheme. International Journal of Innovative Computing, Information and Control (2012).
G. Dhiman and A. Kaur. STOA: A bio-inspired based optimization algorithm for industrial engineering problems. Engineering Applications of Artificial Intelligence (2019).
W.-P. Yang. Vertical particle swarm optimization algorithm and its application in soft-sensor modeling. International Conference on Machine Learning and Cybernetics (2007).
S. Mirjalli and A. Lewis. The Whale Optimization Algorithm. Advances in Engineering Software (2016).
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