Nature inspired optimization algorithms or simply variations of metaheuristics?
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[1] Anupam Yadav,et al. AEFA: Artificial electric field algorithm for global optimization , 2019, Swarm Evol. Comput..
[2] Ali Kaveh,et al. Lion Pride Optimization Algorithm: A meta-heuristic method for global optimization problems , 2018, Scientia Iranica.
[3] Ali Kaveh,et al. CYCLICAL PARTHENOGENESIS ALGORITHM: A NEW META-HEURISTIC ALGORITHM , 2017 .
[4] John E. Beasley,et al. OR-Library: Distributing Test Problems by Electronic Mail , 1990 .
[5] Alberto Tonda. Inspyred: Bio-inspired algorithms in Python , 2019, Genetic Programming and Evolvable Machines.
[6] Xiaoli Zhang,et al. An ACO-based algorithm for parameter optimization of support vector machines , 2010, Expert Syst. Appl..
[7] Bilal Alatas,et al. Sports inspired computational intelligence algorithms for global optimization , 2019, Artificial Intelligence Review.
[8] Xiaodong Li,et al. Swarm Intelligence in Optimization , 2008, Swarm Intelligence.
[9] Francesc Comellas,et al. Bumblebees: a multiagent combinatorial optimization algorithm inspired by social insect behaviour , 2009, GEC '09.
[10] Simon Fong,et al. Elephant Search Algorithm for optimization problems , 2015, 2015 Tenth International Conference on Digital Information Management (ICDIM).
[11] Andrea Serani,et al. Dolphin Pod Optimization - A Nature-Inspired Deterministic Algorithm for Simulation-Based Design , 2017, MOD.
[12] Vijay Kumar,et al. Emperor penguin optimizer: A bio-inspired algorithm for engineering problems , 2018, Knowl. Based Syst..
[13] Seyedali Mirjalili,et al. Henry gas solubility optimization: A novel physics-based algorithm , 2019, Future Gener. Comput. Syst..
[14] Hossam Faris,et al. Optimizing connection weights in neural networks using the whale optimization algorithm , 2016, Soft Computing.
[15] Sadoullah Ebrahimnejad,et al. Emperor Penguins Colony: a new metaheuristic algorithm for optimization , 2019, Evolutionary Intelligence.
[16] Ajith Abraham,et al. Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications , 2009, Foundations of Computational Intelligence.
[17] Qiang Chen,et al. An ABC supported QoS multicast routing scheme based on beehive algorithm , 2008, QShine '08.
[18] Ali Ahrari,et al. Grenade Explosion Method - A novel tool for optimization of multimodal functions , 2010, Appl. Soft Comput..
[19] Goldberg,et al. Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.
[20] Vahideh Hayyolalam,et al. Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems , 2020, Eng. Appl. Artif. Intell..
[21] Marc Toussaint,et al. A No-Free-Lunch theorem for non-uniform distributions of target functions , 2004, J. Math. Model. Algorithms.
[22] Nils J. Nilsson,et al. Artificial Intelligence: A New Synthesis , 1997 .
[23] S. Ilker Birbil,et al. A Global Optimization Method for Solving Fuzzy Relation Equations , 2003, IFSA.
[24] Leandro dos Santos Coelho,et al. Cheetah Based Optimization Algorithm: A Novel Swarm Intelligence Paradigm , 2018, ESANN.
[25] Alexandros Tzanetos,et al. Nature Inspired Optimization Algorithms Related to Physical Phenomena and Laws of Science: A Survey , 2017, Int. J. Artif. Intell. Tools.
[26] Hussein A. Abbass,et al. MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).
[27] Muzaffar Eusuff,et al. Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .
[28] Ali Kaveh,et al. Colliding bodies optimization: A novel meta-heuristic method , 2014 .
[29] Pei-wei Tsai,et al. Cat Swarm Optimization , 2006, PRICAI.
[30] Ali Kaveh,et al. Natural Forest Regeneration Algorithm: A New Meta-Heuristic , 2016 .
[31] B. Rajakumar. The Lion's Algorithm: A New Nature-Inspired Search Algorithm , 2012 .
[32] Dan Boneh,et al. On genetic algorithms , 1995, COLT '95.
[33] Hong Yan,et al. Adaptive Cockroach Colony Optimization for Rod-Like Robot Navigation , 2015 .
[34] 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.
[35] D. Pham,et al. THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS , 2006 .
[36] Tibor Bosse,et al. Recent Trends in Applied Artificial Intelligence , 2013, Lecture Notes in Computer Science.
[37] Yu Liu,et al. A new bio-inspired optimisation algorithm: Bird Swarm Algorithm , 2016, J. Exp. Theor. Artif. Intell..
[38] Iztok Fister,et al. A comprehensive database of Nature-Inspired Algorithms , 2020, Data in brief.
[39] Omid Bozorg Haddad,et al. Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization , 2006 .
[40] Farookh Khadeer Hussain,et al. Support vector regression with chaos-based firefly algorithm for stock market price forecasting , 2013, Appl. Soft Comput..
[41] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[42] A. Mucherino,et al. Monkey search: a novel metaheuristic search for global optimization , 2007 .
[43] Lester James V. Miranda,et al. PySwarms: a research toolkit for Particle Swarm Optimization in Python , 2018, J. Open Source Softw..
[44] Ying Tan,et al. Fireworks Algorithm for Optimization , 2010, ICSI.
[45] John Mark Bishop,et al. The Stochastic Search Network , 1992 .
[46] Yu Liu,et al. A New Bio-inspired Algorithm: Chicken Swarm Optimization , 2014, ICSI.
[47] Reda Mohamed Hamou,et al. New Swarm Intelligence Technique of Artificial Social Cockroaches for Suspicious Person Detection Using N-Gram Pixel with Visual Result Mining , 2015, Int. J. Strateg. Decis. Sci..
[48] Javier Del Ser,et al. jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics , 2019, Swarm Evol. Comput..
[49] Andrew Lewis,et al. Grey Wolf Optimizer , 2014, Adv. Eng. Softw..
[50] Simon Fong,et al. Wolf search algorithm with ephemeral memory , 2012, Seventh International Conference on Digital Information Management (ICDIM 2012).
[51] Md Alauddin,et al. Mosquito flying optimization (MFO) , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).
[52] P. Deepa Shenoy,et al. Fault tolerant BeeHive routing in mobile ad-hoc multi-radio network , 2014, 2014 IEEE REGION 10 SYMPOSIUM.
[53] Marco Dorigo,et al. Distributed Optimization by Ant Colonies , 1992 .
[54] Kenneth Sörensen,et al. Metaheuristics - the metaphor exposed , 2015, Int. Trans. Oper. Res..
[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] Christian Blum,et al. Implementing a model of Japanese tree frogs' calling behavior in sensor networks: a study of possible improvements , 2011, GECCO '11.
[57] Alexandros Tzanetos,et al. A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies , 2020 .
[58] Petros Koumoutsakos,et al. Optimization based on bacterial chemotaxis , 2002, IEEE Trans. Evol. Comput..
[59] Asaf Varol,et al. A Novel Intelligent Optimization Algorithm Inspired from Circular Water Waves , 2015 .
[60] Q. Henry Wu,et al. A bacterial swarming algorithm for global optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.
[61] James M. Keller,et al. Roach Infestation Optimization , 2008, 2008 IEEE Swarm Intelligence Symposium.
[62] Hossam Faris,et al. Grasshopper optimization algorithm for multi-objective optimization problems , 2017, Applied Intelligence.
[63] Iztok Fister,et al. A new population-based nature-inspired algorithm every month : Is the current era coming to the end ? , 2016 .
[64] Shuai Li,et al. BAS: Beetle Antennae Search Algorithm for Optimization Problems , 2017, ArXiv.
[65] Hossein Nezamabadi-pour,et al. GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..
[66] Dušan Teodorović,et al. Bee Colony Optimization – a Cooperative Learning Approach to Complex Transportation Problems , 2005 .
[67] Magdalene Marinaki,et al. Bumble Bees Mating Optimization Algorithm for the Vehicle Routing Problem , 2014 .
[68] Grega Vrbancic,et al. NiaPy: Python microframework for building nature-inspired algorithms , 2018, J. Open Source Softw..
[69] Christian Blum,et al. Swarm Intelligence: Introduction and Applications , 2008, Swarm Intelligence.
[70] Ardeshir Bahreininejad,et al. Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems , 2012 .
[71] M. Osman Tokhi,et al. A novel adaptive spiral dynamic algorithm for global optimization , 2013, 2013 13th UK Workshop on Computational Intelligence (UKCI).
[72] Muhammad Arif,et al. MOX: A novel global optimization algorithm inspired from Oviposition site selection and egg hatching inhibition in mosquitoes , 2011, Appl. Soft Comput..
[73] MirjaliliSeyedali. Moth-flame optimization algorithm , 2015 .
[74] Janez Brest,et al. A Brief Review of Nature-Inspired Algorithms for Optimization , 2013, ArXiv.
[75] Jiang Jianjun,et al. A Dolphin Partner Optimization , 2009, 2009 WRI Global Congress on Intelligent Systems.
[76] A. Kaveh,et al. A novel heuristic optimization method: charged system search , 2010 .
[77] Andrew Lewis,et al. The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..
[78] Fariborz Jolai,et al. Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm , 2016, J. Comput. Des. Eng..
[79] Chen Zhaohui,et al. Cockroach swarm optimization for vehicle routing problems , 2011 .
[80] Xin-She Yang,et al. Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.
[81] Marco Dorigo,et al. Optimization, Learning and Natural Algorithms , 1992 .
[82] Harish Sharma,et al. Spider Monkey Optimization algorithm for numerical optimization , 2014, Memetic Computing.
[83] Seyed Mohammad Mirjalili,et al. Ions motion algorithm for solving optimization problems , 2015, Appl. Soft Comput..
[84] Qiang Liu,et al. Beetle Swarm Optimization Algorithm: Theory and Application , 2018, Filomat.
[85] Leandro dos Santos Coelho,et al. A Novel Metaheuristic Algorithm Inspired by Rhino Herd Behavior , 2018 .
[86] Kun Li,et al. Fault diagnosis for down-hole conditions of sucker rod pumping systems based on the FBH–SC method , 2015, Petroleum Science.
[87] S. Hr. Aghay Kaboli,et al. Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems , 2017, J. Comput. Sci..
[88] Lionel M. Ni,et al. Proceedings of the 5th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness , 2008 .
[89] Bo Wang,et al. Lion pride optimizer: An optimization algorithm inspired by lion pride behavior , 2012, Science China Information Sciences.
[90] P. Dhavachelvan,et al. A survey on nature inspired meta-heuristic algorithms with its domain specifications , 2016, 2016 International Conference on Communication and Electronics Systems (ICCES).
[91] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[92] Juan J. Flores,et al. Gravitational Interactions Optimization , 2011, LION.
[93] Ali Husseinzadeh Kashan,et al. League Championship Algorithm: A New Algorithm for Numerical Function Optimization , 2009, 2009 International Conference of Soft Computing and Pattern Recognition.
[94] Tzanetos Alexandros,et al. Nature Inspired Optimization Algorithms Related to Physical Phenomena and Laws of Science: A Survey , 2017 .
[95] Raymond Chiong,et al. Why Is Optimization Difficult? , 2009, Nature-Inspired Algorithms for Optimisation.
[96] Richard A. Formato,et al. CENTRAL FORCE OPTIMIZATION: A NEW META-HEURISTIC WITH APPLICATIONS IN APPLIED ELECTROMAGNETICS , 2007 .
[97] Belal Al-Khateeb,et al. The blue monkey: A new nature inspired metaheuristic optimization algorithm , 2019, Periodicals of Engineering and Natural Sciences (PEN).
[98] Shah-HosseiniHamed. Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation , 2011 .
[99] Yongquan Zhou,et al. A Novel Global Convergence Algorithm: Bee Collecting Pollen Algorithm , 2008, ICIC.
[100] S. Deb,et al. Elephant Herding Optimization , 2015, 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI).
[101] D. Wolpert,et al. No Free Lunch Theorems for Search , 1995 .
[102] Wansheng Tang,et al. Monkey Algorithm for Global Numerical Optimization , 2008 .
[103] Randal S. Olson,et al. PMLB: a large benchmark suite for machine learning evaluation and comparison , 2017, BioData Mining.
[104] J. Bishop. Stochastic searching networks , 1989 .
[105] H. R. E. H. Bouchekara,et al. Optimal power flow using black-hole-based optimization approach , 2014, Appl. Soft Comput..
[106] Ibrahim Aljarah,et al. EvoloPy-FS: An Open-Source Nature-Inspired Optimization Framework in Python for Feature Selection , 2019, Algorithms for Intelligent Systems.
[107] Guangjun Liao,et al. 2010 Second WRI Global Congress on Intelligent Systems , 2010 .
[108] Shu-Cherng Fang,et al. An Electromagnetism-like Mechanism for Global Optimization , 2003, J. Glob. Optim..
[109] Yue Zhang,et al. BeeHive: An Efficient Fault-Tolerant Routing Algorithm Inspired by Honey Bee Behavior , 2004, ANTS Workshop.
[110] Kevin M. Passino,et al. Biomimicry of bacterial foraging for distributed optimization and control , 2002 .
[111] Xin-She Yang,et al. A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.
[112] Nizamettin Aydin,et al. An application of black hole algorithm and decision tree for medical problem , 2015, 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE).
[113] Marc Parizeau,et al. DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..
[114] Yuanyang Zou,et al. The whirlpool algorithm based on physical phenomenon for solving optimization problems , 2019, Engineering Computations.
[115] Ali Kaveh,et al. Water Evaporation Optimization , 2016 .
[116] Hossam Faris,et al. Harris hawks optimization: Algorithm and applications , 2019, Future Gener. Comput. Syst..
[117] Mohammed Ali Tawfeeq. Intelligent Algorithm for Optimum Solutions Based on the Principles of Bat Sonar , 2012, ArXiv.
[118] Iztok Fister,et al. Adaptation and Hybridization in Nature-Inspired Algorithms , 2015 .
[119] Yujun Zheng. Water wave optimization: A new nature-inspired metaheuristic , 2015, Comput. Oper. Res..
[120] Seyed Mohammad Mirjalili,et al. The Ant Lion Optimizer , 2015, Adv. Eng. Softw..
[121] Seyed Mohammad Mirjalili,et al. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..
[122] Husheng Wu,et al. Wolf Pack Algorithm for Unconstrained Global Optimization , 2014 .
[123] Abdelhakim Ameur El Imrani,et al. Hurricane-based Optimization Algorithm , 2014 .
[124] Jianhua Yang,et al. Dolphin swarm algorithm , 2016, Frontiers of Information Technology & Electronic Engineering.
[125] Xin-She Yang,et al. Nature-Inspired Algorithms and Applied Optimization , 2018 .
[126] Shinq-Jen Wu,et al. A bio-inspired optimization for inferring interactive networks: Cockroach swarm evolution , 2015, Expert Syst. Appl..
[127] Nikos D. Lagaros,et al. Pity beetle algorithm - A new metaheuristic inspired by the behavior of bark beetles , 2018, Adv. Eng. Softw..
[128] Amir Hossein Alavi,et al. Krill herd: A new bio-inspired optimization algorithm , 2012 .
[129] Zhengxin Chen. Computational intelligence for decision support , 1999 .
[130] Ardeshir Bahreininejad,et al. Mine blast algorithm for optimization of truss structures with discrete variables , 2012 .
[131] Leandro dos Santos Coelho,et al. Meerkats-inspired Algorithm for Global Optimization Problems , 2018, ESANN.
[132] Abdolreza Hatamlou,et al. Heart: a novel optimization algorithm for cluster analysis , 2014, Progress in Artificial Intelligence.
[133] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[134] Andrew Wirth,et al. The Rationale Behind Seeking Inspiration from Nature , 2009, Nature-Inspired Algorithms for Optimisation.
[135] A. Kaveh,et al. A new optimization method: Dolphin echolocation , 2013, Adv. Eng. Softw..
[136] Feng Zou,et al. Optimal approximation of stable linear systems with a novel and efficient optimization algorithm , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).
[137] Ismael Rodríguez,et al. Using River Formation Dynamics to Design Heuristic Algorithms , 2007, UC.
[138] Leandro dos Santos Coelho,et al. Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).
[139] Michael O'Neill,et al. Grammatical Evolution: Evolving Programs for an Arbitrary Language , 1998, EuroGP.
[140] Hossam Faris,et al. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..
[141] Hamed Shah-Hosseini,et al. The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm , 2009, Int. J. Bio Inspired Comput..
[142] Hossam Faris,et al. EvoloPy: An Open-source Nature-inspired Optimization Framework in Python , 2016, IJCCI.
[143] A. Kaveh,et al. A novel meta-heuristic optimization algorithm: Thermal exchange optimization , 2017, Adv. Eng. Softw..
[144] 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.
[145] A. Kaveh,et al. A new meta-heuristic method: Ray Optimization , 2012 .
[146] Seyedali Mirjalili,et al. SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..
[147] Natalio Krasnogor,et al. Nature‐inspired cooperative strategies for optimization , 2009, Int. J. Intell. Syst..
[148] Dayang N. A. Jawawi,et al. Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm , 2016, Swarm Evol. Comput..
[149] Walmir M. Caminhas,et al. Bee colonies as model for multimodal continuous optimization: The OptBees algorithm , 2012, 2012 IEEE Congress on Evolutionary Computation.
[150] Ali Kaveh,et al. Artificial Coronary Circulation System; A new bio-inspired metaheuristic algorithm , 2019, Scientia Iranica.
[151] Erik Valdemar Cuevas Jiménez,et al. A swarm optimization algorithm inspired in the behavior of the social-spider , 2013, Expert Syst. Appl..
[152] Minghao Yin,et al. Animal migration optimization: an optimization algorithm inspired by animal migration behavior , 2014, Neural Computing and Applications.
[153] Ali Kaveh,et al. Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints , 2017 .
[154] Fevrier Valdez,et al. Modular Neural Networks architecture optimization with a new nature inspired method using a fuzzy combination of Particle Swarm Optimization and Genetic Algorithms , 2014, Information Sciences.
[155] Dervis Karaboga,et al. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..
[156] Abdelouahab Moussaoui,et al. Penguins Search Optimization Algorithm (PeSOA) , 2013, IEA/AIE.
[157] Xin-She Yang,et al. Mathematical Analysis of Nature-Inspired Algorithms , 2018 .
[158] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[159] Seyed Mohammad Mirjalili,et al. Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.
[160] Abdolreza Hatamlou,et al. Black hole: A new heuristic optimization approach for data clustering , 2013, Inf. Sci..
[161] Reza Akbari,et al. A novel bee swarm optimization algorithm for numerical function optimization , 2010 .
[162] Reza Moghdani,et al. Volleyball Premier League Algorithm , 2018, Appl. Soft Comput..
[163] Yong Wang,et al. A New Stochastic Optimization Approach - Dolphin Swarm Optimization Algorithm , 2016, Int. J. Comput. Intell. Appl..