Hi Dr Sandeep I tink this document would be useful

http://www.econ.ubbcluj.ro/~rodica.lung/taco/literatura/Yang_nature_book_part.pdf

Contains : Genetic Algorithm, Ant, Bee, Swarm, Firefly, Bat and other Nature Inspired Algorithms.

Genetic Algorithms (for optimization problems)

Neural Networks (Regression, pattern Recognition)

ACO Ant Colony Optimization and its variants (optimization)

Diploid Genetic (Robustness)

Cellular Automata (AL)

http://www.econ.ubbcluj.ro/~rodica.lung/taco/literatura/Yang_nature_book_part.pdf

or you can go for latest survey work Soft Computing through Google refereral.

You can also check for the Grey Wolf Optimizer... a quite new nature-inspired heuristic.

Article Grey Wolf Optimizer

Nature Inspired Algorithms is a very active research area. This is because problems with which we are normally familiar are getting more and more complex due to size and other aspects, but also to new problems cropping up all the time on which existing methods are not effective. Nature seems to have been there and done it, so to speak. That is why we seem to get a lot of inspiration from it these days and in the foreseeable future. To answer your question, I would say that recent Nature Inspired algorithms include the Artificial Bee Colony Algorithm, the Firefly Algorithm, the Social Spider Algorithm, the Bat Algorithm, the Strawberry Algorithm, the Plant Propagation Algorithm, the Seed Based Plant Propagation Algorithm and many others. These are very effective compared to early Nature Inspired Algorithms such as the Genetic Algorithm, Simulated Annealing, Ant Colony and Swarm Optimisation and others on most optimisation/search problems. Moreover, some of them, such as the Plant Propagation Algorithm, have very few parameters that need arbitrary setting. 

Bird Swarm ,   2015

Krill Herd Swarm , 2012

Algorithm Based on the Self-defense Mechanisms of PlantsSwarm,  2015

Chicken Swarm, 2014

Offshore wind farm design with the Coral Reefs Optimization algorithm 2014

Flower pollination algorithm: a novel approach for multiobjective optimization 2014

plant root growth with self-similarity propagation 2015

you can refer Dr.Xin she yang's algorithms such as firefly algorithm, cuckoo search algorithm, flower pollination algorithm etc

As Prof. Abdel Salhi rightly said that Nature Inspired Algorithms is a very active research area. This is because  we can solve NP-hard  problems. Recent Nature Inspired algorithms include t Social Spider Algorithm, the Bat Algorithm, the Strawberry Algorithm, the Plant Propagation Algorithm, the Seed Based Plant Propagation Algorithm, Monkey Optimization etc.

The latest one is SOCIAL SPIDER ALGORITHM.

You can check all these algorithms in various latest issues of Evolutionary computations journals of standard publication. 

Check out this page. This guy is very good. He proposed more than 5 nature inspired algorithms and shares his code for research.

http://www.alimirjalili.com/Projects.html

Compiled a list of Nature Inspired Algorithms. There may be some more algorithm if any body know please suggest.

Wonderful List .

If we are not careful, the list will become longer and longer with little for the potential user to go on if he/she wanted to choose a suitable algorithm for their application. We need to take a step back and see how these algorithms differ from each other. My guess is that many are nothing more than a rehash of previous search/optimisation ideas.  This means that it is worth looking into this list and group the algorithms perhaps in terms of common basic ideas on which they are built, performance, required arbitrary parameters and other criteria. I also think that we should not be driven by the profusion of species and natural processes around us to create new algorithms. We should look for underlying principles. These, often, can be found in different species, processes and settings, because Nature is efficient; it will reuse good working principles.

Very true Mr. Abdel Salhi. Some algorithms are just a variant of existing popular algorithms. With few modifications they are named as new NIA.

You can considered the "quantum invasive weed optimization (QIWO)" algorithm.

Article An Improved Quantum Evolutionary Algorithm Based on Invasive...

I join the answer of my colleague, the metheurtisc algorithms are linked directly to the application, some new algorithm such as penguins each algorithms and quantum based approach, are not well applied to sveral problems, you can try all for a new problem.

I agree with Youcef. Let me just go back to the original question put by Dr Sandeep Kumar which was:

"Can any one suggest a complete list of latest Nature Inspired Algorithms or any source/website etc.?"

I think there is now a list compiled by serious scholars on optimization. Believe me, it does not support the proliferation of new methods without the required scientific rigor. It is not a hall of fame of new algorithms. It is more of a derisory listing making fun of the lack of scientific rigor in many papers that are out there. I have previously warned that we should look at what is out there already and make sure that we do not replicate the work of others and that what we propose is on a solid basis and has as much as possible a mathematical justification. We also need to conduct comparisons which are meaningful, reproducible and as comprehensive as possible. We all know that there is pressure on colleagues to publish and there are journals and conferences out there which are more that happy to accept papers which are not of the right quality provided the authors are prepared to pay for the privilege.  We need as a community of scientists to resist the temptation to put out new ideas when they are not necessary because they do not stand to scrutiny or they have already been put forward previously and elsewhere. Please have a look at this website which has come to my attention recently. 

https://github.com/fcampelo/EC-Bestiary

If we are not careful, we may throw this whole exciting area into disrepute. 

which algorithms are best practices now a days?

You could find them:

https://github.com/fcampelo/EC-Bestiary

Sanded, I would like to know if you are gathering source code of this evolutionary algorithms.

Although the list seems infinite as there are more not cover in Akinsunmade Akintayo Emmanuel's list. These include Afrian Buffalo Optimization (ABO) and Rock Hyrex Intelligent Optimization (RHIO). Infact, this could result to distraction according to Abdel Salhi. However, I'm of the opinion that research efforts should be geared towards application area, rather than developing new ones.

Is bird swarm optimization different from particle swarm optimization.

similarly,

Lion optimization algorithm from Ant lion optimization?

Dear @Ranpreet Kaur,

yes, these algorithms are different in both occasions. However, in Nature Inspired Algorithms many schemes have a lot similarities. These similarities have nothing to do with the name. Usually, different names means that there are different algorithms.

Alexandros is right. There is a lot of similarity between these algorithms. It is a shame really that scholars embark upon devising new algorithms before checking what is out there already. This has resulted in algorithms which are very similar, when they are not exactly the same.

In cases like that of Bird Swarm Optimization, it is really confusing because birds do not swarm, they flock! The use of Birds and Swarm together in naming a new algorithm implies that we are talking about some hybrid which involves Swarm Optimization. Apparently not.

As I said in a previous message, if we are not careful the reputation of all those involved in the topic of Nature-Inspired heuristics will be adversely affected. Professional scholarship dictates that we first check the literature before we put anything out there that we call new.

I do not recommend to attempt to list out metaheuristics according to specific names and terminologies, as most recent "nature-inspired" methods rely on artistic/fancy terminologies but boil down to a few core optimization concepts which are already known. I recommend to read this excellent position paper first:

Article Metaheuristics -- the metaphor exposed

As well as some recent surveys that analyse the core success factors of recent metaheuristics:

Article Metaheuristics in Combinatorial Optimization: Overview and C...

Article Heuristics for Multi-Attribute Vehicle Routing Problems : A ...

As a community, we should orient our effort on an analysis and unification of optimization methods under simple technical names, rather than wasting time, funding (and reputation) by inventing new names and creating micro-societies dedicated to cats, dogs and predating water drops.

I have performed a comprehensive survey on metaheuristics. It is published in Artificial Intelligence Review. It includes large list of algorithms.

Hussain, K., Salleh, M. N. M., Cheng, S., & Shi, Y. (2018). Metaheuristic research: a comprehensive survey. Artificial Intelligence Review, 1-43.

Whale optimization algorithm

Salp optimization algorithm

Transgenetic Algorithms

Fantastic algorithms

Grasshopper optimization Algorithm

They are many check the HHO

Also you can check the social engineering optimization.

There are many suggested algorithms here, but there is no single justification why it supposed to be better than the others?

In my case, I want to integrate a simple but effective optimization algorithm with my fuzzy network to select the most relevant fuzzy rules from a set of rules and remove the less relevant rules. So could someone help me please by suggesting a suitable algorithm with good justification? is the source code of the suggested algorithm is available?

Thanks.

Hi All,

One of the recent Nature Inspired algorithms is

Crow Search Algorithm (CSA) introduced in 2016

Alireza A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169, 1–12.

A long list with references to the literature can be found at the following link:

https://github.com/fcampelo/EC-Bestiary

find the paper

A Survey on Nature-Inspired Optimization Algorithms and Their Application in Image Enhancement Domain

Dear Dr. Mohammed

I hope you are doing well. Here you can found many metaherustics optimization algorithm https://en.wikipedia.org/wiki/List_of_metaphor-based_metaheuristics

if you would like using Cuttlefish algorithm may I can help how using this algorithm in your work. Best regards.

Thanks all for the helpful information

You can find a list of some natural inspired algorithms at this link:

https://arxiv.org/pdf/1307.4186.pdf

and more recent:

https://link.springer.com/book/10.1007%2F978-981-10-5221-7#about

1- Elephant swarm water search algorithm

2- Firefly-BAT (FFBAT) optimized

Few more to add: whale optimization,butterfly optimization, Salp swarm optimization, spider optimization algorithm, Socio spider monkey optimization, Grey wolf optimization,Cuckoo search, Elephant herding optimization,

squirrel search, Grasshopper optimization algorithm

Generalized Ant Colony Optimizer (GACO)

Superb reply covering almost every algorithm time date by Prof. Sandeep Kumar.

Can anyone suggest me which multi-objective optimization algorithm that suitable to calculate the 3 types of pH values, 3 temperature values and 3 time in hours for exposure the bacteria to adhere to the caco2 cell?

Please suggest me the optimization algorithm & give any related article or coding to refer. Thank you in advance!

I think mirjalli papers may be useful for your work

Dragonfly algorithm

i would like to know about the latest algorithm to optimize single-reservoir operation.

please suggest me the newest & best one

thanks.

Human mental search (HMS) algorithm

Try Crow Search Algorithm

Antlion Optimization.

Falcon Optimization

Evaporation rate based Water Cycle Algorithm

Grey Wolf Optimization

Teaching Learning Based Optimization

Generalized Ant Colony Optimizer (GACO)

Whale Optimization Algorithm is best

how many of these are real value adds when you solve real world NP-hard problems in the industry ? Most of these new algorithms only provide distractions from the solution of really challenging and truly important problems in optimization. New algorithms should be developed only if they are truly novel ideas that solve challenging problems that are not solved by existing algorithms and methods. To my mind, if you put enough variations in PSO and ACO. Most of the problems are solved.

Also, my experience says that showcasing convergence results on a subset of 100 well known benchmark functions is just a rat race for publications. This is a very bad practice that must be discouraged. Only a few publications are truly novel ones that solve real-world problems.

Flexible PSO algorithm

Artificial Bee Colony (ABC)

Gene Expression Programming (GEP)

Preference Selection Index (PSI)

Genetic algorithm, ant colony optimization, particle swarm optimization, simulated annealing, artificial bee colony

Hi to everyone,

throughout my PhD studies, I collected the majority of Nature-Inspired Algorithms and built a list with some info. This list is available in: https://data.mendeley.com/datasets/xfnzd2c8v7/1

and is described in detail in an article in Data in Brief Journal:

Article A comprehensive database of Nature-Inspired Algorithms

I update this database in monthly basis. However, before sending me a message, note that in our studies (mine and my colleagues') Nature-Inspired Algorithm are considered the methods that meet the definition given by

Chapter The Rationale Behind Seeking Inspiration from Nature

“The term nature refers to any part of the physical universe which is not a product of intentional human design”.

Feel free to cite this database, and also use the info contained in it. In the future, I will try to collect also the rest of meta-heuristics, i.e. sports-based methods, social-based methods etc.

Honey-Bees Mating Optimization (HBMO) Algorithm

Harris hawks optimization (HHO)

Dear Alexandros Tzanetos ,

I have a problem with opening your excel file. Could you please send it again to me?

[email protected]

Dear Alexandros Tzanetos,

You have done great work.

Dear Nasrin Fathollahzadeh Attar ,

it is a csv file, which can be opened with any spreadsheet software (LibreOffice Calc, Microsoft Excel, etc.)

Try opening the file with any of these software, or else send me a personal message to tell me if there is any other format that may be useful for you.

Dear Sandeep Kumar ,

thank you for your kind words! Please, feel free to use it and cite it in your research.

Slim mould algorithm (SMA)

Smart Metaheuristic algorithm

https://link.springer.com/article/10.1007/s00366-020-00951-x

Not easy

90% coverage is excellent

Chimp Optimization Algorithm

HHO, SMA, MPA

Another recent one is Capuchin Search Algorithm

More recent is

Border Collie Optimization

one of the fastest and high-quality papers is SMA, it has a high potential for publication in best level journals:

Article Slime mould algorithm: A new method for stochastic optimization

Code Matlab code of Slime Mould Algorithm (SMA)

Please find the latest database of nature inspired algortihms in this follwoing article:

Article A comprehensive database of Nature-Inspired Algorithms

Dear Muhammad Nabeel ,

thank you for sharing my work here!

For anyone interested, I have already mentioned this database in a post response above. By recommending that, it will be listed in Popular Answers and the interested reader will get the chance to see that first.

Rat Swarm Optimizer

Sandeep Kumar ,

You can get it from here: https://github.com/thieu1995/mealpy

Harris Hawks Optimizer

Article Harris hawks optimization: Algorithm and applications

Gradient-based optimizer

Archimedes optimization algorithm

https://www.researchgate.net/publication/344461357_Archimedes_optimization_algorithm_a_new_metaheuristic_algorithm_for_solving_optimization_problems

Please find the updated taxonomy (till 2020). A comprehensive survey of nature-inspired algorithms.

Article Nature-inspired algorithms for Wireless Sensor Networks: A c...

Coronavirus Optimization Algorithm:

Article Coronavirus Optimization Algorithm: A Bioinspired Metaheuris...