Nature has inspired many researchers in many ways and thus is a rich source of inspiration. Now, the majority of the new optimization algorithms are nature-inspired, because they have been developed by drawing inspiration from nature. Even with the emphasis on the source of inspiration, we can still have different levels of classifications, depending on how details and how many subsources we will wish to use.
Based on the highest level sources, we can divide all existing algorithms into three major categories: bio-inspired, physics based, and others.
Example of some methods
- The genetic algorithms (GAs) was developed in 1996, were developed based on the Darwinian principle of the ‘survival of the fittest’ and the natural process of evolution through reproduction.
- The artificial bee colony algorithm (ABC) was developed in 2009, is inspired by honeybees’ food-searching sources behavior . These analogical models are also useful for describing the EAs to researchers familiar with the widely known concepts.
- Bat algorithm is a metaheuristic optimization algorithm developed by Xin-She Yang in 2010.This bat algorithm is based on the echolocation behaviour of microbats with varying pulse rates of emission and loudness.
The particle swarm optimization algorithm (PSO) was developed by Kennedy and Eberhart, the PSO simulates the choreographic movements of superorganisms such as flocks of birds and schools of fish moving in concert.
- The firefly algorithm (FA) is a nature inspired algorithms which is based on the flashing light of fireflies. The FA was developed in 1996.
-The biogeography-based optimization algorithm models the bio-interactions among living creatures in a habitat.
-The cuckoo search algorithm models parasitic bio-interactions in living creatures.
-The harmony search meta-heuristic algorithm (2006) which is based on natural musical performance process of searching for a perfect state of harmony such as during jazz improvisation.
First thing seems to be a new and attractive name. It already happened few times that some the so called nature (bio-)inspired algorithms were shown to be equivalent to others.
First thing seems to be a new and attractive name. It already happened few times that some the so called nature (bio-)inspired algorithms were shown to be equivalent to others.
I think first of all we should know why we are looking for a new algorithm??? What are the limitations you try to overcome by developing a new nature inspired algorithm?? Secondly, your algorithm should be robust. I would be good if you eliminate the "random" part involved in other nature inspired algorithms.
Kindly, go through the following publication.
Y. del Valle, G. Venayagamoorthy, S. Mohagheghi, J.-C. Hernandez, R. Harley, Particle swarm optimization: Basic concepts, variants and applications in power systems, IEEE Transactions on Evolutionary Computation, 12 (2) (2008) 171-195.
I hope it will be very helpful to you.
Regards,
M.N.Alam
https://www.researchgate.net/publication/296636431_Codes_in_MATLAB_for_Particle_Swarm_Optimization
Code Codes in MATLAB for Particle Swarm Optimization
https://www.researchgate.net/publication/297245624_Particle_Swarm_Optimization_Algorithm_and_its_Codes_in_MATLAB
Research Particle Swarm Optimization: Algorithm and its Codes in MATLAB
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