Research paper
DISSERTATION ALGORITHM
DEVELOPMENT TIPS FOR DEVELOPING
GREY WOLF OPTIMIZATION ALGORITHM
Where and how it is Solve Combined
Economic Emission Dispatch Problems?
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SHORT NOTES
You will find the best dissertation research areas / topics for future researchers enrolled in
Engineering.
In order to identify the future research topics, we have reviewed the Engineering literature (recent
peer-reviewed studies) on the AI in optimization problems.
Nature-Inspired Optimization Algorithm is the recent trend in Artificial Intelligence.
Grey Wolf Optimizer (GWO) is one of Nature-Inspired Optimization Algorithm.
Grey Wolf Optimizer (GWO) is inspired by grey wolves called Canis lupus
Grey Wolf Optimizer is used to solve complicated aircraft problems like weapon target assignment
problem (WTA) and also used to solve combined economic emission dispatch problems.
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BACKGROUND
Over the couple of decades, meta-heuristic optimization
strategies have become very prevalent, particularly in
engineering applications.
The developing algorithm is easy to implement, they are simple
in concept, the gradient data is not necessary, local optima can
circumvent them, they can be used in a wide variety of
disciplinary issues.
Grey wolf optimization is one of the popular meta-heuristic
optimization algorithms.
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GREY WOLF (CANIS LUPUS)
BEHAVIOUR
Grey wolf (Canis lupus) is a member of the Canida
Grey wolves are regarded as predators of the apex, indicating
that they are at the top of the pyramid (food chain).
The grey lifestyle is that the grey wolf has a very strict social
dominance hierarchy, as shown in figure.
Figure: Hierarchy of Grey wolf
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FRIST LEVEL OF HIERARCHY OF
GREY WOLVES
Alpha and they are group leaders. Alphas can be male and female and they are the
decision-makers.
The decisions made by the alpha are communicated to the pack.
The alpha wolf is also known as the dominant wolf, as the pack must obey his / her orders.
Alpha wolves are only permitted to mate in the pack.
It reveals that a pack's organization and discipline is more important than its strength.
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SECOND LEVEL OF HIERARCHY OF
GREY WOLVES
In the hierarchy of grey wolves, the second level is Beta.
The beta wolf could be female or male, and if in case one of the alpha wolves becomes very
old or passes away, he/she is possibly the best choice to be the alpha.
The beta wolf is supposed to honour the alpha, but can also order the other lower-level
wolves.
The beta confirms the alpha orders throughout the pack and provides input to the alpha.
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THIRD LEVEL OF HIERARCHY
OF GREY WOLVES
The lowest grey wolf ranking is Omega. The omega takes a scapegoat's role.
All other dominant wolves can comment on the omega wolves.
It might seem that the omega is not an essential animal in the pack, but it has been noted that in
scenario of losing the omega, the entire pack will face the internal fight and few more issues.
It helps to please the whole pack and preserve the system of dominance.
The omega is also the nannies in the family in certain situations.
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GREY WOLF ATTACK
TOWARDS THE PREY
The delta wolves must report to alphas and betas.
Sentinels ensure the pack's security and guarantee.
Elders are the old skilled alpha or beta wolves.
Hunters assist the alphas and betas during prey hunting and supplying the pack with food.
The interesting behaviour of grey wolves is group hunting.
The predominant stages of grey wolf hunting are tracking, chasing, and reaching the prey.
The victim will be hunted, encircled and threatened until it stops moving.
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GREY WOLF
OPTIMIZER
ALGORITHM
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CONCLUSION
The hunting strategy and the social hierarchy of grey wolves have
been mathematically modelled and developed as an algorithm for
Grey Wolf Optimiser.
It is efficient and powerful algorithm to solve problems of continuous
optimization as well as real-life optimization.
GWO is able to solve complex engineering problems with high
efficiency and superior performance.
It is used in aircraft engineering to overcome weapon target
assignment problems and also used to solve combined economic
emission dispatch problems.
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FUTURE SCOPES
Can be used in optimal positioning and sizing multiple active
power filters in the radial distribution system
Can be used as a bridging tool
Can be used as an adaptive neuro-fuzzy inference system
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