The present article helps the USA, the UK, Europe and the Australian students pursuing their computer Science postgraduate degree to identify right topic in the area of computer science specifically on Grey Wolf Optimizer, Artificial Intelligence (AI), Nature-Inspired Algorithm, Optimization Algorithm, Global optimisation. These topics are researched in-depth at the University of Spain, Cornell University, University of Modena and Reggio Emilia, Modena, Italy, and many more. PhD Assistance offers UK Dissertation Research Topics Services in Computer Science Engineering Domain. When you Order Computer Science Dissertation Services at PhD Assistance, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts. To Learn More :https://bit.ly/2LPAWKS Contact Us: UK NO: +44-1143520021 India No: +91-8754446690 Email: info@phdassistance.com Website Visit : https://www.phdassistance.com/ https://www.phdassistance.com/uk/ https://phdassistance.com/academy/ https://research.phdassistance.com/

1. Research paper
Dissertation Algorithm Development Tips for Developing
ANT-LION OPTIMIZER ALGORITHM
Where and how it is used in the micro grid-connected system?
Dissertation Algorithm Development Ideas from Existing Recent Research
Gaps in Computer Science Engineering Domain
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2. 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.
Ant-Lion Optimizer Algorithm (ALO) is one of Nature-Inspired
Optimization Algorithm.
ALO is used to solve complicated optimisation problems in
engineering design particularly in electrical engineering.
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SHORT NOTES
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A nature-inspired algorithm called ALO.
ALO algorithm imitates nature's ant lion hunting method.
Based on the ant lion nature behaviour, the Ant lion
optimizer algorithm is designed.
ALO algorithm is employed to solve classical engineering
challenges.
ALO identifies superior optimal designs, can also solve
restricted problems with different search spaces.
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Background
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4. Ant-Lion Behaviour (Doodlebugs)
Antlions (doodlebugs) pertain to the family of Myrmeleontidae and Net-winged insects.
Insects are not immediately caught and the insects attempt to evade the trap.
In this situation, to slip the prey into the bottom of the pit, antlions smartly throw sands to the bottom of the hole.
The trap size differs depending on the hunger level of ant lion or moon shape.
Antlions strive to dig bigger traps if they are in more hungry or during full moon.
It was also found that an antlion may not logically deduce the moon's shape to determine the trap's size, but it has
an inner lunar clock to make these decisions
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5. Ant-Lion Preying Behavior
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1. The random selection of antlions utilises a roulette wheel to discover the search space.
2. Random walks of ants across the antlions also underscore the discovery of the search space.
3. Using a random walk and roulette wheel, the ALO algorithm help to fix local deflation optima.
4. The ALO algorithm uses a search agent population to estimate the maximum global, so local optimum avoidance is extremely high.
5. Estimating random walks for each ant and dimension creates a variety of movement behaviours for ants within the trap of antlions and
preserves the range of place examined around antlions.
6. Optimized shrinking method is used to establish random walk borders which facilitate manipulation as the response to increases Iteration.
7. During iterations, ants ' movement frequency is adjustably reduced, which ensures consistency of the ALO algorithm.
8. Successful search space regions are saved when antlions migrate throughout optimization to the location of the good ants.
9. In every iteration, the finest antlion is rescued and regarded the oligarchy, so that all ants also appear to the optimal solution.
10. The ALO algorithm has very few parameters to adjust, so it is a flexible algorithm to solve various problems.
11. The ALO algorithm is a gradient-free algorithm that treats issues as a black box, so it can easily be used to solve real problems.
Ant-Lion Optimizer Algorithm (ALO)
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7. Ant lion optimizer algorithm's initial inspiration is the scavenging action of the larvae stage of the antlion.
Algorithm is designed based on the antlion behaviour and preying nature.
ALO identifies superior optimal designs and can also solve limited search space problems.
Micro grid-connected system can be quickly integrated and more power can be distributed.
ALO can work in High levels of exploitation and convergence.
ALO algorithm solves classical engineering issues and real Computational Fluid Dynamics (CFD) issue.
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CONCLUSION
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8. Can be used in Hyperspectral imaging (HSI) technique
Can be used in agricultural credit classification
Can be used in optimal fractional order design of a proportional– integral–derivative
controller
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Future scopes
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