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The square brackets $[]$ in $[\tau_{ij}]^\alpha$ and $[\eta_{ij}]^\beta$ may be just a way of emphasing that the elements $\tau_{ij} \in \mathbb{R}$ and $\eta_{ij} \in \mathbb{R}$ of respectively the matrices $\mathbf{\tau} \in \mathbb{R}^{n \times n}$ and $\mathbf{\eta} \in \mathbb{R}^{n \times n}$ (where $n$ is the number of nodes in the graph) are ...


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Swarm intelligence (SI) is a sub-field of or an approach to artificial intelligence (AI), where you have multiple individuals (for example, artificial ants), which collectively can produce what we (or most of us) would intuitively call intelligent behaviour. SI is sometimes categorized as a sub-field of evolutionary computation (which also includes ...


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I wrote some python code to reproduce this paper's purported results. My code very efficiently optimizes simple smooth functions like bowls, but does not come close to reproducing the paper's claimed results on more complex functions, including with the parameters the authors report. I think that, since both @Jairo and I were unable to reproduce the results ...


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Artificial Intelligence, as its name suggests, is intelligence made by humans. It's usually thought of as having human-like behaviors and characteristics. However, it doesn't have to resemble humans to be AI. It just has to be made by humans. Many common AI algorithms aren't even made to resemble humans, they may just have similarities. Reinforcement ...


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Well, one of the simpler definitions for SI sounds like this: The emergent collective intelligence of groups of simple agents.” (Bonabeau et al, 1999) So, in order to get to the SI you have to use some kind of algorithms/AI to get simple intelligent agents. It's just cooperative intelligence, or cooperative AI if you wish. SI just uses today's AI/ML ...


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Let's start by making clear that both AS and MMAS use only global pheromone update. Now, the MMAS has two main differences regarding AS: In AS, all ants that completed a solution are used for the update, while in MMAS only the best ant with a complete solution is used for update (as you had pointed out). In AS, the pheromone values are not explicitly ...


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The first ant colony optimisation algorithm was introduced by Marco Dorigo in the report Positive Feedback as a Search Strategy (1991) and his PhD thesis Optimization, Learning and Natural Algorithms (1992). He's still one of the leading figures in the field of swarm intelligence (having also written or co-written several papers and books). Another important ...


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Particle Swarm Optimization can be used to optimize a neural network with more than one hidden layer. Instead of optimizing a single weight matrix, and two bias vectors, you are just optimizing more of them. However, PSO is not often used for larger neural networks, because, particle swarm optimization is not all that efficient at working with a large amount ...


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No. In general, you can't find a tight bound for evolutionary algorithms, and it is one of the main difference of these algorithms with the classical algorithms. You should notice that it does not mean you can't find when the evolutionary algorithms are finished! But, you can't find a tight bound for the algorithms time complexity to reach to the optimal ...


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A continous domain can be imagined as a space in which the axes of the coordinate systems are the parameters of the continous domain. If we take 2D Cartesian space as an example, it is a continous domain where there is an infininite amount of possibilities of placing an object in this space. Let's call the position of the object its state. Constraining the ...


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Swarm intelligence, compound intelligence, or group intelligence may emerge as an important concept as AI develops toward higher complexity. Whether these terms should be considered synonymous is doubtful. Compound features in biology are the result of control in differentiation during the development of an organism from a single cell. Compounding in ...


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The biggest issue here may be similarity to prior work. As for the benchmarks, benchmarks are a common means for comparing algorithms. What it means here would be to compare the end-result (your chosen goal) for each of the algorithms compared in a similar scenario, or a generated test-scenario. This will mean that you will have to utilize all chosen ...


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I would assume that in this case, they are asking you to compare the performance of your particular algorithm to other similar algorithms used in pathfinding. For example, A*


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In my answer, I have often switched between AGI and ASI for reference. This is fine as an AGI will reach ASI as it is optimizing itself and learning. I think it is not only important by necessary that AGI and ASI are of collaborative nature. Nick Bostrom, in his book Superintelligence: Paths, Dangers, Strategies in Chapter 10 described three ways in which an ...


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