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Bat Algorithm BA Nature-inspired Bio-inspired 2010 [29] Charged System Search CSS Nature-inspired Physics/Chemistry-based 2010 [30] Eagle Strategy ES Nature-inspired 2010 Fireworks Algorithm FWA 2010 [31] Cuckoo Optimization Algorithm COA Nature-inspired Bio-inspired 2011 [32] Stochastic Diffusion Search SDS 2011 Teaching-Learning-Based ...
The ant colony optimization algorithm is a probabilistic technique for solving computational problems that can be reduced to finding good paths through graphs.Initially proposed by Marco Dorigo in 1992 in his PhD thesis, [1] [2] the first algorithm aimed to search for an optimal path in a graph based on the behavior of ants seeking a path between their colony and a source of food.
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity.
In the ant colony optimization algorithms, an artificial ant is a simple computational agent that searches for good solutions to a given optimization problem. To apply an ant colony algorithm, the optimization problem needs to be converted into the problem of finding the shortest path on a weighted graph. In the first step of each iteration ...
A minimum spanning tree of a weighted planar graph.Finding a minimum spanning tree is a common problem involving combinatorial optimization. Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, [1] where the set of feasible solutions is discrete or can be reduced to a discrete set.
These algorithms run online and repeatedly determine values for decision variables, such as choke openings in a process plant, by iteratively solving a mathematical optimization problem including constraints and a model of the system to be controlled.
The actor-critic algorithm (AC) is a family of reinforcement learning (RL) algorithms that combine policy-based RL algorithms such as policy gradient methods, and value-based RL algorithms such as value iteration, Q-learning, SARSA, and TD learning.
An Overview of Evolutionary Algorithms in Multiobjective Optimization, with Carlos M. Fonseca, Evolutionary Computation, MIT Press, 1995. [ 2 ] He is a Fellow of the Royal Academy of Engineering since 2005, [ 3 ] a Fellow of the International Federation of Automatic Control since 2009, a Fellow of the Institution of Engineering Technology, and ...