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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 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 ...
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 ...
He received a PhD in System and Information Engineering in 1992 from the Polytechnic University of Milan with a thesis titled Optimization, learning, and natural algorithms. [ 1 ] [ 2 ] He is the leading proponent of the ant colony optimization metaheuristic (see his book published by MIT Press in 2004), and one of the founders of the swarm ...
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 ...
Bacterial colony optimization; Barzilai-Borwein method; Basin-hopping; Benson's algorithm; Berndt–Hall–Hall–Hausman algorithm; Bin covering problem; Bin packing problem; Bland's rule; Branch and bound; Branch and cut; Branch and price; Bregman Lagrangian; Bregman method; Broyden–Fletcher–Goldfarb–Shanno algorithm
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.
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.