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2004, Dorigo and Stützle publish the Ant Colony Optimization book with MIT Press [133] 2004, Zlochin and Dorigo show that some algorithms are equivalent to the stochastic gradient descent, the cross-entropy method and algorithms to estimate distribution [8] 2005, first applications to protein folding problems.
Many optimization algorithms need to start from a feasible point. ... Algorithms for Optimization, The MIT Press, ISBN 978-0-26203942-0, (2019).
This is a chronological table of metaheuristic algorithms that only contains fundamental computational intelligence algorithms. Hybrid algorithms and multi-objective algorithms are not listed in the table below.
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
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 mathematical optimization, Dantzig's simplex algorithm (or simplex method) ... Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001.
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.
It is an algorithm design paradigm for discrete and combinatorial optimization problems, as well as mathematical optimization. A branch-and-bound algorithm consists of a systematic enumeration of candidate solutions by means of state space search: the set of candidate solutions is thought of as forming a rooted tree with the