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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.
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.
Its steps are given in Algorithm 4. Often successive neighborhoods will be nested. Observe that point x' is generated at random in Step 4 in order to avoid cycling, which might occur if a deterministic rule were applied. In Step 5, the best improvement local search (Algorithm 1) is usually adopted.
1988: First conference on genetic algorithms is organized at the University of Illinois at Urbana-Champaign. 1988: Koza registers his first patent on genetic programming. [20] [21] 1989: Goldberg publishes a well known book on genetic algorithms. [22] 1989: Evolver, the first optimization software using the genetic algorithm.
The greedy randomized adaptive search procedure (also known as GRASP) is a metaheuristic algorithm commonly applied to combinatorial optimization problems. GRASP typically consists of iterations made up from successive constructions of a greedy randomized solution and subsequent iterative improvements of it through a local search. [1]
Parallel metaheuristic is a class of techniques that are capable of reducing both the numerical effort [clarification needed] and the run time of a metaheuristic. To this end, concepts and technologies from the field of parallelism in computer science are used to enhance and even completely modify the behavior of existing metaheuristics.
LEMON also contains some metaheuristic optimization tools and provides a general high-level interface for several LP and MIP solvers, such as GLPK, ILOG CPLEX, CLP, CBC, SoPlex. LEMON has its own graph storing format, the so called Lemon Graph Format and includes general EPS drawing methods and special graph exporting tools.