In computer science and operations

In computer science and operations research, the ant colony
optimization algorithm (ACO) is a probabilistic technique for
solving computational problems which can be reduced to
finding good paths through graphs.
This algorithm is a member of the ant colony algorithms family,
in swarm intelligence methods, and it constitutes some
metaheuristic optimizations. Initially proposed by Marco
Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was
aiming 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. The original idea has since diversified to solve a
wider class of numerical problems, and as a result, several
problems have emerged, drawing on various aspects of the
behavior of ants
Ant colony optimization algorithms have been applied to many
combinatorial optimization problems, ranging from quadratic
assignment toprotein folding or routing vehicles and a lot of
derived methods have been adapted to dynamic problems in real
variables, stochastic problems, multi-targets and parallel
implementations. It has also been used to produce near-optimal
solutions to the travelling salesman problem.

In computer science and operations research, the ant colony 
optimization algorithm (ACO) is a probabilistic technique for 
solving computational problems which can be reduced to 
finding good paths through graphs. 
This algorithm is a member of the ant colony algorithms family, 
in swarm intelligence methods, and it constitutes some 
metaheuristic optimizations. Initially proposed by Marco 
Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was 
aiming 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. The original idea has since diversified to solve a 
wider class of numerical problems, and as a result, several 
problems have emerged, drawing on various aspects of the 
behavior of ants 
Ant colony optimization algorithms have been applied to many 
combinatorial optimization problems, ranging from quadratic 
assignment toprotein folding or routing vehicles and a lot of 
derived methods have been adapted to dynamic problems in real 
variables, stochastic problems, multi-targets and parallel 
implementations. It has also been used to produce near-optimal 
solutions to the travelling salesman problem.

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In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was aiming 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. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants Ant colony optimization algorithms have been applied to many combinatorial optimization problems, ranging from quadratic assignment toprotein folding or routing vehicles and a lot of derived methods have been adapted to dynamic problems in real variables, stochastic problems, multi-targets and parallel implementations. It has also been used to produce near-optimal solutions to the travelling salesman problem.

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Dalam ilmu komputer dan riset operasi, koloni semut
optimasi algoritma (ACO) adalah teknik probabilistik untuk
menyelesaikan masalah komputasi yang dapat dikurangi untuk
menemukan jalur yang baik melalui grafik.
Algoritma ini adalah anggota dari semut algoritma koloni keluarga,
dalam metode intelijen segerombolan , dan itu merupakan beberapa
optimasi metaheuristik. Awalnya diusulkan oleh Marco
Dorigo tahun 1992 di tesis PhD-nya, [1] [2] algoritma pertama
yang bertujuan untuk mencari jalur yang optimal dalam grafik, berdasarkan
perilaku semut mencari jalan antara koloni mereka dan
sumber makanan . Ide asli sejak diversifikasi untuk memecahkan
kelas yang lebih luas dari masalah numerik, dan sebagai hasilnya, beberapa
masalah telah muncul, menggambar pada berbagai aspek
perilaku semut
algoritma semut telah diterapkan pada banyak
masalah optimasi kombinatorial, mulai dari kuadrat
tugas toprotein lipat atau routing kendaraan dan banyak
metode berasal telah disesuaikan dengan masalah yang dinamis secara real
variabel, masalah stokastik, multi-target dan paralel
implementasi. Ini juga telah digunakan untuk menghasilkan dekat-optimal
solusi untuk masalah salesman keliling.

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