Ad
related to: ant colony optimization algorithm
Search results
Results From The WOW.Com Content Network
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 ...
The humanoid ant algorithm (HUMANT) [1] is an ant colony optimization algorithm. The algorithm is based on a priori approach to multi-objective optimization (MOO), which means that it integrates decision-makers preferences into optimization process. [2] Using decision-makers preferences, it actually turns multi-objective problem into single ...
Ant colony optimization (ACO), introduced by Dorigo in his doctoral dissertation, is a class of optimization algorithms modeled on the actions of an ant colony. ACO is a probabilistic technique useful in problems that deal with finding better paths through graphs.
Ant Colony Optimization: ACO Nature-inspired Bio-inspired 2006 [13] Glowworm Swarm Optimization: GSO Nature-inspired Swarm-based 2006 [14] Shuffled Frog Leaping Algorithm SFLA Nature-inspired Bio-inspired 2006 [15] Invasive Weed Optimization IWO Nature-inspired Plant-based 2006 [16] Seeker Optimization Algorithm SOA Nature-inspired Human-based ...
Ant colony optimization is a widely used algorithm which was inspired by the behaviours of ants, and has been effective solving discrete optimization problems related to swarming. [31] The algorithm was initially proposed by Marco Dorigo in 1992, [ 32 ] [ 33 ] and has since been diversified to solve a wider class of numerical problems.
Optimized Markov chain algorithms which use local searching heuristic sub-algorithms can find a route extremely close to the optimal route for 700 to 800 cities. TSP is a touchstone for many general heuristics devised for combinatorial optimization such as genetic algorithms , simulated annealing , tabu search , ant colony optimization , river ...
Evolutionary computing techniques mostly involve metaheuristic optimization algorithms. Broadly speaking, the field includes: Agent-based modeling. Ant colony optimization; Particle swarm optimization; Swarm intelligence; Artificial immune systems; Artificial life. Digital organism; Cultural algorithms; Differential evolution; Dual-phase evolution
Ant colony optimization (ACO) uses many ants (or agents) to traverse the solution space and find locally productive areas. The cross-entropy method (CE) generates candidate solutions via a parameterized probability distribution. The parameters are updated via cross-entropy minimization, so as to generate better samples in the next iteration.