In each guiding template, the star which it corresponds to is located in its centre and the stars in a defined neighbourhood of the centre star are taken as destinations for ant colony
FOR each ant i Initialize ant i END FOR Iteration k=1 DO FOR each particle i Construct the trail Select the next node Calculate the value with the fuzzy inference system Update the local pheromone trail IF the trail is complete Calculate the value with the fuzzy inference system Update the global pheromone trail ELSE GO to Select the next node END IF END FOR k = k + 1 WHILE Maximum iterations or minimum error criteria are not attained In this paper, we used an Ant Colony
System (ACS)  for three important reasons.
These algorithms like ant colony
optimization have been applied to feature selection as no solid heuristic exist to find optimal feature subset, so it is expected that the ants discover good feature combinations as they proceed through the search space.
Ant System (AS), Ant Colony
System (ACS), Max-Min Ant System (MMAS), Elitist Ant System (EAS), and Rank-Based Ant System (RAS)  are variants of ACO that have been developed to solve problems in various domains.
Keywords: feature selection, Ant Colony
Optimization, pattern recognition.
The three algorithms are Bayes theorem, Clustering, and Ant colony
Based on ACA, we propose a novel improved multigroup ant colony
algorithm (IMGACA) in Section 3, and the algorithm includes random sequence-based UCAV selection strategy, constraint-based candidate task generation strategy, objective function value-based state transition strategy, and crossover operator-based local search strategy.
In the process of applying ant colony
optimization to specific problems, the search space should be as large as possible.
Two intelligent heuristic algorithms including dynamic programming-tabu search algorithm and ant colony
algorithm are selected here to solve it, where the tabu search algorithm is a dynamic neighborhood search algorithm with strong hill-climbing ability, and it can jump out of the local optimization to find the global optimal solution.
Genetic Algorithm-Chaos Ant Colony
The ant colony
algorithm, which is a bionic algorithm, is applied herein to the study of the three regular graph coloring problem, in order to gain a more reasonable solution to the problems of coloring and number labeling.
The author has organized the main body of his text in fourteen chapters devoted to cellular automata and spatial diffusion models, artificial neural networks, ant colony
optimization, organization and organizational theory, self-organization, intelligence principles, agent-based modeling, catastrophe theory and methods, fish and particle swarm optimization, and many other related subjects.
optimization (ACO) is a meta-heuristic approach introduced by Marco in 1992 [14-20].