An Improved Ant Colony Optimization with Optimal Search Library for Solving the Traveling Salesman Problem

Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

“…Task scheduling problems are related to the efficiency of all computing facilities and are of paramount importance [16]. Cloud computing task scheduling is a NP-complete problem; it can be solved in different methods: traditional deterministic algorithms and heuristic intelligent algorithms [17][18][19][20][21][22][23][24][25]. However, those methods don not take energy consumption into account, and, to overcome this limitation, researchers have proposed some approaches.…”

Modeling of the process of territorial communities formation using swarm intelligence algorithms

“…The result of equation (13) is the acquisition of an experiment for the memory of the ant colony, and the connection between the HSs making up the route with the smaller total distance will receive larger pheromone values than the connections that are components of the routes that have a long length. The obtained result ∆M t ij k ( ) is used in equation (14) to increase the intensity of pheromone on each edge between the incident traced by the ant-agent route. An increase in the mark value (pheromone imposition) occurs at the end of each iterations of the route search cycle by ants-agents, following the procedure of «pheromone evaporation» (15).…”

“…Equation (14) is applied to the entire route, and the pheromone value on each edge increases in proportion to the length of the traversed route. Therefore, it is necessary to wait until the ant-agent completes the journey and only then update the meaning of pheromone according to the experience obtained by the agents, otherwise the true length of the traversed path will remain unknown [9,14].…”

“…Each iteration of the search cycle for ants-agents ends with the return of all agents to the initial HS and the collection of results, after which the «taboo» list is cleared for each k -th agent and the length of the traversed route is deleted along with the recorded resulting route. The cycle is executed by one or more of the following conditions: a certain number of iterations are executed; the best solutions have a very small change factor compared to previous solutions; a certain time limit for MOP calculation has been exhausted [14,15]. After the path is completed and the ant-agent returns to the original node, a procedure is performed to determine if there is an opportunity to form a community based on the received data.…”

Study on an Improved ACO Algorithm Based on Multi-Strategy in Solving Function Problem

“…Juang et al [22] proposed a cooperative continuous ant colony optimization (CCACO) algorithm and applies it to address the accuracy-oriented fuzzy systems (FSs) design problems. Pang et al [23] proposed an improved ant colony optimization algorithm, which defines the new heuristic information and the improved pheromone update rules. Jiang et al [24] proposed a co-evolutionary improved multi-ant colony optimization (CIMACO) algorithm for ship multi and branch pipe route design.…”