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Ant Colony Optimization (ACO) tends to fall into local optima and has insufficient convergence when solving the Traveling Salesman Problem (TSP). To overcome this problem, this paper proposes a multiple ant colony optimization (LDTACO) based on novel Long Short-Term Memory network and adaptive Tanimoto communication strategy. Firstly, we introduce an Artificial Bee Colony-based Ant Colony System (ABC-ACS), which along with the classic Ant Colony System (ACS) and Max-Min Ant System (MMAS), form the final proposed algorithm. These three types of subpopulations complement each other to improve overall optimization performance. Secondly, the evaluation reward mechanism is proposed to enhance the guiding role of the Recommended paths, which can effectively accelerate convergence speed. Besides, an adaptive Tanimoto communication strategy is put forward for interspecific communication. When the algorithm is stagnant, the homogenized information communication method is activated to help the algorithm jump out of the local optima, thus improving solution accuracy. Finally, the experimental results show that the proposed algorithm can lead to more accurate solution accuracy and faster convergence speed. [ABSTRACT FROM AUTHOR]

One of the major problems of transport logistics is planning optimal delivery routes. Solving this problem leads to combinatorial optimizations that require complex computations. The present research considers an asymmetric problem of transport routing with a limitation of the carrying capacity of transport facilities, the duration of the route, and a heterogeneous transport facilities fleet. An algorithm for solving the routing problem based on the ant colony method is proposed. The web application that has been developed implements the proposed algorithm for solving this problem. The obtained optimal routes were compared with the results of route building by other cartographic services. The proposed algorithm showed the best result. [ABSTRACT FROM AUTHOR]

To address the lack of convergence speed and diversity of Ant Colony Optimization (ACO), a dynamic reproductive ant colony algorithm based on piecewise clustering (RCACS) is proposed to optimize the problems. First, the data is segmented by the clustering algorithm, and the exit and entrance of each cluster are obtained by the nearest neighbor strategy. The algorithm traversed all points of the cluster to find an unclosed shortest path according to the exit and entrance. Many fragment paths eventually merge into a full TSP. This strategy can accelerate the convergence speed of the algorithm and help it get higher accuracy. Second, when the algorithm stagnates, the dynamic regeneration mechanism based on feature transfer will transfer the excellent features of the mother-ants to the child-ants, so that it can further explore the neighborhood of the current optimal solution and help the algorithm jump out of the local optimum. From the results of simulation experiments and the rank-sum test, it can be found that the improved algorithm can effectively improve the diversity and accuracy of the algorithm, especially when solving large-scale problems. [ABSTRACT FROM AUTHOR]

As a bionic optimization algorithm, ant colony algorithm has the advantages of robustness, parallel computation and easy combination and so on, which can solve complicated combinatorial optimization problems. However, the selection strategy of traditional algorithm is more random, which leads to the slow evolution speed. Therefore, an improved ant colony algorithm is proposed, which uses density peak clustering algorithm to classify sites and local optimization strategy. The simulating results of multiple TSP problems demonstrate that the improved algorithm has good optimization ability, greatly improves the quality of the solution and the speed of optimization, and overcomes the slowness and tendency of the algorithm. [ABSTRACT FROM AUTHOR]

Cloud computing is the most emerging technology in distributed systems which provides users flexibility of storing data and sharing of computing resources by making use of the concept of virtualization. Large amount of data processing is required in developing cloud application services which increases the bandwidth. To avoid this, proper scheduling of tasks is required. Task scheduling is a combinatorial optimization problem and is one of the critical issues to be solved in cloud computing. Proper task scheduling not only reduces the make span but also hikes the system performance. In this research work, a novel strategy is proposed to solve task scheduling using Ant Colony Optimization (ACO) by adapting Reinforcement learning (RL) along with fault tolerance to make the scheduling process fault resistant, and to achieve the objective of minimum make-span. The proposed algorithm, Reinforced-Ant Colony Optimization (RACO) yields about 60% of better performance than sole implementation of ACO. [ABSTRACT FROM AUTHOR]

A spectral-line-extraction algorithm based on the ant-colony algorithm is proposed to address the difficulty of extracting spectral lines in low signal-to-noise ratio conditions, and the problem that results from the optimal path algorithm falls into local optimization. The algorithm applies the ant-colony path-optimization strategy to detect a spectral line and constructs a corresponding mathematical model using the grid method. A new cost function is proposed to replace path length as the optimization standard in the conventional ant-colony algorithm. At the same time, the roulette rule is used to determine the direction of the next step. This algorithm improves the traditional heuristic function, increases the attraction of the target spectral line to the route search, and improves the convergence rate. Sea-trial data show that the algorithm performs better in extracting spectral lines with a low signal-to-noise ratio than the optimal path algorithm. [ABSTRACT FROM AUTHOR]