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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]

In order to improve the optimization effect of traditional ant colony algorithm in solving traveling salesman problem, this paper developed a dual-ant colony optimization algorithm with dynamic differentiation and neighborhood induction mechanism. Firstly, the algorithm introduced chaotic random strategy, and changed the original greedy strategy in the initialization stage of the algorithm to chaotic distribute the initial pheromone, so as to maintain the diversity of the population and improve the accuracy of the solution. Secondly, the algorithm divided the isolated ants in the ant colony into isolated ant colony and normal ant colony, and the two groups of ants searched around the current optimal path and the outlier path respectively. It adopted the induction mechanism among the populations. Normal ants were responsible for searching the optimal path, and isolated ants released pheromones randomly in chaos to induce the normal ant colony to a new path, thus effectively balancing the contradiction between the convergence rate and the diversity of solutions. The simulation results of different scale TSP show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

As the fourth fundamental circuit element, the memristor may execute computations while storing data. Fracmemristor takes advantage of the fractional calculate's long-term memory, non-locality, weak singularity, and the memristor's storage–computational integration. Since the physical structure of the fracmemristor is similar to the topology of the ant transfer probability flow in ACO, we propose the fractional-order memristive ant colony algorithm (FMAC), which uses the fracmemristor physical system to record the probabilistic transfer information of the nodes that the ant will crawl through in the future and pass it to the current node of the ant, so that the ant acquires the ability to predict the future transfer. After instigating the optimization capabilities with TSP, we discovered that FMAC is superior to PACO-3opt, the best integer-order ant colony algorithm currently available. FMAC operates substantially more quickly than the fractional-order memristor ant colony algorithm due to the transfer probability prediction module based on the physical fracmemristor system (FACA). [ABSTRACT FROM AUTHOR]

Aiming at the problems of slow convergence and easy fall into local optimal solution of the classic ant colony algorithm in path planning, an improved ant colony algorithm is proposed. Firstly, the Floyd algorithm is introduced to generate the guiding path, and increase the pheromone content on the guiding path. Through the difference in initial pheromone, the ant colony is guided to quickly find the target node. Secondly, the fallback strategy is applied to reduce the number of ants who die due to falling into the trap to increase the probability of ants finding the target node. Thirdly, the gravity concept in the artificial potential field method and the concept of distance from the optional node to the target node are introduced to improve the heuristic function to make up for the fallback strategy on the convergence speed of the algorithm. Fourthly, a multi-objective optimization function is proposed, which comprehensively considers the three indexes of path length, security, and energy consumption and combines the dynamic optimization idea to optimize the pheromone update method, to avoid the algorithm falling into the local optimal solution and improve the comprehensive quality of the path. Finally, according to the connectivity principle and quadratic B-spline curve optimization method, the path nodes are optimized to shorten the path length effectively. [ABSTRACT FROM AUTHOR]

The study of material failure phenomena with coupled multiple scales is a need and a challenge. The fundamental difficulty in these problems is due to multiscale bridging material parameters and damage, which ensure that different scales of analysis should be concordant. To address this issue, an inversion algorithm based on ant colony optimization was developed to establish a bridge from macroscale to mesoscale. By using the developed algorithm, mesoscopic parameters including elasticity modulus and damage can be predicted by relying solely on the macroscopic mechanical response, which can be easily obtained from experiments. The developed algorithm can promote the development of multiscale mechanics, which can support an effective method to determine harmonious material parameters and damage in different scales. As a case study, the multiscale bridging inversion results, including elasticity modulus, stress and strain, and damage from macroscale to mesoscale of a concrete specimen, are given and compared with the corresponding experiment. The ability of the developed inversion algorithm is verified. [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]