Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been successfully applied to various Combinatorial Optimization Problems (COPs).

In modern logistics management systems, route planning requires high efficiency. The Open Capacitated Vehicle Routing Problem (OCVRP) deals with finding optimal delivery routes for a fleet of vehicles serving geographically distributed customers, without requiring the vehicles to return to the depot after deliveries. The present study is comparative in nature and speaks of two algorithms for OCVRP solution: Ant Colony Optimization (ACO), a nature-inspired metaheuristic; and Google OR-Tools, an industry-standard toolkit for optimization. Both implementations were developed in Python and using a custom dataset. Performance appraisal was based on routing efficiency, computation time, and scalability. The results show that ACO allows flexibility in routing parameters while OR-Tools runs much faster with more consistency and requires less input. This could help choose among routing strategies for scalable real-time logistics systems.

This study presents Neural Focused Ant Colony Optimization (NeuFACO), a non-autoregressive framework for the Traveling Salesman Problem (TSP) that combines advanced reinforcement learning with enhanced Ant Colony Optimization (ACO). NeuFACO employs Proximal Policy Optimization (PPO) with entropy regularization to train a graph neural network for instance-specific heuristic guidance, which is integrated into an optimized ACO framework featuring candidate lists, restricted tour refinement, and scalable local search. By leveraging amortized inference alongside ACO stochastic exploration, NeuFACO efficiently produces high-quality solutions across diverse TSP instances.

Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally, customizing ACO for a specific problem requires the expert design of knowledge-driven heuristics. In this paper, we propose DeepACO, a generic framework that leverages deep reinforcement learning to automate heuristic designs. DeepACO serves to strengthen the heuristic measures of existing ACO algorithms and dispense with laborious manual design in future ACO applications. As a neural-enhanced meta-heuristic, DeepACO consistently outperforms its ACO counterparts on eight COPs using a single neural architecture and a single set of hyperparameters. As a Neural Combinatorial Optimization method, DeepACO performs better than or on par with problem-specific methods on canonical routing problems. Our code is publicly available at https://github.com/henry-yeh/DeepACO.

A range of complicated real-world problems have inspired the development of several optimization methods. Here, a novel hybrid version of the Ant colony optimization (ACO) method is developed using the sample space reduction technique of the Cohort Intelligence (CI) Algorithm. The algorithm is developed, and accuracy is tested by solving 35 standard benchmark test functions. Furthermore, the constrained version of the algorithm is used to solve two mechanical design problems involving stepped cantilever beams and I-section beams. The effectiveness of the proposed technique of solution is evaluated relative to contemporary algorithmic approaches that are already in use. The results show that our proposed hybrid ACO-CI algorithm will take lesser number of iterations to produce the desired output which means lesser computational time. For the minimization of weight of stepped cantilever beam and deflection in I-section beam a proposed hybrid ACO-CI algorithm yielded best results when compared to other existing algorithms. The proposed work could be investigate for variegated real world applications encompassing domains of engineering, combinatorial and health care problems.

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Abstract: Gradual pattern extraction is a field in (KDD) Knowledge Discovery in Databases that maps correlations between attributes of a data set as gradual dependencies. A gradual dependency may take a form of "the more Attribute K, the less Attribute L". In this paper, we propose an ant colony optimization technique that uses a probabilistic approach to learn and extract frequent gradual patterns. Through computational experiments on real-world data sets, we compared the performance of our ant-based algorithm to an existing gradual item set extraction algorithm and we found out that our algorithm outperforms the later especially when dealing with large data sets. Abstract: Ant Colony Optimization algorithm is a magnificent heuristics technique based on the behavior of ants.

This paper describes a new approach for approximating the inverse kinematics of a manipulator using an Ant Colony Optimization (ACO) based RBFN (Radial Basis Function Network). In this paper, a training solution using the ACO and the LMS (Least Mean Square) algorithm is presented in a two-phase training procedure. To settle the problem that the cluster results of k-mean clustering Radial Basis Function (RBF) are easy to be influenced by the selection of initial characters and converge to a local minimum, Ant Colony Optimization (ACO) for the RBF neural networks which will optimize the center of RBF neural networks and reduce the number of the hidden layer neurons nodes is presented. The result demonstrates that the accuracy of Ant Colony Optimization for the Radial Basis Function (RBF) neural networks is higher, and the extent of fitting has been improved.

Manually optimizing artificial neural network (ANN) structures has been an obstacle to the advancement of machine learning as it is significantly time-consuming and requires a considerable level of domain expertise [1]. The structure of an ANN is typically chosen based on its reputation based on results of existent literature or based on knowledge shared across the machine learning community, however changing even a few problem-specific meta-parameters can lead to poor generalization upon committing to a specific topology [2, 3]. To address these challenges, a number of neural architecture search (NAS) [1, 4-8] and neuroevolution (NE) [9, 10] algorithms have been developed to automate the process of ANN design. More recently, nature-inspired neural architecture search (NINAS) algorithms have shown increasing promise, including the Artificial Bee Colony (ABC) optimization procedure [11], the Bat algorithm [12], the Firefly algorithm [13], and the Cuckoo Search algorithm [14]. Among the more recently successful applied NINAS strategies are those based on ant colony optimization (ACO) [15], which have proven to be particularly powerful when automating the design of recurrent neural networks (RNNs). Originally, ACO for NAS was limited to small structures based on Jordan and Elman RNNs [16] or was used as a process for reducing the number of network inputs [17]. Later work proposed generalizations of ACO for optimizing the synaptic connections of RNN memory cell structures [18] and even entire RNN architectures in an algorithmic framework called Ant-based Neural Topology Search (ANTS) [19]. In the ANTS process, ants traverse a single massively-connected "superstructure", which contains all of the possible ways that the nodes of an RNN may connect with each other, both in terms of structure (i.e., all possible feed forward connections), and in time (i.e., all possible recurrent synapses that span many different time delays), searching for optimal RNN sub-networks.