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

In Software Product Lines (SPLs) it may be difficult or even impossible to test all the products of the family because of the large number of valid feature combinations that may exist. Thus, we want to find a minimal subset of the product family that allows us to test all these possible combinations (pairwise). Furthermore, when testing a single product is a great effort, it is desirable to first test products composed of a set of priority features. This problem is called Prioritized Pairwise Test Data Generation Problem. State-of-the-art algorithms based on Integer Linear Programming for this problema are faster enough for small and medium instances. However, there exists some real instances that are too large to be computed with these algorithms in a reasonable time because of the exponential growth of the number of candidate solutions. Also, these heuristics not always lead us to the best solutions. In this work we propose a new approach based on a hybrid metaheuristic algorithm called Construct, Merge, Solve & Adapt. We compare this matheuristic with four algorithms: a Hybrid algorithm based on Integer Linear Programming ((HILP), a Hybrid algorithm based on Integer Nonlinear Programming (HINLP), the Parallel Prioritized Genetic Solver (PPGS), and a greedy algorithm called prioritized-ICPL. The analysis reveals that CMSA results in statistically significantly better quality solutions in most instances and for most levels of weighted coverage, although it requires more execution time.

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.

The development of domain ontological models, though being a mature research arena backed by well-established methodologies, still suffer from two key shortcomings. Firstly, the issues concerning the semantic persistency of ontology concepts and their flexible reuse in domain development employing existing approaches. Secondly, due to the difficulty in understanding and reusing top-level concepts in existing foundational ontologies, the obfuscation regarding the semantic nature of domain representations. The paper grounds the aforementioned shortcomings in representation diversity and proposes a three-fold solution - (i) a pipeline for rendering concepts reuse-ready, (ii) a first characterization of a minimalistic foundational knowledge model, named foundational teleology, semantically explicating foundational distinctions enforcing the static as well as dynamic nature of domain representations, and (iii) a flexible, reuse-native methodology for diversity-aware domain development exploiting solutions (i) and (ii). The preliminary work reported validates the potentiality of the solution components.

Free Coupon Discount - Knowledge Graph solution development using TigerGraph, Knowledge Graph Solutions Created by Neena Sathi Preview this Course GET COUPON CODE You will be able to understand and document the use case for knowledge graph solution You will be able to Design a Knowledge Graph solution You will be able to Design / extract data from Knowledge Graph data sources. You will be able to Design / Build key knowledge graph solution components and analytics Finally, You will be able to Prototype a graph analytics experience and document your understanding on Knowledge Graph Insights using a "Rapid Prototyping of Knowledge Graph Solutions using TigerGraph" course will help you strategize knowledge graph use cases and help you build or prototype a use case for your knowledge graph engagement. This course includes - How to define Graph Use Case - How to set up Sandbox using TigerGraph for your Graph use case - How to develop and execute structured graph queries - How to define elastic or higher level graph representation - Finally how to connect your graph solution with other solution components using Python. Who this course is for: Management, strategy and business analyst professionals Architects, technical leads and system analysts from IT organization Senior year undergraduate and graduate students in Business, Analytics, and IT Vendors, consultants and service providers for Graph Analytics 100% Off Udemy Coupon . You will be able to understand and document the use case for knowledge graph solution You will be able to Design a Knowledge Graph solution You will be able to Design / extract data from Knowledge Graph data sources.

It's true to say that RPA has taken us a long way towards unlocking productivity benefits tied up in manual processes. It has galvanized a mindset that things can change without a need for massive systems reengineering. Although wholesale change isn't always a realistic option for most organization, RPA acts as a catalyst to help businesses progress and add value, enabling them to build strategic plans around investments they've already made into their legacy systems. It doesn't preclude big change – it simply supports a faster release of benefits alongside greater change initiatives, so that everyone's a winner! The expansion into artificial intelligence (AI) is the next step of this more granular, faster form of transformation, with more and more business activities either wholly or partially automated by increasingly sophisticated means.

Many optimization techniques evaluate solutions consecutively, where the next candidate for evaluation is determined by the results of previous evaluations. For example, these include iterative methods, "black box" optimization algorithms, simulated annealing, evolutionary algorithms and tabu search, to name a few. When solving an optimization problem, these algorithms evaluate a large number of solutions, which raises the following question: Is it possible to learn something about the optimum using these solutions? In this paper, we define this "learning" question in terms of a logistic regression model and explore its predictive accuracy computationally. The proposed model uses a collection of solutions to predict the components of the optimal solutions. To illustrate the utility of such predictions, we embed the logistic regression model into the tabu search algorithm for job shop scheduling problem. The resulting framework is simple to implement, yet provides a significant boost to the performance of the standard tabu search.

Construct, Merge, Solve & Adapt (CMSA) [6] is a hybrid metaheuristic that can be applied to any combinatorial optimization problem for which is known a way of generating feasible solutions, and whose subproblems can be solved to optimality by a black-box solver. Moreover, note that CMSA is thought for those problem instances for which the application of 1 the standalone black-box solver is not feasible due to the problem instance size and/or difficulty. The main idea of CMSA is to generate reduced subinstances of the original problem instances, based on feasible solutions that are constructed at each iteration, and to solve these reduced instances by means of the black-box solver. Obviously, the parameters of CMSA have to be adjusted in order for the size of the reduced sub-instances to be such that the black-box solver can solve them efficiently. CMSA has been applied to several NPhard combinatorial optimization problems, including minimum common string partition [6, 4], the repetition-free longest common subsequence problem [5], and the multidimensional knapsack problem [15]. A possible disadvantage of CMSA is the fact that a problem-specific way of probabilistically generating solutions is used in the above-mentioned applications. Therefore, the goal of this paper is to design a CMSA variant that can be easily applied to different combinatorial optimization problems. One way of achieving this goal is the development of a solver for a quite general problem.

A well known N P-hard problem called the Generalized Traveling Salesman Problem (GTSP) is considered. In GTSP the nodes of a complete undirected graph are partitioned into clusters. The objective is to find a minimum cost tour passing through exactly one node from each cluster. An exact exponential time algorithm and an effective meta-heuristic algorithm for the problem are presented. The meta-heuristic proposed is a modified Ant Colony System (ACS) algorithm called Reinforcing Ant Colony System (RACS) which introduces new correction rules in the ACS algorithm. Computational results are reported for many standard test problems. The proposed algorithm is competitive with the other already proposed heuristics for the GTSP in both solution quality and computational time.