Consultant-Guided Search (CGS) is a recent metaheuristic for combinatorial optimization problems, which has been successfully applied to the Traveling Salesman Problem (TSP). In experiments without local search, it has been able to outperform some of the best Ant Colony Optimization (ACO) algorithms. However, local search is an important part of any ACO algorithm and a comparison without local ...

The influence of the allowed running time on the choice of the parameters of an ant system is investigated. It is shown that different parameter values appear to be optimal depending on the algorithm run-time. The performance of the MAX -MIN Ant System (MMAS) on the University Course Timetabling Problem (UCTP) – a type of constraint satisfaction problem – is used as an example. The parameters t...

In the last few years a significant number of ant colony optimization (ACO) algorithms have been proposed for tackling multi-objective optimization problems. In this paper, we propose a software framework that allows to instantiate the most prominent multiobjective ACO (MOACO) algorithms. More importantly, the flexibility of this MOACO framework allows the application of automatic algorithm con...

The problem of finding a minimum vertex cover (MinVC) in a graph is a well known NP-hard problem with important applications. There has been much interest in developing heuristic algorithms for finding a “good” vertex cover in graphs. In practice, most heuristic algorithms for MinVC are based on the local search method. Previously, local search algorithms for MinVC have focused on solving acade...

Presently, the introduction of intelligent models to optimize structural problems has become an important issue in civil engineering and almost all other fields of engineering. Optimization models in artificial intelligence have enabled us to provide powerful and practical solutions to structural optimization problems. In this study, a novel method for optimizing structures as well as solving s...

Local search is a powerful and well-established method for solving hard combinatorial problems. Yet, until recently, it has provided very little user support, leading to time-consuming and error-prone implementation tasks. We introduce a scheme that, from a high-level description of a constraint in existential second-order logic with counting, automatically synthesises incremental penalty calcu...

Maximal covering problem (MCP) is classified as a linear integer optimization problem which can be effectively solved by integer programming technique. However, as the problem size grows, integer programming requires excessive time to get an optimal solution. This paper suggests a method for applying integer programming-based local search (IPbLS) to solve large-scale maximal covering problems. ...

We consider the problem of scheduling a single machine to minimize total tardiness with sequence dependent setup times. We present two algorithms, a problem space-based local search heuristic and a Greedy Randomized Adaptive Search Procedure (GRASP) for this problem. With respect to GRASP, our main contributions are—a new cost function in the construction phase, a new variation of Variable Neig...

today, online social networks are very popular due to the possibility of creating relationships between people all over the world. these social networks with possibilities such as friend recommendation generally use local features derived from social graph structure. for friend recommendation, there are different algorithms with local and global approaches. in this paper, we proposed an algorit...