Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more fine-tuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Salesman Problem. To show that Ant Colony Optimization algorithms could be good alternatives to exi...

In many practical problems, several conflicting criteria exist for evaluating solutions. In recent years, strong research efforts have been made to develop efficient algorithmic techniques for tackling such multi-objective optimization problems. Many of these algorithms are extensions of well-known metaheuristics. In particular, over the last few years, several extensions of ant colony optimiza...

In this thesis, we investigate the capabilities of Ant Colony Optimization (ACO) metaheuristic tosolve combinatorial and multi-objective optimization problems. First, we propose a taxonomy ofACO algorithms proposed in the literature to solve multi-objective problems. Then, we studydifferent pheromonal strategies for the case of mono-objective multidimensional knapsackproblem. We...

This paper deals with the key issue of management to improve the allocation capability, facilitating the optimization of the objective in minimizing the cost, with the accuracy in dynamic allocation of nearest customers to depot. A method has been proposed to solve the Multi Depot Vehicle Routing Problem (MDVRP) based on Ant Algorithm which is a modified version of the traditional Ant colony Op...

The use of metaheuristics in Multi-objective Combinatorial Optimization, particularly Ant Colony Optimization (ACO), has grown recently. This paper proposes an approach where multi-species ants compete for food resources. Each species has its own search strategy and do not access pheromone information of other species. As in nature, successful ant populations are allowed to grow, whereas the ot...

The Ant Colony Optimization (ACO) metaheuristic is a bio-inspired approach for hard combinatorial optimization problems for stationary and non-stationary environments. In the ACO metaheuristic, a colony of artificial ants cooperate for finding high quality solutions in a reasonable time. An interesting example of a non-stationary combinatorial optimization problem is the Multiple Elevators Prob...

Limited amount of time and computational resources in industrial domain makes Ant Colony Optimization (ACO) a useful approach to find near optimal solutions in polynomial time for Nondeterministic Polynomial time (NP) problems. For dynamically changing graphs, such as in case of network routing and urban transportation systems which are based on Travelling Salesman Problem (TSP), the ant colony...

This paper presents improved Ant Colony Optimization (ACO) algorithms for data mining. The goal of the algorithms is to extract classification rules from data. The traditional Ant Colony Optimization algorithm is enhanced with genetic operators to develop improved ACO algorithms. The genetic operators like crossover, mutation are used to develop Ant Colony Optimization with Crossover (ACOC), AC...

We explore the relation between memcomputing, namely computing with and in memory, and swarm intelligence algorithms. In particular, we show that one can design memristive networks to solve short-path optimization problems that can also be solved by ant-colony algorithms. By employing appropriate memristive elements one can demonstrate an almost one-toone correspondence between memcomputing and...

— This paper examines ant colony optimization technique for edge detection. ACO is a technique to find out solutions for combinatorial optimization problem. The essential part of ACO algorithms is the combination of prior information about the structure of a solution with post information about the structure of previously obtained good solutions. Keywords— Ant Colony Optimization, Edge Detectio...