This paper presents an hybrid algorithm based on genetic algorithm and ant colony optimization for continuous optimization, which combines the global exploration ability of the former with the local exploiting ability of the later. The proposed algorithm is evaluated on several benchmark functions. The simulation results show that the proposed algorithm performs quite well and outperforms class...

Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. Originally the method to construct decision tree follows greedy approach which results in local tree and classification rules. Ant Colony Optimization (ACO) is a metaheuristic approach used to get more optimal solution compared to other methods from large search space. In propo...

In this article we present an application of the Ant Colony Optimization (ACO) metaheuristic to the single machine total weighted tardiness problem. First, we briefly discuss the constructive phase of ACO in which a colony of artificial ants generates a set of feasible solutions. Then, we introduce some simple but very effective local search. Last, we combine the constructive phase with local s...

Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has been investigated by many researchers. In this paper, a novel feature subset search procedure that utilizes...

land-use spatial allocation is a multi-objective collaborative spatial optimization methodfor rational use of the land use. based on global search capabilities and the information feedbackmechanism of ant colony optimization (aco), a land-use spatial allocation model (aco-la) isproposed. firstlyfirst, a construction graph is built for modeling the land-use spatial allocationproblem. secondlysec...

This paper uses a modified Ant Colony Optimization (ACO) algorithm to price simple financial derivatives. We use ants to find the optimum time to exercise an option. Our algorithm searches the solution space to find optimum solution under some user defined constraints. In this preliminary study we show that the modified ACO works in predicting an optimum exercise time of a simple vanilla option.

Ant Colony Optimization (ACO) algorithms belong to class of metaheuristic algorithms, where a search is made for optimized solution rather than exact solution, based on the knowledge of the problem domain. ACO algorithms are iterative in nature. As the iteration proceeds, solution converges to the optimized solution. In this paper, we propose new updation mechanism based 1 / 4

Ant Colony Optimization (ACO) algorithm has evolved as the most popular way to attack the combinatorial problems. The ACO algorithm employs multi agents called ants that are capable of finding optimal solution for a given problem instances. These ants at each step of the computation make probabilistic choices to include good solution component in partially 1 / 4

This paper illustrates first approach to solve linear system of equations by using Ant Colony Optimization (ACO). ACO is multi-agent heuristic algorithm working in continuous domains. The main task is checking efficiency of this method in several examples and discussion about results. There will be also presented future possibilities regarding researches.

In this paper, we enriched Ant Colony Optimization (ACO) with interval outranking to develop a novel multi-objective ACO optimizer approach problems many objective functions. This proposal is suitable if the preferences of Decision Maker (DM) can be modeled through relations. The introduced algorithm (Interval Outranking-based ACO, IO-ACO) first ant-colony that embeds an model bear vagueness an...