The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the ...

Spam is an unwanted email that is harmful to communications around the world. Spam leads to a growing problem in a personal email, so it would be essential to detect it. Machine learning is very useful to solve this problem as it shows good results in order to learn all the requisite patterns for classification due to its adaptive existence. Nonetheless, in spam detection, there are a large num...

In this paper the multiple projects resourceconstrained project scheduling problem is considered. Several projects are to be scheduled simultaneously with sharing several kinds of limited resources in this problem. Each project contains non-preemptive and deterministic duration activities which compete limited resources under resources and precedence constraints. Moreover, there are the due dat...

This paper compares the behaviour of three metaheuristics for the function optimization problem on a set of classical functions handling a large number of variables and known to be hard. The first algorithm to be described is Particle Swarm Optimization (PSO). The second one is based on the paradigm of Artificial Immune System (AIS). Both algorithms are then compared with a Genetic Algorithm (G...

despite the growing use of evolutionary multi-objective optimization algorithms in different categories of science, these algorithms as a powerful tool in portfolio optimization and specially solving multi-objective portfolio optimization problem is still in its early stages. in this paper, moeas have been used for solving multi-objective portfolio optimization problem in tehran stock market. f...

The probabilistic traveling salesman problem (PTSP), a paradigmatic example of a stochastic combinatorial optimization problem, is used to study routing problems under uncertainty. Recently, we introduced a new estimation-based iterative improvement algorithm for the PTSP and we showed that it outperforms for a number of instance classes the previous state-of-the-art algorithms. In this paper, ...

Evolutionary computation is inspired by nature in order to formulate metaheuristics capable to optimize several kinds of problems. A family of algorithms has emerged based on this idea; e.g. genetic algorithms, evolutionary strategies, particle swarm optimization (PSO), ant colony optimization (ACO), etc. In this paper we show a populationbased metaheuristic inspired on the gravitational forces...

The incorporation of data mining techniques into metaheuristics has been efficiently adopted to solve several optimization problems. Nevertheless, we observe in the literature that this hybridization has been limited to problems in which the solutions are characterized by sets of (unordered) elements. In this work, we develop a hybrid data mining metaheuristic to solve a problem for which solut...

This work deals with memetic-computing agent-models based on the cooperative integration of search agents endowed with (possibly different) optimization strategies, in particular memetic algorithms. As a proof-ofconcept of the model, we deploy it on the tool switching problem (ToSP), a hard combinatorial optimization problem that arises in the area of flexible manufacturing. The ToSP has been t...

The majority of the algorithms used to solve hard optimization problems today are population metaheuristics. These methods are often presented under a purely algorithmic angle, while insisting on the metaphors which led to their design. We propose in this article to regard population metaheuristics as methods making evolution a probabilistic sampling of the objective function, either explicitly...