Ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. More recently, Parpinelli et al have applied ACO to data mining classification problems, where they introduced a classification algorithm called Ant Miner. In this paper, we present a system that combines both the proposed Improved Quickreduct algorithm for data preprocessing and ant ...

SI is a computational and collective behavioral metaphor that is used for solving problems. The problems can be solved by SI by taking ants, termites, bees and wasps as an example. The application of SI algorithm are ACO, PSO and ABC which have been already applied to solve real world optimization problems in engineering. ACO is a member of SI in which ACO is inspired by the behaviour of ant co...

In this paper multiobjective optimization problem simultaneously optimized using hybrid PS-ACO algorithm has been attempted. The methodology used in this paper is based upon the information sharing and movement of swarms or particles in a search space, and further applying ACO on the result obtained by the PSO. Multiobjective optimization problems are present at physical design level at partiti...

Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when they are enhanced to maintain diversity and transfer knowledge. Several approaches have been integrated with ACO to improve its performance for DOPs. Among these integrations, the ACO algorithm with immigrants schemes has shown good results on the dynamic travelling salesman pro...

Aiming at the trajectory generation and optimization of mobile robots in complex uneven environments, a hybrid scheme using mutual learning adaptive ant colony (MuL-ACO) is proposed this paper. In order to describe environment with various obstacles, 2D-H map introduced Then an algorithm based on simulated annealing (SA) generate initial trajectories robots, where de-temperature function algori...

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary...

In recent years, there has been a growing interest in addressing dynamic optimization problems (DOPs) using evolutionary algorithms (EAs). Several approaches have been developed for EAs to increase the diversity of the population and enhance the performance of the algorithm for DOPs. Among these approaches, immigrants schemes have been found beneficial for EAs for DOPs. In this paper, random, e...

Feature Selection is the process of selecting a subset of features available, allowing a certain objective function to be optimized, from the data containing noisy,irrelevant and redundant features. This paper presents a novel feature selection method that is based on hybridization of ACO with a binary PSO to obtain excellent properties of two algorithms by synthesizing them and aims at achievi...

The problem of optimal planning of multiple sources of distributed generation (DG) in distribution networks is treated in this paper using an improved Ant Colony Optimization algorithm (ACO). This objective of this problem is to determine the DG optimal size and location that in order to minimize the network real power losses. Considering the multiple sources of DG, both size and location are s...

This paper proposes a procedure for solving the optimal reactive power dispatch (ORPD) problem using ant colony optimization (ACO) algorithm. The objective of the ORPD is to minimize the transmission line losses under control and dependent variable constraints using proposed sensitivity parameters of reactive power that dependent on a modification of Fast Decoupled Power Flow (FDPF) model. The ...