Ant colony optimization (ACO) and particle swarm optimization (PSO) are two popular algorithms in swarm intelligence. Recently, a continuous ACO named ACOR was developed to solve the continuous optimization problems. This study incorporated ACOR with PSO to improve the search ability, investigating four types of hybridization as follows: (1) sequence approach, (2) parallel approach, (3) sequenc...

A technique for Fuzzy Cognitive Maps learning, which is based on the minimization of a properly defined objective function using the Particle Swarm Optimization algorithm, is presented. The workings of the technique are illustrated on an industrial process control problem. The obtained results support the claim that swarm intelligence algorithms can be a valuable tool for Fuzzy Cognitive Maps l...

A swarm of robots is a collection of tiny identical autonomous robots. The robots perform a given task, e.g., cleaning a big surface, moving a big object, guarding an area etc., in a collaborative framework. The goal of research in swarm robotics is to develop a low cost multi-robot system which will be at least as efficient as one big expensive unit. The field of swarm robotics has been addres...

Clustering is the unsupervised learning in which the data is divided into similar groups (cluster) without any prior knowledge. The emerging swarm-based algorithms become an alternative to the conventional clustering methods to enhance the quality of results. Artificial Bee Colony (ABC) Algorithm is one of the Swarm Intelligent based optimization algorithm that exhibit foraging properties of a ...

Ensemble-based learning is a very promising option to reach a robust partition. Due to covering the faults of each other, the classifiers existing in the ensemble can do the classification task jointly more reliable than each of them. Generating a set of primary partitions that are different from each other, and then aggregation the partitions via a consensus function to generate the final part...

Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular. Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved for the majorit...

This paper proposes an improved ant colony optimization cluster algorithm based on a classics algorithm LF algorithm. By the introduction of a new formula and the probability of similarity metric conversion function, as well as the new formula of distance, this algorithm can deal with the category data easily. It also introduces a new adjustment process, which adjusts the cluster generated by t...

Balancing assembly lines, a family of optimization problems commonly known as Assembly Line Balancing Problem, is notoriously NP-Hard. They comprise a set of problems of enormous practical interest to manufacturing industry due to the relevant frequency of this type of production paradigm. For this reason, many researchers on Computational Intelligence and Industrial Engineering have been conce...

Ant foraging is a paradigmatic example of self-organized behavior. We give new experimental evidence for previously unobserved short-term adaptiveness in ant foraging and show that current mathematical foraging models cannot predict this behavior. As a true extension, we develop Itô diffusion models that explain the newly discovered behavior qualitatively and quantitatively. The theoretical ana...

The paper proposed a network scheduling in cloud computing based on intelligence Particle Swarm Optimization algorithm aimed at the disadvantages of cloud computing network scheduling. Firstly, on the basis of cloud model, used intelligence Particle Swarm Optimization algorithm with strong ability of global searching to find the better solution of cloud computing network scheduling then turned ...