Related papers: Memcomputing and Swarm Intelligence
Recent advancements in the development of memristive devices has opened new opportunities for hardware implementation of non-Boolean computing. To this end, the suitability of memristive devices for swarm intelligence algorithms has enabled…
One of the important issues in computer networks is "Load Balancing" which leads to efficient use of the network resources. To achieve a balanced network it is necessary to find different routes between the source and destination. In the…
Swarm intelligence is widely recognized as a powerful paradigm of self-organized optimization, with numerous examples of successful applications in distributed artificial intelligence. However, the role of physical interactions in the…
The swarm intelligence of animals is a natural paradigm to apply to optimization problems. Ant colony, bee colony, firefly and bat algorithms are amongst those that have been demonstrated to efficiently to optimize complex constraints. This…
Taking inspiration from nature for meta-heuristics has proven popular and relatively successful. Many are inspired by the collective intelligence exhibited by insects, fish and birds. However, there is a question over their scalability to…
The aim of this paper is to highlight and explore a traditional problem, which is the minimum spanning tree, and finding the shortest-path in network routing, by using Swarm Intelligence. This work to be considered as an investigation topic…
Ant Colony Optimization algorithm is a magnificent heuristics technique based on the behavior of ants. Parallel computing is a means to achieve the desired results in commensurable execution time. Parallelization of Ant Colony Optimization…
This paper presents the Computing Networks (CNs) framework. CNs are used to generalize neural and swarm architectures. Artificial neural networks, ant colony optimization, particle swarm optimization, and realistic biological models are…
We show that memristive networks-namely networks of resistors with memory-can efficiently solve shortest-path optimization problems. Indeed, the presence of memory (time non-locality) promotes self organization of the network into the…
Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributions so that a complementary…
With the rapid upliftment of technology, there has emerged a dire need to fine-tune or optimize certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods…
Swarm intelligence has becoming a powerful technique in solving design and scheduling tasks. Metaheuristic algorithms are an integrated part of this paradigm, and particle swarm optimization is often viewed as an important landmark. The…
Inferring gene interaction network from gene expression data is an important task in systems biology research. The gene interaction network, especially key interactions, plays an important role in identifying biomarkers for disease that…
In this paper we propose DeepSwarm, a novel neural architecture search (NAS) method based on Swarm Intelligence principles. At its core DeepSwarm uses Ant Colony Optimization (ACO) to generate ant population which uses the pheromone…
Swarm intelligence is a discipline that studies the collective behavior that is produced by local interactions of a group of individuals with each other and with their environment. In Computer Science domain, numerous swarm intelligence…
Data mining focuses on discovering interesting, non-trivial and meaningful information from large datasets. Data clustering is one of the unsupervised and descriptive data mining task which group data based on similarity features and…
Swarm intelligence is a very powerful technique to be used for optimization purposes. In this paper we present a new swarm intelligence algorithm, based on the bat algorithm. The Bat algorithm is hybridized with differential evolution…
Hand-crafting effective and efficient structures for recurrent neural networks (RNNs) is a difficult, expensive, and time-consuming process. To address this challenge, we propose a novel neuro-evolution algorithm based on ant colony…
This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms. Social algorithms use multiple agents and the social…
Artificial life models, swarm intelligent and evolutionary computation algorithms are usually built on fixed size populations. Some studies indicate however that varying the population size can increase the adaptability of these systems and…