In the spirit of Searle’s definition of weak and strong artificial intelligence, this paper presents a discussion on weak computational creativity in swarm intelligence systems. It addresses the concepts of freedom and constraint and their impact on the creativity of the underlying systems. An analogy is drawn on mapping these two ‘prerequisites’ of creativity onto the two well-known phases of ...

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of...

Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe comp...

Swarm intelligence is a relatively novel field. It addresses the study of the collective behaviors of systems made by many components that coordinate using decentralized controls and self-organization. A large part of the research in swarm intelligence has focused on the reverse engineering and the adaptation of collective behaviors observed in natural systems with the aim of designing effectiv...

Swarm-based algorithms are inspired by the behavior of some social living beings, such as ants, termites, birds, and fishes. Self-organization and decentralized control are remarkable features of swarm-based systems that, such as in nature, lead to an emergent behavior. Emergent behavior is a property that emerges through local interactions among system components and it is not possible to be a...

Research in bioinformatics necessitates the use of advanced computing tools for processing huge amounts of ambiguous and uncertain biological data. Swarm Intelligence (SI) has recently emerged as a family of nature inspired algorithms, especially known for their ability to produce low cost, fast and reasonably accurate solutions to complex search problems. In this chapter, we explore the role o...

Swarm Intelligence (SI) is a relatively new technology that takes its inspiration from the behavior of social insects and flocking animals. In this paper, we focus on two main SI algorithms: Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). An extension of ACO algorithm and a PSO algorithm has been implemented to solve the portfolio optimization problem, which is a continuous...

Nowadays, the high-dimensionality of data causes a variety problems in machine learning. It is necessary to reduce feature number by selecting only most relevant them. Different approaches called Feature Selection are used for this task. In paper, we propose method that uses Swarm Intelligence techniques. algorithms perform optimization searching optimal points search space. We show usability t...

Tayebeh Mostajabi, Javad Poshtan Abstract— A central topic of swarm intelligence is the investigation of different types of emergent collective behaviors in swarms. This article focus on the swarm intelligence applications in control and system identification. Particle swarm optimization (PSO), a novel population based stochastic optimizer with fast convergence speed and simple implementation a...