Nature offers us many interesting and surprising examples in which the behaviour of a group of organisms seems to have some fundamentally distinct characteristics, not shared by the individuals in that group. Different species of birds flock together, and species of fish form schools, in groups that vary in size from a handful to many millions. Meanwhile it is wellknown thatmost species of ants...

Nature-inspiredmetaheuristic algorithms have become powerful and popular in computational intelligence and many applications. There are some important developments in recent years, and this special issue aims to provide a timely review of such developments, including ant colony optimization, bat algorithm, cuckoo search, particle swarm optimization, genetic algorithms, support vector machine, n...

Clustering represents the large datasets by a structured well defined number of clusters or prototypes. K-Means is a useful technique to data clustering which partitions the data into K-Clusters. However, the results of k-means algorithm are based on the selection of initial seeds and converge to local optimum solution. The Swarm Intelligence (SI) is an algorithm to apply many simple agents beh...

Agent-based routing in wireless ad hoc networks defines a set of rules that all the participating nodes follow. Routing becomes a collaboration between nodes, reducing computational and resource costs. Swarm Intelligence uses agent-like entities from insect societies as a metaphor to solve the routing problem. Certain insects exchange information about their activities and the environment in wh...

Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this paper, we carry out a critical analysis of these SI-based algorithms by analyzing their ways to mimic evolutionary operators. We also analyze the ways of achieving exploration and exploitation in algorithms ...

This work revolves around the applicability of Computational Intelligence in the field of Robotics. This application of Computational Intelligence in Swarm Robotics can make robots to get one step closer to human beings. Computational Intelligence is mainly modelled on the social behaviour of animals. Swarm Robotics is the new emerging field in the world. It is an alternative approach to roboti...

Swarm Intelligence is the part of Artificial Intelligence based on study of actions of individuals in various decentralized systems. The Bee Colony Optimization (BCO) metaheuristic has been introduced fairly recently as a new direction in the field of Swarm Intelligence. Artificial bees represent agents, which collaboratively solve complex combinatorial optimization problem. The chapter present...

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 ...

In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

The social insect metaphor for solving problems has become an emerging topic in the recent years. This approach emphasizes on direct or indirect interactions among simple agents. Swarm Intelligence is the collective behavior of decentralized [8], self-organized [4] system whereby the collective behavior of agent interacting locally with the environment causes coherent global pattern to emerge. ...