Uncovering the social interaction network in swarm intelligence algorithms

This is the final version. Available from the publisher via the DOI in this record.Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems, such as robustness, scalability, and flexibility. Yet, we fail to understand why swarm-based algorithms work well, and neither can we compare the various approaches in the literature. The absence of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here we address this gap by introducing a network-based framework—the swarm interaction network—to examine computational swarm-based systems via the optics of the social dynamics. We investigate the structure of social interaction in four swarm-based algorithms, showing that our approach enables researchers to study distinct algorithms from a common viewpoint. We also provide an in-depth case study of the Particle Swarm Optimization, revealing that different communication schemes tune the social interaction in the swarm, controlling the swarm search mode. With the swarm interaction network, researchers can study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction

Polynomial regression using a perceptron with axo-axonic connections

Social behavior is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these network

The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks

Particle Swarm Optimization

The procedure is to obtain the best solution for the certain parameters in the given network to satisfy every requirement for design by considering the smallest affordable cost will be considered as an optimization. Optimization traditional approach will have some constraints like, outcome of single-based, local optima convergence, problems in unknown search space. To overcome the above constraints, many research organizations have established various metaheuristics to search optimization solutions for unsolved issues. The main intend to explain the Particle Swarm Optimization algorithm (PSOA) is to explain the stochastic optimization approach basics. Motivation of this Particle Swarm Optimization algorithm (PSOA) is to develop a strong metaheuristic optimization solution which is inspired natural swarm behavior like schooling of birds and fishes. PSOA is a simplified social network simulation. The final intent of this PSOA is a graphical representation and graphical simulate smoothly but undefined bird or fish flock’s directions. Every bird’s vicinity of observability is restricted to some area. Though having many birds permits every bird in the swarm fitness function to be bigger surface concerned. Mathematically every Particle Swarm Optimization algorithm (PSOA) has associated with fitness value, velocity, and position. Memory of maintaining global fitness, best position, and global fitness value