Image compression is one of the most important and successful applications of the wavelet transform. Images will have huge amount of information which require more storage space, and high transmission bandwidths and more transmission time. So it is important to compress the image by eliminating the redundant information in the image and encoding only the essential information. As multimedia inf...

This paper presents a swarm intelligence based approach for optimal scheduling in sensor networks. Sensors are characterized by their transaction times and interdependencies. In the presence of interdependencies the problem of optimal scheduling to minimize the overall transaction/response time is modeled as a graph partitioning problem. Graph partitioning problem is a well known NPcomplete pro...

The paper is based on feed forward neural network (FFNN) optimization by particle swarm intelligence (PSI) used to provide initial weights and biases to train neural network. Once the weights and biases are found using Particle swarm optimization (PSO) with neural network used as training algorithm for specified epoch, the same are used to train the neural network for training and classificatio...

Particle swarm optimization method is based on artificial intelligence technique. It is an optimization method that was developed in 1995 by Eberhart and Kennedy based on the social behaviors of fish schooling or birds flocking. By increasing the overall rate of fault detection, a greater number of errors can be found more rapidly in the code. Particle , fitness function , local best , global b...

This paper systematically presents the Swarm Intelligence (SI) methods for optimization of multiple and many objective problems. The fundamental difference of Multiple andMany Objective Optimization problems have been studied very rigorously. The three forefront swarm intelligence methods, i.e., Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony Optimiza...

The SWarm Intelligence-based Reinforcement Learning (SWIRL) method is proposed in this paper to efficiently generate Artificial Neural Network (ANN) based solutions to various problems. Basically, two swarm intelligence based algorithms are combined together in SWIRL to train the ANN models. Ant Colony Optimization (ACO) is applied to optimize ANN topology, while Particle Swarm Optimization (PS...

Interactions in swarm robotic search are explored for intelligence emergence based on Extended Particle Swarm Optimization (EPSO) model. For this end, the best combination of proper properties in typical versions of PSO is transferred to swarm robotic search. Synchronous / asynchronous communication modes and respective control strategies under conditions of parallel distributed control are com...

The catfish particle swarm optimization (CatfishPSO) algorithm is a novel swarm intelligence optimization technique. This algorithm was inspired by the interactive behavior of sardines and catfish. The observed catfish effect is applied to improve the performance of particle swarm optimization (PSO). In this paper, we propose fuzzy CatfishPSO (F-CatfishPSO), which uses fuzzy to dynamically chan...

So far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has been done. Moreover to overcome the problem of i...

We present an algorithm that is inspired by theoretical and empirical results in social learning and swarm intelligence research. The algorithm is based on a framework that we call incremental social learning. In practical terms, the algorithm is a hybrid between a local search procedure and a particle swarm optimization algorithm with growing population size. The local search procedure provide...