This study presents a new evolutionary learning algorithm to optimize the parameters of the neural fuzzy classifier (NFC). This new evolutionary learning algorithm is based on a hybrid of bacterial foraging optimization and particle swarm optimization. It is thus called bacterial foraging particle swarm optimization (BFPSO). The proposed BFPSO method performs local search through the chemotacti...

In order to have clarity in the satellite images we have used Particle Swarm Optimization technique. When incorporated with traditional clustering algorithms, problems such as local optima and sensitivity to initialization, are reduced, thus exploring a greater area using global search. This segmented image is further classified using Kappa coefficient. Keywords— Particle Swarm Optimization(PSO...

this paper proposes a method to solve multi-objective problems using improved particle swarm optimization. we propose leader particles which guide other particles inside the problem domain. two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. the first one is based on the mean of the m optimal particles and the second one is based on appoin...

Memetic algorithms, one type of algorithms inspired by nature, have been successfully applied to solve numerous optimization problems in diverse fields. In this paper, we propose a new memetic computing model, using a hierarchical particle swarm optimizer (HPSO) and latin hypercube sampling (LHS) method. In the bottom layer of hierarchical PSO, several swarms evolve in parallel to avoid being t...

Numerous particle swarm optimization (PSO) algorithms have been developed for solving numerical problems in recent years. However, most of existing PSO only one search phase. There is no strengthened phase the well-performed particles, and also re-initialization exhausted particles. These issues may still restrict performance complex problems. In this paper, inspired by bee-foraging mechanism a...

In view of shortcomings of the particle swarm optimization algorithm such as poor late optimization ability and proneness to local optimization etc, this paper proposes an opposition-based learning particle swarm optimization (OBLPSO) algorithm for the optimization of logistics distribution routes, firstly, establishes a logistics distribution route optimization mathematical model, and then sol...

This article presents a fuzzy self-adaptive particle swarm optimization (FSAPSO) learning algorithm to extract a near optimum codebook of vector quantization (VQ) for carrying on image compression. The fuzzy self-adaptive particle swarm optimization vector quantization (FSAPSOVQ) learning schemes, combined advantages of the fuzzy inference method (FIM), the simple VQ concept and the efficient s...

blind source separation technique separates mixed signals blindly without any information on the mixing system. in this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. in these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. in order to evalu...