Synthetic aperture radar (SAR) has been recognized as a powerful tool for geological feature detection. This work introduces a new approach using Particle Swarm Optimization automatically detected geological features from PALSAR SAR data. The result shows that the new formula based on Particle Swarm Optimization can be delineated lineament features in PALSAR data. The new approach using Particl...

Particle swarm optimization (PSO) has shown to be a robust and efficient optimization algorithm therefore PSO has received increased attention in many research fields. This paper demonstrates the feasibility of applying the Dynamic Inertia Weight Particle Swarm Optimization to solve a Non-Polynomial (NP) Complete puzzle. This paper presents a new approach to solve the Nonograms Puzzle using Dyn...

The dogleg severity is one of the most important parameters in directional drilling. Improvement of these indicators actually means choosing the best conditions for the directional drilling in order to reach the target point. Selection of high levels of the dogleg severity actually means minimizing well trajectory, but on the other hand, increases fatigue in drill string, increases torque and d...

In the last decennium, particle swarms have received considerable attention in the fields of optimization and control. Inspired by swarms of social animals, such as birds, fish, and termites, simple behavior on the local level has been shown to result in useful complex behavior on the global level. Particle Swarm Optimization has proven to be a very powerful optimization heuristic, and swarm ag...

The past decades have seen the great progress pattern recognition and image understanding, motivated by a wide range of real world applications. The previous approaches mostly based on the feature extraction and recognition methods, and usually suffer from the problem of weak discrimination power. In this paper, we propose a complex model based on particle swarm and neural network pattern recog...

Vehicle routing problem is a NP-hard problem in combination optimization problem, It is influenced by many factors, and the process is complex. However, the search space of particle swarm algorithm is limited and prone to precocious phenomenon. In this paper, we based on the original particle swarm optimization, present a new way of location update algorithm (VPSO).VPSO in the process of solvin...

Error Correcting Output Codes reveal an efficient strategy in dealing with multi-class classification problems. According to this technique, a multi-class problem is decomposed into several binary ones. On these created sub-problems we apply binary classifiers and then, by combining the acquired solutions, we are able to solve the initial multiclass problem. In this paper we consider the optimi...

The focal inadequacy of modern wireless sensor networks is constrained energy resources of sensors. Therefore, Energy efficient clustering techniques and routing algorithms must be deployed to intensify the durability of wireless sensor networks. The research of an improved particle swarm optimization method for wireless sensor networks is presented in this paper. In order to conquer the inadeq...

The implementation of meta-heuristic algorithm in defining the optimal solution for distribution network reconfiguration has been addressed by many researchers over the years. Nonetheless, the solution in minimizing the main objective which is total power losses could not be solved until today. In this study, the hybridization of evolutionary particle swarm optimization has been modified in ord...

This paper introduces a modified particle swarm optimizer (PSO) called the Multi-Species Particle Swarm Optimizer (MSPSO) for locating all the global minima of multimodal functions. MSPSO extend the original PSO by dividing the particle swarm spatially into a multiple cluster called a species in a multi-dimensional search space. Each species explores a different area of the search space and tri...