A number of algorithms based on the evolutionary processing have been proposed forcommunication networks backbone such as Genetic Algorithm (GA). However, there has beenlittle work on the SWARM optimization algorithms such as Particle Swarm Optimization(PSO) for backbone topology design. In this paper, the performance of PSO on GA isdiscussed and a new algorithm as PSOGA is proposed for the net...

numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of multi-objective optimization (moo) had arisen several years ago. due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by swarm intelligence (si) techniques. particle swarm optimization (pso) has ...

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...

his paper presents the review on image edge detection by using particle swarm optimization and gravitational search algorithm. The paper present the various works done by particle swarm optimization and gravitational search algorithm in the field of digital image processing. By observing the previous works the two optimization algorithms particle swarm optimization algorithm and gravitational s...

In order to optimize the spectrum resource allocation in the orthogonal frequency division multiplexing system and minimize the transmitted power, this paper proposed a modified basic particle swarm optimization algorithm to allocate the subcarriers and bits of the orthogonal frequency division multiplexing system. The algorithm uses the average individual extreme value instead of individual ex...

Reactive power optimization is important to ensure power quality, improve system security, and reduce active power loss. So, this paper proposed parallel immune particle swarm optimization (PIPSO) algorithm. This algorithm makes basic particle swarm optimization (BPSO) and discrete particle swarm optimization (DPSO) to optimize in parallel, and improves the convergence capability of particle sw...

nowadays, by successful application of on time production concept in other concepts like production management and storage, the need to complete the processing of jobs in their delivery time is considered a key issue in industrial environments. unrelated parallel machines scheduling is a general mood of classic problems of parallel machines. in some of the applications of unrelated parallel mac...

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms ...

The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of...

the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...