This paper studies wireless sensor networks node deployment problem and proposes intelligent single particle optimizer based wireless sensor networks adaptive coverage. According to the probability model measure characteristic of wireless sensor nodes, the method adaptively determines the optimal deployment of sensor nodes using intelligent single particle optimizer, achieving sensor node based...

in this paper, a new enhanced version of the particle swarm optimization (pso) is presented. an important modification is made by adding probabilistic functions into pso, and it is named probabilistic particle swarm optimization (ppso). since the variation of the velocity of particles in pso constitutes its search engine, it should provide two phases of optimization process which are: explorati...

Cloud computing technology helps to resolve the problem in storage management by providing virtual resources end users. But, overloading of machines results degradation performances as well it increases energy consumption machines. Several techniques were used determine workloads cloud and then apply migration algorithm for efficient utilization resources. process depends on past outputs only f...

Abstract: This study proposes a novel chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm for solving shortterm hydrothermal scheduling problem with a set of equality and inequality constraints. In the proposed method, chaotic local search technique is employed to enhance the local search capability and convergence rate of the algorithm. In addition, a novel constraint handlin...

Particle swarm optimization (PSO) has been widely used mainly due to its simple concept and its ability to converge to reasonable solution fast. However, this algorithm is always inefficient while optimizing complex global optimization problems because it is easy to be trapped into local optima. Researches into the combination of evolutionary operators with PSO is one of the most significant an...

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 ...

An automatic least square support vector regression (LSSVR) optimization method that uses mixed kernel chaotic particle swarm (CPSO) to handle issues has been provided. The LSSVR model is composed of three components. position the particles (solution) in a sequence with good randomness and ergodicity initial characteristics taken into consideration first section. binary (PSO) used choose potent...

The objective of this paper is to investigate the enhancement of damping the low frequency oscillations via tuning of static synchronous compensator (STATCOM) damping controller. The problem of STATCOM based damping controller is formulated as an optimization problem according with the time domain-based objective function which is solved by a quantum-behaved particle swarm optimization (QPSO) t...

Particle Swarm Optimization (PSO) approach intertwined with Lozi map chaotic sequences to obtain Takagi–Sugeno (TS) fuzzy model for representing dynamical behaviours are proposed in this paper. The proposed method is an alternative for nonlinear identification approaches especially when dealing with complex systems that cannot always be modelled using first principles to determine their dynamic...

Particle swarm optimization algorithm is easy to reach premature convergence in the solution process, and fall into the local optimal solution. Aiming at the problem, this paper proposes a particle swarm optimization algorithm with chaotic mapping (CM-PSO). The algorithms uses chaotic mapping function to optimize the initial state of population, improve the probability of obtain optimal solutio...