In optimizing the particle swarm optimization (PSO) that inevitable existence problem of prematurity and the local convergence, this paper base on this aspects is put forward a kind of modified particle swarm optimization algorithm, take the gradient descent method (BP algorithm) as a particle swarm operator embedded in particle swarm algorithm, and at the same time use to attenuation wall (Dam...

With regard to the subject of sensor network node optimization, a strategy for wireless sensor network coverage optimization based on improved fish swarm algorithm is proposed in this paper. The improved algorithm is targeted on network coverage, node utilization rate and energy consumption balance, which makes use of the ergodic property of chaotic motion to overcome the disadvantage that arti...

in this paper we introduce an improvement in the path planning algorithm for the humanoid soccer playing robot which uses ferguson splines and pso (particle swarm optimization). the objective of the algorithm is to find a path through other playing robots to the ball, which should be as short as possible and also safe enough. ferguson splines create preliminary paths using random generated para...

High performance concrete is designed to meets special requirements such as high strength, high flowability, and high durability in large scale concrete construction. To obtain such performance many trial mixes are required to find desired combination of materials and there is no conventional way to achieve proper mix proportioning. Genetic algorithm is a global optimization technique based ...

A novel optimization algorithm, called accelerated particle swarm optimization (APSO), is proposed to be used for dynamic spectrum sensing in cognitive radio networks. While modified swarm-based optimization algorithms focus on slight variations of the standard mathematical formulas, in APSO, the acceleration variable of the particles is also considered in the search space. We show that the pro...

Extreme Learning Machine (ELM) is popular in batch learning, sequential and progressive due to its speed, easy integration, generalization ability. While, Traditional ELM cannot train massive data rapidly efficiently memory residence, high time space complexity. In ELM, the hidden layer typically necessitates a huge number of nodes. Furthermore, there no certainty that arrangement weights biase...

This paper presents a new variant of Particle Swarm Optimization algorithm named QPSO for solving global optimization problems. QPSO is an integrated algorithm making use of a newly defined, multiparent, quadratic crossover operator in the Basic Particle Swarm Optimization (BPSO) algorithm. The comparisons of numerical results show that QPSO outperforms BPSO algorithm in all the twelve cases ta...

Location problem of multi-distribution center is a kind of NP hard problem. To solve such problems, this paper proposes a chaos adaptive mutation particle swarm optimization algorithm. The algorithm uses the ergodic property of chaos to initialize the particle swarm to enhance the diversity of the population, according to the variance of population fitness to adjust the probability of mutation,...

in this paper, a new approach is proposed for the optimum design of single-phase induction motor. by using the classical design equations and the evolutionary algorithms such as genetic algorithms (ga), particle swarm optimization (pso) and modified particle swarm optimization (mpso), a single phase induction motor (spim) was designed with the maximum efficiency. the finite element method (fem)...