Many optimization problems in real world are dynamic in the sense that the global optimum value and the shape of fitness function may change with time. The task for the optimization algorithm in these environments is to find global optima quickly after the change in environment is detected. In this paper, we propose a new hybrid model of particle swarm optimization and cellular automata which a...

NNPC has been used widely to control nonlinear systems. However traditional gradient decent algorithm (GDA) needs a large computational cost, so that NNPC is not acceptable for systems with rapid dynamics. To apply NNPC in fast control of mobile robots, the paper proposes an improved optimization technique, particle swarm optimization with controllable random exploration velocity (PSO-CREV), to...

This paper introduces the Particle Swarm Optimization (PSO) algorithm to solve the optimal network reconfiguration problem for power loss reduction. The PSO is a relatively new and powerful intelligence evolution method for solving optimization problems. It is a population-based approach. The PSO was inspired from natural behavior of the bees on how they find the location of most flowers. The p...

This paper presents a modification of tlte panicle swarm optimization algorithm (PSO) intended to combat the problem ofpremature convergence observed in many applications of PSO. The proposed new algorithm moves particles towards neorby particles of higher fitness, instead of amacting each panicle towards just the best position discovered so far by any particle. This is accomplished by using th...

The parts optimization are very important for scroll compressor design. According to existing problems of current optimization algorithm and actual optimization problems, the improved optimization algorithm—genetic-particle swarm optimization (GA-PSO) is proposed for scroll plate optimization. The optimization method integrates crossover of genetic algorithm (GA) and evolutionary mechanism of p...

Particle swarm optimization (PSO) has been employed on several optimization problems, including the clustering problem. PSO has also been employed in the clustering of data of different structure and dimensionality. In this paper it is employed in the clustering of nucleic acid sequences. The application of clustering, as a statistical tool, in the analysis of data of varied complexity has been...

Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal Optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide varie...

There is a concept of PSO algorithm which is very much efficient and effective with optimized result popular these days in so many streams. This paper provides the run of PSO on cloudSim with comparison analysis from different simulators. The paper covers five different sections in details. First is introduction towards the topic. Then cloud computing with cloudSim comes as another section whic...

-A novel approach for the implementation of Nonlinear Model Predictive Control (NMPC) using Particle Swarm Optimization (PSO) technique is proposed. Two different approaches are made in the PSO algorithms, Random PSO (RPSO) and knowledge based PSO (KPSO) for the determination of optimum controller gain in MPC structure In order to test the performance of the proposed PSO based MPC system a nonl...