Showing papers on "Firefly algorithm published in 2004"

Particle swarm optimization algorithm for single machine total weighted tardiness problem

Abstract: In This work we present a particle swarm optimization algorithm to solve the single machine total weighted tardiness problem. A heuristic rule, the smallest position value (SPV) rule, is developed to enable the continuous particle swarm optimization algorithm to be applied to all classes of sequencing problems, which are NP-hard in the literature. A simple but very efficient local search method is embedded in the particle swarm optimization algorithm. The computational results show that the particle swarm algorithm is able to find the optimal and best-known solutions on all instances of widely used benchmarks from the OR library.

Simulation of a new hybrid particle swarm optimization algorithm

Abstract: In this paper a new hybrid particle swarm optimization (PSO) algorithm is introduced which makes use of gradient information to achieve faster convergence without getting trapped in local minima. Simulation results comparing the standard PSO algorithm to the new hybrid PSO algorithm are presented. The De Jong test suite of optimization problems is used to test the performance of all algorithms. Performance measures to compare the performance of different algorithms are discussed. The new hybrid PSO algorithm is shown to converge faster for a certain class of optimization problems.

Solutions of Multi-Objective Optimization Problems Based on Particle Swarm Optimization

Abstract: Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced,as it is easily understood and realized. Through the improvement of the option modes of gBest and pBest of PSO algorithm,an optimization algorithm applied to solving multi-objective optimization problems is presented,and the search for Pareto optimal set of multi-objective optimization problems is implemented. The effectiveness of the algorithm is proved by experiments.

A modified particle swarm optimization algorithm

Abstract: A modified particle swarm optimization (PSO) algorithms is proposed. This method integrates the particle swarm optimization with the simulated annealing algorithm. It can solve the problem of local minimum of the particle swarm optimization, and narrow the field of search continually, so it has higher efficiency of search. This algorithm is applied to the function optimization problem and simulation shows that the algorithm is effective.

Application of generalized ant colony optimizaiton algorithm integrated with particle swarm optimization algorithm in economic dispatch of power system

Abstract: An optimized algorithm in which the general ant colony optimization (GACO) is integrated with particle swarm optimization (PSO) is proposed and is applied to economic dispatch of a complicated, non-concex and nonlinear power system. This integrated algorithm possesses large scale search capability of generalized ant colony algorithm and better local search capability of particle swarm algorithm at the same time. Under the condition of ensuring global convergence, high quality optimization solution can be searched by the proposed algorithm. The simulation results of several calculation examples show that the proposed algorithm is effective and feasible.

Multi-optimum programming based particle swarm optimization algorithm and its application in multi-dimensional & multi-modal function optimization

Abstract: In this paper, the knowledge of multi-optimum distribution state is introduced into general programming of the particle swarm movement to avoid falling into local optimums at the original stage of the computation. The algorithm is improved based on the modified particle algorithm and used to optimize the multi-dimensional and multi-optimum function. Simulation results show that, the general convergence character of the algorithm derived in this paper has better performance than the results derived based on the modified particle algorithm.

A New Swarm Optimization Method--Particle Swarm Optimization Algorithm

Abstract: A new optimizer-Particle Swarm Optimization (PSO) is introduced. The development in PSO is reviewed. Then, its applications in some areas are summarized. At last, the research and application of PSO in the future are given.