Work

Hybrid particle swarm optimization algorithm for flow shop scheduling problem

Year: 2012

Type: article

Abstract: A Hybrid Particle Swarm optimization Algorithm(HPSA)is presented to solve the flow shop scheduling problem with total flowtime minimization. The initial particle swarms are generated by heuristics. It combines the particle swarm optimization algorithm, genetic operators and local search strategies together. Taillard’s benchmark program is used to generate a large number of random instances. The experimental results indicate that the quality of solutions is improved using HPSA through the improvement of the option mode of population and a bigger searching scope. The performance of the proposed method is superior to the efficient heuristics currently used and hybrid tabu search algorithms. The average relation percentage deviation and standard deviation are reduced evidently, and the percent of optimal solutions increases obviously.

Source: Computer Engineering and Applications Journal

Author: Zhao Cheng

Institution: Anhui Normal University

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W2392470126

FWCI: 0.63

Cites:

Cited by: 1

Related to: 10