Journal ArticleDOI
A hybrid particle swarm optimization for job shop scheduling problem
D. Y. Sha,Cheng-Yu Hsu +1 more
TLDR
A hybrid particle swarm optimization (PSO) for the job shop problem (JSP) is proposed and the computational results show that the modified PSO performs better than the original design, and that the hybrid PSO is better than other traditional metaheuristics.About:
This article is published in Computers & Industrial Engineering.The article was published on 2006-12-01. It has received 307 citations till now. The article focuses on the topics: Particle swarm optimization & Multi-swarm optimization.read more
Citations
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Journal ArticleDOI
A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems
TL;DR: A novel set-based PSO (S-PSO) method for the solutions of some combinatorial optimization problems (COPs) in discrete space is presented and tested on two famous COPs: the traveling salesman problem and the multidimensional knapsack problem.
Journal ArticleDOI
Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition
TL;DR: Based on the proposed discrete framework, a multiobjective discrete particle swarm optimization algorithm is proposed to solve the network clustering problem and the decomposition mechanism is adopted.
Journal ArticleDOI
Particle swarm optimization: Hybridization perspectives and experimental illustrations
TL;DR: An attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO and three hybrid PSO algorithms are compared on a test suite of nine conventional benchmark problems.
Proceedings ArticleDOI
A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling
TL;DR: Experimental results show that the proposed Revised Discrete Particle Swarm Optimization (RDPSO) algorithm can achieve much more cost savings and better performance on make span and cost optimization.
Journal ArticleDOI
Metaheuristic Scheduling for Cloud: A Survey
TL;DR: This paper begins with a brief retrospect of traditional scheduling, followed by a detailed review of metaheuristic algorithms for solving the scheduling problems by placing them in a unified framework.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Journal ArticleDOI
Tabu Search—Part II
TL;DR: The elements of staged search and structured move sets are characterized, which bear on the issue of finiteness, and new dynamic strategies for managing tabu lists are introduced, allowing fuller exploitation of underlying evaluation functions.
Journal ArticleDOI
Future paths for integer programming and links to artificial intelligence
TL;DR: Four key areas of Integer programming are examined from a framework that links the perspectives of artificial intelligence and operations research, and each has characteristics that appear usefully relevant to developments on the horizon.
Book ChapterDOI
Parameter Selection in Particle Swarm Optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters.