An exploration of the literature on the use of 'swarm intelligence-based techniques' for public service problems

被引:5
作者
Dereli, Tuerkay [1 ]
Seckiner, Serap Ulusam [1 ]
Das, Guelesin Sena [2 ]
Gokcen, Hadi [3 ]
Aydin, Mehmet Emin [4 ]
机构
[1] Gaziantep Univ, Fac Engn, Dept Ind Engn, TR-27310 Sehitkamil, Gaziantep, Turkey
[2] TUBITAK, Sci & Technol Res Council Turkey, EU Framework Programs Natl Off, TR-06100 Kavaklidere, Turkey
[3] Gazi Univ, Fac Engn & Architecture, Dept Ind Engn, TR-06570 Ankara, Turkey
[4] Univ Bedfordshire, Dept Comp & Informat Syst, Inst Res Applicable Comp, Luton LU1 3JU, Beds, England
关键词
swarm intelligence; public services; ant colony optimisation; ACO; particle swarm optimisation; PSO; bee(s) algorithm; ANT COLONY OPTIMIZATION; MATING OPTIMIZATION; SCHEDULING PROBLEM; SEARCH ALGORITHM; SYSTEMS; DESIGN; POWER; COMBINATORIAL; VARIANTS;
D O I
10.1504/EJIE.2009.027034
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The importance of studying public service systems and finding robust solutions to the problems encountered in public service management has increased considerably over the past decade. One of the main objectives is to find acceptable solutions to Public Service Problems (PSPs) within an affordable period of time. However, many PSPs remain difficult to solve within a reasonable time due to their complexity and dynamic nature. This requires solving PSPs with techniques which provide efficient algorithmic solutions. There has been increasing attention in the literature to solving PSPs through the use of Swarm Intelligence-Based Techniques (SIBTs) like ant colony optimisation, particle swarm optimisation, Bee(s) Algorithm (BA), etc. This paper presents a review of Swarm Intelligence (SI) applications in public services (including PSPs in specific application areas), as well as the models and SI algorithms that have been reported in the literature.
引用
收藏
页码:379 / 423
页数:45
相关论文
共 165 条
[1]   Optimal power flow using particle swarm optimization [J].
Abido, MA .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2002, 24 (07) :563-571
[2]   Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation [J].
Afshar, A. ;
Bozorg-Haddad, Omid ;
Marino, M. A. ;
Adams, B. J. .
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2007, 344 (05) :452-462
[3]  
AHUJA RK, 2003, 446403120 MIT SLOAN
[4]   PSO-based algorithm for home care worker scheduling in the UK [J].
Akjiratikarl, Chananes ;
Yenradee, Pisal ;
Drake, Paul R. .
COMPUTERS & INDUSTRIAL ENGINEERING, 2007, 53 (04) :559-583
[5]  
ALAM S, 2006, ALAR TECHNICAL REPOR
[6]  
ALAM S, 2005, P SPIE INT SOC OPTIC, V6039
[7]   A survey of particle swarm optimization applications in power system operations [J].
Alrashidi, M. R. ;
El-Hawary, M. E. .
ELECTRIC POWER COMPONENTS AND SYSTEMS, 2006, 34 (12) :1349-1357
[8]   A new discrete particle swarm optimization approach for the single-machine total weighted tardiness scheduling problem with sequence-dependent setup times [J].
Anghinolfi, Davide ;
Paolucci, Massimo .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2009, 193 (01) :73-85
[9]   A novel approach for optimal chiller loading using particle swarm optimization [J].
Ardakani, A. Jahanbani ;
Ardakani, F. Fattahi ;
Hosseinian, S. H. .
ENERGY AND BUILDINGS, 2008, 40 (12) :2177-2187
[10]   Hybrid heuristics for examination timetabling problem [J].
Azimi, ZN .
APPLIED MATHEMATICS AND COMPUTATION, 2005, 163 (02) :705-733