Weightless Swarm Algorithm (WSA) for dynamic optimization problems

T. O. Ting*, Ka Lok Man, Sheng Uei Guan, Mohamed Nayel, Kaiyu Wan

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

24 Citations (Scopus)


In this work the well-known Particle Swarm Optimization (PSO) algorithm is applied to some Dynamic Optimization Problems (DOPs). The PSO algorithm is improved by simplification instead of introducing additional strategies into the algorithm as done by many other researchers in the aim of improving an algorithm. Several parameters (w, Vmax, Vmin and c 2) are being excluded from the conventional PSO. This algorithm is called Weightless Swarm Algorithm (WSA) as the prominent parameter, inertia weight w does not exist in this proposed algorithm. Interestingly, WSA still works effectively via swapping strategy found from countless trials and errors. We then incorporate the proven clustering technique from literature into the framework of the algorithm to solve the six dynamic problems in literature. From the series of tabulated results, we proved that WSA is competitive as compared to PSO. As only one parameter exists in WSA, it is feasible to carry out parameter sensitivity to find the optimal acceleration coefficient, c 1 for each problem set.

Original languageEnglish
Title of host publicationNetwork and Parallel Computing - 9th IFIP International Conference, NPC 2012, Proceedings
Number of pages8
Publication statusPublished - 2012
Event9th IFIP International Conference on Network and Parallel Computing, NPC 2012 - Gwangju, Korea, Republic of
Duration: 6 Sept 20128 Sept 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7513 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th IFIP International Conference on Network and Parallel Computing, NPC 2012
Country/TerritoryKorea, Republic of


  • Dynamic optimization
  • Swapping
  • Weightless swarm

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