Particle swarm optimization: Difference between revisions

Revision as of 07:05, 27 February 2023

Revision as of 23:58, 24 July 2023

Line 95:

Another argument in favour of simplifying PSO is that [[metaheuristic]]s can only have their efficacy demonstrated [[empirical]]ly by doing computational experiments on a finite number of optimization problems. This means a metaheuristic such as PSO cannot be [[Program correctness|proven correct]] and this increases the risk of making errors in its description and implementation. A good example of this<ref name=tu04robust/> presented a promising variant of a [[genetic algorithm]] (another popular metaheuristic) but it was later found to be defective as it was strongly biased in its optimization search towards similar values for different dimensions in the search space, which happened to be the optimum of the benchmark problems considered. This bias was because of a programming error, and has now been fixed.<ref name=tu04corrections/>

Initialization of velocities may require extra inputs. The Bare Bones PSO variant<ref>{{Cite journalbook|last=Kennedy|first=James|date=2003|title=Bare Bones Particle Swarms|journal=Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706) |chapter=Bare bones particle swarms |date=2003|pages=80–87|doi=10.1109/SIS.2003.1202251|isbn=0-7803-7914-4|s2cid=37185749}}</ref> has been proposed in 2003 by James Kennedy, and does not need to use velocity at all.

Another simpler variant is the accelerated particle swarm optimization (APSO),<ref>X. S. Yang, S. Deb and S. Fong, [https://arxiv.org/abs/1203.6577 Accelerated particle swarm optimization and support vector machine for business optimization and applications], NDT 2011, Springer CCIS 136, pp. 53-66 (2011).</ref> which also does not need to use velocity and can speed up the convergence in many applications. A simple demo code of APSO is available.<ref>{{Cite web | url=http://www.mathworks.com/matlabcentral/fileexchange/?term=APSO | title=Search Results: APSO - File Exchange - MATLAB Central}}</ref>

Line 218:

<ref name="taherkhani2016inertia">{{cite journal | first1=M. | last1=Taherkhani | title=A novel stability-based adaptive inertia weight for particle swarm optimization | last2=Safabakhsh | first2=R. | journal=Applied Soft Computing | year=2016 | volume=38 | pages=281–295 | doi=10.1016/j.asoc.2015.10.004}}</ref>

<ref name="bratton2007">{{cite book | first1=Daniel | last1=Bratton | url=http://www.cil.pku.edu.cn/resources/pso_paper/src/2007SPSO.pdf | title=Defining a Standard for Particle Swarm Optimization | last2=Kennedy | first2=James | journaltitle=Proceedings of the 2007 IEEE Swarm Intelligence Symposium (SIS| 2007)chapter=Defining a Standard for Particle Swarm Optimization | pages=120–127 | year=2007| doi=10.1109/SIS.2007.368035 | isbn=978-1-4244-0708-8 | s2cid=6217309 }}</ref>

<ref name="Zambrano-Bigiarini2013">{{cite book | first1=M. | last1=Zambrano-Bigiarini | last2=Clerc | first2=M. | last3=Rojas | first3=R. | title=2013 IEEE Congress on Evolutionary Computation | chapter=Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements | last2=Clerc | first2=M. | last3=Rojas | first3=R. | journal=Evolutionary Computation (CEC), 2013 IEEE Congress on | pages=2337–2344 | year=2013| doi=10.1109/CEC.2013.6557848 | isbn=978-1-4799-0454-9 | s2cid=206553432 }}</ref>

<ref name="kennedy2002population">{{cite book | first1=J. | last1=Kennedy | last2=Mendes | first2=R. | title=PopulationProceedings structureof andthe particle2002 swarmCongress performanceon |Evolutionary last2=MendesComputation. CEC'02 (Cat. No.02TH8600) | first2chapter=R.Population structure and particle swarm performance | journal=Evolutionary Computation, 2002. CEC'02. Proceedings of the 2002 Congress on | volume=2 | pages=1671–1676 vol.2 | year=2002 | doi=10.1109/CEC.2002.1004493| isbn=978-0-7803-7282-5 | citeseerx=10.1.1.114.7988 | s2cid=14364974 }}</ref>

<ref name="oliveira2016communication">{{cite book | first1=M. | last1=Oliveira | title=Communication Diversity in Particle Swarm Optimizers | last2=Pinheiro | first2=D. | last3=Andrade | first3=B. | last4=Bastos-Filho | first4=C. | last5=Menezes | first5=R. | journal=International Conference on Swarm Intelligence | volume=9882 | year=2016 | pages=77–88 | doi=10.1007/978-3-319-44427-7_7| series=Lecture Notes in Computer Science | isbn=978-3-319-44426-0 | s2cid=37588745 | url=https://semanticscholar.org/paper/4a4bf82f6152d81a83a695fd7e063248f8d42e83 }}</ref>