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research-article

Locating and tracking multiple dynamic optima by a particle swarm model using speciation

Published: 01 August 2006 Publication History

Abstract

This paper proposes an improved particle swarm optimizer using the notion of species to determine its neighborhood best values for solving multimodal optimization problems and for tracking multiple optima in a dynamic environment. In the proposed species-based particle swam optimization (SPSO), the swarm population is divided into species subpopulations based on their similarity. Each species is grouped around a dominating particle called the species seed. At each iteration step, species seeds are identified from the entire population, and then adopted as neighborhood bests for these individual species groups separately. Species are formed adaptively at each step based on the feedback obtained from the multimodal fitness landscape. Over successive iterations, species are able to simultaneously optimize toward multiple optima, regardless of whether they are global or local optima. Our experiments on using the SPSO to locate multiple optima in a static environment and a dynamic SPSO (DSPSO) to track multiple changing optima in a dynamic environment have demonstrated that SPSO is very effective in dealing with multimodal optimization functions in both environments

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  • (2024)Improved shuffled Frog leaping algorithm with unsupervised population partitioning strategies for complex optimization problemsJournal of Combinatorial Optimization10.1007/s10878-023-01102-w47:2Online publication date: 11-Feb-2024
  • (2023)A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization ProblemsACM Transactions on Evolutionary Learning and Optimization10.1145/36048123:4(1-25)Online publication date: 14-Jun-2023
  • (2023)Producing Diverse Rashomon Sets of Counterfactual Explanations with Niching Particle Swarm Optimization AlgorithmsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590444(393-401)Online publication date: 15-Jul-2023
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  1. Locating and tracking multiple dynamic optima by a particle swarm model using speciation

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      Published In

      cover image IEEE Transactions on Evolutionary Computation
      IEEE Transactions on Evolutionary Computation  Volume 10, Issue 4
      August 2006
      96 pages

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      IEEE Press

      Publication History

      Published: 01 August 2006

      Author Tags

      1. Multimodal optimization
      2. optimization in dynamic environments
      3. particle swam optimization (PSO)
      4. tracking optima in dynamic environments

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      • (2024)Improved shuffled Frog leaping algorithm with unsupervised population partitioning strategies for complex optimization problemsJournal of Combinatorial Optimization10.1007/s10878-023-01102-w47:2Online publication date: 11-Feb-2024
      • (2023)A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization ProblemsACM Transactions on Evolutionary Learning and Optimization10.1145/36048123:4(1-25)Online publication date: 14-Jun-2023
      • (2023)Producing Diverse Rashomon Sets of Counterfactual Explanations with Niching Particle Swarm Optimization AlgorithmsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590444(393-401)Online publication date: 15-Jul-2023
      • (2023)History-Guided Hill Exploration for Evolutionary ComputationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325034727:6(1962-1975)Online publication date: 1-Dec-2023
      • (2022)Solving constrained problems with dynamic objective functions2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870354(1-8)Online publication date: 18-Jul-2022
      • (2022)Bayesian neuroevolution using distributed swarm optimization and tempered MCMC▪Applied Soft Computing10.1016/j.asoc.2022.109528129:COnline publication date: 1-Nov-2022
      • (2022)Enhanced SFLA with spectral clustering based co-evolution for 24 constrained industrial optimization problemsMultimedia Tools and Applications10.1007/s11042-022-13790-382:12(17853-17878)Online publication date: 22-Oct-2022
      • (2022)Rare-Event Detection by Quasi-Wang–Landau Monte Carlo Sampling with Approximate Bayesian ComputationJournal of Mathematical Imaging and Vision10.1007/s10851-019-00906-y61:9(1258-1275)Online publication date: 11-Mar-2022
      • (2022)Multimodal and multi-objective optimization algorithm based on two-stage search frameworkApplied Intelligence10.1007/s10489-021-02969-052:11(12470-12496)Online publication date: 1-Sep-2022
      • (2021)Optimal Train Speed Optimization under Several Safety Points by the PSO Algorithm2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504810(1333-1340)Online publication date: 28-Jun-2021
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