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- W2389239600 abstract "Multi-objective particle swarm optimizers(MOPSOs) easily converge to a false Pareto front (the equivalent of a local optimum in single objective optimization), and converge slowly when applied to solve multi-objective optimization problems(MOPs). Therefore, this paper presents a self-adaptive multiobjective particle swarm optimizer based on e -domination ( e DMOPSO) to handle MOPs. In the e DMOPSO algorithm, the neighborhood of each particle is dynamically changed in terms of the performances of the particles, and the velocity of each particle is not adjusted by the best performing particle in its neighborhood, but by all particles in its neighborhood including itself. Finally, external archive is employed to store the nondominated solutions and e -dominance is applied to update non-dominated solutions in external archive. Simulation results show the effectiveness of the proposed e DMOPSO algorithm." @default.
- W2389239600 created "2016-06-24" @default.
- W2389239600 creator A5054808938 @default.
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- W2389239600 date "2011-01-01" @default.
- W2389239600 modified "2023-09-23" @default.
- W2389239600 title "Adaptive multi-objective particle swarm optimizer based on ε dominance" @default.
- W2389239600 hasPublicationYear "2011" @default.
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