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- W2891755911 abstract "Dynamic multi-objective optimization problems (DMOPs) involve multiple optimization objectives which are in conflict with one another and change over time or environment. A novel dynamic multiple population particle swarm optimization algorithm based on decomposition and prediction (denoted as DP-DMPPSO) is proposed to solve DMOPs. Each objective is optimized by one population and each population shares their information with other populations. The populations evolve independently using a modified particle swarm optimization (PSO). An external archive is adopted to store the non-dominated solutions selected from all populations in the evolutionary process and the archive will be output as the final solution. A mechanism for updating the archive based on the objective space decomposition (DOS) is proposed. In addition, a population prediction mechanism is employed to accelerate the convergence to the true Pareto front. DP-DMPPSO is tested on a set of benchmark problems and compared with several state-of-the-art algorithms. The results show DP-DMPPSO is highly competitive for solving dynamic multi-objective optimization problems." @default.
- W2891755911 created "2018-09-27" @default.
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- W2891755911 date "2018-12-01" @default.
- W2891755911 modified "2023-10-14" @default.
- W2891755911 title "A dynamic multiple populations particle swarm optimization algorithm based on decomposition and prediction" @default.
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- W2891755911 doi "https://doi.org/10.1016/j.asoc.2018.08.015" @default.
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