A Multi-objective Particle Swarm Optimization Based on Decomposition

Decomposition is a classic method in traditional multi-objective optimization problems (MOPs). However, so far it has not yet been widely used in multi-objective particle swarm optimization (MOPSO). This paper proposes a MOPSO based on decomposition strategy (MOPSO-D), in which MOPs is decomposed into a number of scalar optimization sub-problems by a set of even spread weight vectors, and each sub-problem is optimized by a particle (here, it is viewed as a sub-swarm) personal history best position (pbest) and global best position in the its all neighbors (gbest) in a single run. By computing the Euclidean distances between any two weight vectors corresponding to a particle, the neighborhood identification strategy of each particle is assigned. The method of decomposition inherited the traditional method merits and makes MOPSO-D have lower computational complexity at each generation than NSMOPSO and OMOPSO. Simulation experiments on multi-objective 0-1 knapsack problems and continuous multi-objective optimization problems show MOPSO-D outperforms or performs similarly to NSMOPSO and OMOPSO.

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