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Particle swarm optimization (PSO) has shown its good performance in many optimization problems. This paper introduces a new approach called hybrid particle swarm optimization like algorithm (HPSO) with fine tuning operators to solve optimisation problems. This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimization like algorithm (ePSO). The performance...
Most researches on portfolio selection problems only consider simple boundary constraints, which can be solved by normalized method. In some cases, investors may purchase a stock under complex boundary constraints. At this situation, the normalized method is invalid. In this paper, a new approach is proposed to deal with complex boundary constraints. The main idea of the new approach is to modify...
Particle swarm optimization (PSO) has shown its good performance in many optimization problems. However, PSO could often easily fall into local minima because the particles could quickly converge to a position by the attraction of the best particles. Under this circumstance, all the particles could hardly be improved. This paper presents a hybrid PSO (AMPSO) to solve this problem by applying a novel...
Particle swarm optimization (PSO) has shown its fast search speed in many optimization and search problems. However, PSO easily fall into local optima on some multimodal and complicated problems. In this paper, an enhanced opposition-based PSO, called EOPSO, is proposed by combing an enhanced opposition-based learning and the standard PSO. The enhanced opposition provides solutions more closely to...
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