Which algorithm is much better compared to particle swarm optimization?
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The algorithm that is much better compared to particle swarm optimization is the DPAMPSO algorithm proposed by Ding, Li, and Huang . This algorithm divides the population into a general subpopulation and an elite subpopulation, and uses a chaos-based inertia weight to balance global exploration and local exploitation abilities. The ordinary subpopulation uses an adaptive mutation strategy, while the elite subpopulation uses an exemplar learning strategy. The DPAMPSO algorithm outperforms other variations of PSO in terms of solving accuracy and convergence speed, as shown in experimental results . Another algorithm that shows promise is the GPSO algorithm proposed by Chuanjun and Ling . This algorithm modifies the standard PSO algorithm to enhance information exchange and sharing between particles, resulting in better search performance than the PSO algorithm .
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08 Dec 2022 | The paper does not mention any algorithm that is better than particle swarm optimization. |
| The paper does not mention any algorithm that is explicitly stated to be better than particle swarm optimization. | |
| The paper does not mention any algorithm that is explicitly stated to be better than particle swarm optimization. | |
08 Dec 2022 | The paper does not mention any algorithm that is better than particle swarm optimization. |
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