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Normative fish swarm algorithm (NFSA) for optimization
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DOI：10.1007/s00500-019-04040-0.png)
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Abstract
En 中文
In this paper, a swarm-based optimization algorithm, normative fish swarm algorithm (NFSA) is proposed as an effective global and local search technique to obtain effective global optima at superior convergence speed. Artificial fish swarm algorithm is a recent swarm-based algorithm that imitates the behavior of fish swarm in the real environment. Many improvements and modifications have been proposed regularly on fish swarm algorithm to improve the performance of optimization, but to date, existing fish swarm algorithms have not yet obtained a global optimum at extremely superior convergence rates. Hence, there still remains a huge potential for the development of fish swarm algorithm. NFSA hybridizes the characteristics of PSOEM-FSA with the normative knowledge as the complementary guidelines for more accurate and precise global optimum approaching. NFSA further improves the adaptive parameters in term of visual and step to balance the contradiction between the exploration and exploitation processes. Random initialization of the initial population is introduced to spread out the solution candidates of artificial fishes over the solution space. For the purpose of experiments, ten benchmark functions have been used in the evaluation process. The proposed algorithm is then compared with other related algorithms published in the literature. The results proved that the proposed NFSA achieved superior results in terms of convergence rate and best optimal solution on a majority of the tested benchmark functions in comparison with other comparative algorithms.
Keywords:
Artificial fish swarm algorithm (AFSA)
Adaptive visual and step
Particle swarm optimization (PSO)
PSO with extended memory (PSOEM)
Normative knowledge
Cultural algorithm (CA)
t test
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