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Artificial Fish Swarm Algorithm (AFSA) is a new swarm intelligence optimization algorithm which is designed to find the single optimal solution for a given problem. But in some practical applications, the global optimal solution and some near optimal solutions are both needed. So a novel Niche Quantum Artificial Fish Swarm Algorithm (NQAFSA) is proposed in this paper to solve these problems. The quantum...
The job shop scheduling problem (JSSP) is a sort of famous combination optimization problems which is difficult to solve using the conventional optimization algorithm. Artificial Fish Swarm Algorithm (AFSA) proves to be powerful in solving some optimization problems and the AFSA has the advantages of not strict to parameter setting, strong robustness, fast convergence and so on. In this paper, the...
This paper presents a novel stochastic approach called the simulated annealing-artificial fish swarm algorithm (SA-AFSA) for solving some multimodal problems. The proposed algorithm incorporates the simulated annealing (SA) into artificial fish swarm algorithm (AFSA) to improve the performance of the AFSA. The hybrid algorithm has the following features: the hybrid algorithm maintains 1) the strong...
In order to improve the global search ability and the convergence speed of the Artificial Fish Swarm Algorithm (AFSA), a novel Quantum Artificial Fish Swarm Algorithm (QAFSA) which is based on the concepts and principles of quantum computing, such as the quantum bit and quantum gate is proposed in this paper. The position of the Artificial Fish (AF) is encoded by the angle in [0, 2π] based on the...
Artificial fish swarm algorithm (AFSA) is a kind of swarm intelligence algorithms, which has the features of not strict to parameter setting, insensitive to initial values, strong robustness and so on. But the precision can not be very high and artificial fish (AF) often suffers the problem of being trapped in local optima. Especially when the objective function is a multimodel function, this problem...
An improved artificial fish swarm algorithm (IAFSA) is proposed, and its complexity is much less than the original algorithm (AFSA) because of a new proposed fish behavior. Based on IAFSA, two novel algorithms for data clustering are presented. One is the improved artificial fish swarm clustering (IAFSC) algorithm, the other is a hybrid fuzzy clustering algorithm that incorporates the fuzzy c-means...
Artificial fish swarm algorithm (AFSA) is a novel intelligent optimization algorithm. It has many advantages, such as good robustness, global search ability, tolerance of parameter setting, and it is also proved to be insensitive to initial values. However, it has some weaknesses as low optimizing precision and low convergence speed in the later period of the optimization. In this paper, an improved...
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