Ant colony algorithm has disadvantages such as long researching time and easily relapsing into local optimization. artificial fish-swarm algorithm is presented to conquer the disadvantages. The combination of the two ...
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ISBN:
(纸本)9783037852590
Ant colony algorithm has disadvantages such as long researching time and easily relapsing into local optimization. artificial fish-swarm algorithm is presented to conquer the disadvantages. The combination of the two algorithms is applied in function optimization to overcome the limitation that the ant colony algorithm does not fit to solve continuous space optimization. The tested function shows the effect of the method.
With uncertain amount of domestic waste door-to-door collection and transportation as the background, the problem of disturbance due to urgent order is studied. Combining the research methods of behavioral science to ...
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ISBN:
(纸本)9781538662434
With uncertain amount of domestic waste door-to-door collection and transportation as the background, the problem of disturbance due to urgent order is studied. Combining the research methods of behavioral science to human behavior perception and quantitative research methods in operational research, disturbance measurement based on prospect theory is provided, and disruption recovery model is built. One improved artificial fish-swarm algorithm based on adaptive view is designed to solve this problem. Compared with adaptive genetic algorithm, the convergence speed and convergence accuracy of the artificial fish-swarm algorithm are more capable. Moreover, through simulation experiment, the effectiveness of model and algorithm is verified.
To overcome the problem of low convergence speed and sensitivity to local convergence with the traditional artificial fish-swarm algorithm (AFSA) to handle complex functions, a novel compound evolutionary algorithm, c...
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ISBN:
(纸本)9780878492800
To overcome the problem of low convergence speed and sensitivity to local convergence with the traditional artificial fish-swarm algorithm (AFSA) to handle complex functions, a novel compound evolutionary algorithm, called AFS-EMPCEOA, was introduced which is combined artificial fish-swarm algorithm with the Elite Multi-parent Crossover Evolutionary Optimization algorithm (EMPCEOA) that is GuoTao algorithm improved by elite multi-parent crossover method. AFSEMPCEOA algorithm program with hybrid discrete variables was also developed. The computing example of mechanical optimization design shows that this algorithm has no special requirements on the characteristics of optimal designing problems, which has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence and high efficiency.
artificial Tribe algorithm (ATA) is a novel intelligent optimization algorithm based on the simulation of bionic intelligent optimization algorithm. This work discusses the main factors which influence the perform...
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artificial Tribe algorithm (ATA) is a novel intelligent optimization algorithm based on the simulation of bionic intelligent optimization algorithm. This work discusses the main factors which influence the performance of ATA, and compares the performance of ATA with that of genetic algorithm (GA), particle swarm optimization (PSO), and artificial fish-swarm algorithm (AFSA) for optimization multivariable functions. The simulation results showed that ATA outperforms the mentioned algorithms in global optimization problems.
Ant colony algorithm(ACA) is a novel simulated evolutionary algorithm, which is based on the process of ants in the nature searching for food. ACA has many good features in optimization, but it has the limitations of ...
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ISBN:
(纸本)9781424427239
Ant colony algorithm(ACA) is a novel simulated evolutionary algorithm, which is based on the process of ants in the nature searching for food. ACA has many good features in optimization, but it has the limitations of stagnation and poor convergence, and is easy to fall in local optimization. Pointing at these disadvantages, artificial fish-swarm algorithm(AFSA) is presented to conquer the disadvantages. The algorithm of rapid search capability of AFSA and the good search characteristics of ACO, and the convergent speed of the presented algorithm avoiding being trapped in local optimum is improved.
For solving the reactive power optimization problems, the ant colony algorithm in combination with artificialfish-swarm and differential evolution algorithms (FDEACO) was presented. Inspired by feeding, clustering an...
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For solving the reactive power optimization problems, the ant colony algorithm in combination with artificialfish-swarm and differential evolution algorithms (FDEACO) was presented. Inspired by feeding, clustering and rear-end behaviors of artificial fish-swarm algorithms, on the basic of the ant colony algorithm, I applied the rear-end behavior of the artificial fish-swarm algorithm to modify the solution of a feasible region searched by ant colony. The velocity of convergence to the optimal solution is accelerated. In the mechanism of pheromone update, the divergence of the differential evolution algorithm was introduced. A random disturbance is added, and the possibility of getting into local optimum is reduced. By reactive power optimization of IEEE 30-bus standard testing systems, and compared with other algorithms, analyzing the results show that the proposed algorithm is efficient and possesses a strong ability of global optimal searching.
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