Artificial fish swarm algorithm (AFSA) is one of the critical intelligent algorithms. In this paper, authors decide to enhance AFSA via diversity operators (AFSA-DO). The will be producing more diverse solutions for obtain reasonable resolutions. AFSA-DO has been used solve flexible job shop scheduling problems (FJSSP). However, FJSSP a significant problem in domain optimization and operation r...

In this paper, we show the functional similarities between Meta-heuristics and the aspects of the science of life (biology): (a) Meta-heuristics based on gene transfer: Genetic algorithms (natural evolution of genes in an organic population), Transgenic Algorithm (transfers of genetic material to another cell that is not descending); (b) Meta-heuristics based on interactions among individual in...

Abstract In order to improve the existing problems of artificial fish swarm algorithm, algorithm is improved by using characteristics RNA computing, and RNA-artificial proposed. Since cold-chain food has perishable quality can generate a large number carbon emissions during its transportation, this paper establishes model for optimizing cold chain low logistics location in Beijing-Tianjin-Hebei...

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm ...

Abstract A new stochastic resonance method that is based on a fast artificial fish swarm algorithm has been proposed in an effort to address the adaptive parameter-induced for weak signal detection’s slow convergence time. The target evaluation function system output signal-to-noise ratio. of scale transformation and amplitude compression used pre-process high frequency large parameter signals....

An improved fish-swarm algorithm is proposed for resource scheduling in cloud computing. The improvement process is divided into the following aspects, The experimental results show that the improved fish-swarm algorithm is more suitable for resource scheduling in cloud computing because of its high accuracy, short iterative process and fast resource scheduling time.