With the massive development of information and communications technologies, the need to optimize information processing power and increase accuracy is becoming very important. This paper presents the analysis of an intelligent Artificial Fish Swarm Algorithm (AFSA) that properly select optimization parameters more effectively. It is computational intelligent with ability to solve nonlinear high dimensional problems. It addresses problems of conventional AFSA migration into local minima using control parameters such as visual distance and step sizes. Performance of the algorithm was tested using a subset of applied mathematical optimization test functions such as Ackley, Cosine Mixture, Neumaier, Rosenbrock and Rastrigin functions. Numerical results show that the intelligent algorithm outperformed the standard algorithm in 4 out of the 5 test functions. This can be very useful in computationally intensive processes.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An Intelligence Artificial Fish Swarm Optimization Technique


    Beteiligte:


    Erscheinungsdatum :

    01.07.2019


    Format / Umfang :

    1087553 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A modified artificial fish swarm algorithm for unit commitment optimization

    Jin, Jing / Zhang, Zhaowei / Zhang, Lingling | British Library Conference Proceedings | 2023



    Artificial Fish Swarm-Inspired Whale Optimization Algorithm for Solving Multimodal Benchmark Functions

    Rahman, Imran / Mohamad-Saleh, Junita / Sulaiman, Noorazliza | TIBKAT | 2019


    Artificial Fish Swarm-Inspired Whale Optimization Algorithm for Solving Multimodal Benchmark Functions

    Rahman, Imran / Mohamad-Saleh, Junita / Sulaiman, Noorazliza | Springer Verlag | 2019