Faculty of Computer Science and Engineering

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5

The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

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End date: 2009Subject: search.filters.subject.data mining, heuristic, coefficients tuning, tabu search, quadratic assignment problem

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    Using data mining technique for coefficient tuning of an adaptive Tabu search
    (IEEE, 2007-09-09)
    ;
    Gjorgjevikj, Dejan
    ;
    Loshkovska, Suzana
    This paper describes the Adaptive Tabu Search algorithm (A-TS), an improved tabu search algorithm for combinatorial optimization. A-TS uses a novel approach for evaluation of the moves, incorporated in a new complex evaluation function. A new decision making mechanism triggers the evaluation function providing means for avoiding possible infinite loops. The new evaluation function implements effective diversification strategy that prevents the search from stagnation. It also incorporates two adaptive coefficients that control the influence of the aspiration criteria and the long-term memory, respectively. The adaptive nature of A-TS is based on these two adaptive coefficients. This article also presents a new data mining approach towards improving the performance of A-TS by tuning these coefficients. A-TS performance is applied to the Quadratic Assignment Problem. Published results from other authors are used for comparison. The experimental results show that A-TS performs favorably against other established techniques.
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    Item type:Publication,
    Using data mining technique for coefficient tuning of an adaptive Tabu search
    (IEEE, 2007-09-09)
    ;
    Gjorgjevikj, Dejan
    ;
    Loshkovska, Suzana
    This paper describes the Adaptive Tabu Search algorithm (A-TS), an improved tabu search algorithm for combinatorial optimization. A-TS uses a novel approach for evaluation of the moves, incorporated in a new complex evaluation function. A new decision making mechanism triggers the evaluation function providing means for avoiding possible infinite loops. The new evaluation function implements effective diversification strategy that prevents the search from stagnation. It also incorporates two adaptive coefficients that control the influence of the aspiration criteria and the long-term memory, respectively. The adaptive nature of A-TS is based on these two adaptive coefficients. This article also presents a new data mining approach towards improving the performance of A-TS by tuning these coefficients. A-TS performance is applied to the Quadratic Assignment Problem. Published results from other authors are used for comparison. The experimental results show that A-TS performs favorably against other established techniques.