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Information Journal Paper

Title

GRADIENT-BASED ANT COLONY OPTIMIZATION FOR CONTINUOUS SPACES

Pages

  33-45

Abstract

 A novel version of ANT COLONY Optimization (ACO) ALGORITHMS for solving continuous space problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to the case of continuous space is implemented within the general framework. The stigmergic communication is simulated through considering certain direction vectors which are memorized. These vectors are normalized gradient vectors that are calculated using the values of the evaluation function and the corresponding values of object variables.The proposed Gradient-based Continuous ANT COLONY Optimization (GCACO) method is applied to several benchmark problems and the results are compared and contrasted with other population-based ALGORITHMS such as EVOLUTIONARY Strategies (ES), EVOLUTIONARY Programming (EP), and Genetic ALGORITHMS (GA). The results obtained from GCACO compare satisfactorily with those of other ALGORITHMS and in some cases are superior in terms of accuracy and computational demand.

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    APA: Copy

    EFTEKHARI, MEHRDAD, DAEI, B., & KATEBI, S.A.D.. (2006). GRADIENT-BASED ANT COLONY OPTIMIZATION FOR CONTINUOUS SPACES. ESTEGHLAL, 25(1), 33-45. SID. https://sid.ir/paper/5916/en

    Vancouver: Copy

    EFTEKHARI MEHRDAD, DAEI B., KATEBI S.A.D.. GRADIENT-BASED ANT COLONY OPTIMIZATION FOR CONTINUOUS SPACES. ESTEGHLAL[Internet]. 2006;25(1):33-45. Available from: https://sid.ir/paper/5916/en

    IEEE: Copy

    MEHRDAD EFTEKHARI, B. DAEI, and S.A.D. KATEBI, “GRADIENT-BASED ANT COLONY OPTIMIZATION FOR CONTINUOUS SPACES,” ESTEGHLAL, vol. 25, no. 1, pp. 33–45, 2006, [Online]. Available: https://sid.ir/paper/5916/en

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