The Enhanced Firefly Algorithm Based on Modified Exploitation and Exploration Mechanism

Abstract

As a nature-inspired search algorithm, the Firefly algorithm (being a naturally outstanding search algorithm with few control parameters) may have a considerable influential performance. In this paper, we present a new firefly algorithm to address the parameter selection and adaptation strategy in the standard firefly algorithm. The proposed firefly algorithm introduces a modified exploration and exploitation mechanism, with adaptive randomness and absorption coefficients. The proposed method employs the adaptation of the randomness and absorption coefficients to be a function of time/iterations. Moreover, gray relational analysis advancing fireflies is used to allocate different information from appealing ones effectively. Standard benchmark functions are applied to verify the effects of these improvements and it is illustrated that, in most situations, the performance of the proposed firefly algorithm is superior to (or at least highly competitive with) the standard firefly algorithm, and state-of-the-art approaches in terms of performance.

Optimal design of symmetric fractional delay filter using firefly algorithm

“…FFA is based on two factors: light intensity variation and attractiveness. Suppose that a firefly's attractiveness is assessed by its brightness, which is linked with the objective function 25 . In this work, objective function is framed as to minimize the FD error.…”

“…FFA is an efficient algorithm and simple to implement 25 . FFA works for nonlinear, multimodal, and complex systems and has the potential of coming out of local optima.…”

“…However, instead of having global algorithm, the standard FFA lacks in the exploration ability due to premature convergence. Various techniques have been suggested to improve the performance of FFA such as Sababha et al 25 enhanced the FFA by modifying the parameter randomness and absorption coefficients and Saha et al 31 improved the FFA performance by modifying the randomization parameter. In this paper, inertia weight is incorporated in the FFA to improve the exploration and exploitation search ability, which is then called improved FFA.…”

Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

“…In this subsection, the average elapsed times of Table 3 are used to assess the computational efficiency of the reported algorithms. Such an algorithmic property of resource usage is quantified by the Computational Time Efficiency (CTE) metric defined as follows [34]:…”

“…by the Computational Time Efficiency (CTE) metric defined as follows [34]: According to the ET measures of problem (19), the CTE for each algorithm over the reported optimization criterion is summarized in Table 7. In terms of computational efficiency, it can be observed from these results that the proposed TEO algorithm attained the second rank for IAE and ITAE criteria and the third and fourth ranks for ISE and ITSE criteria, respectively.…”