In this paper, an adaptivemodificationrateartificialbeecolony (AMR-ABC) algorithm is proposed by incorporating a novel adaptivemodificationrate to adaptively balance exploration and exploitation to determine wh...
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In this paper, an adaptivemodificationrateartificialbeecolony (AMR-ABC) algorithm is proposed by incorporating a novel adaptivemodificationrate to adaptively balance exploration and exploitation to determine which parameters (or the number of parameters) to be updated in a solution during each iteration. The performance of the AMR-ABC algorithm is compared to those the standard ABC algorithm and its two variants, and the Parks-McClellan algorithm for designing Type 3 (orders: 14, 26, and 38) and Type 4 (orders: 13, 25, and 37) linear phase FIR differentiators to evaluate their design capabilities. Design results have shown that the proposed AMR-ABC algorithm (i) outperforms four other design algorithms with the lowest p-norm error in each of the six differentiator designs and (ii) is robust such that the same p-norm error solution with an equiripple amplitude response in each of the six differentiator designs can be obtained by repeating a design with a different population of randomly generated initial solutions. The filter coefficients of six linear phase FIR differentiator designs are given as benchmarks to compare the p-norm error performance of the AMR-ABC algorithm to other algorithms. The AMR-ABC algorithm is attractive to be used for optimisation in this and other design problems.
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