Improved Sparrow Search Algorithm with Elimination Mechanism
The traditional Sparrow Algorithm(SSA)has the advantages of high search accuracy and strong optimization ability,but such problems as premature convergence and easy to fall into the local optimal value in the iterative process also exist.To solve these problems,a Sparrow Search Algorithm(TESSA)with Tent chaotic mapping and last place elimination mechanism is proposed.The 2N segmented Tent chaotic mapping is used to initialize the population position.At the same time,the nonlinear last place elimination mechanism is introduced in the later stage of the algorithm iteration to improve its convergence speed and accuracy.After comparing the performance of TESSA with other four population intelligent algorithms in solving six benchmark functions,the convergence speed,optimization accuracy,standard error and other performance indicators of TESSA have obvious advantages.
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