Abstract
In this study, improved antlion optimization algo-rithm (IALO) is presented. The antlion optimization algorithm (ALO) is an heuristic optimization algorithm based on modeling random walks of ants and hunting ants by antlions. The random walk model of ALO and the IALO revealed by improvements in the selection method have been tested with benchmark functions with different characteristics from the literature. The proposed algorithm is compared with different metrics (accuracy, optimality, best average solution, CPU time, etc.) with particle swarm optimization (PSO), artificial bee colony (ABC) and ant lion optimization algorithm (ALO). The IALO algorithm has an optimal result in a shorter time than the ALO, and it is understood that it is more successful than the ALO in the tests for high dimensional benchmark functions.
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