Voronoi Tessellations based simple optimizer

Population diversity holds significant importance in determining the success of any evolutionary algorithm. It helps the algorithm in efficiently exploring the search space and identifying the promising region(s) containing global optimal solution(s). However, during the optimization procedure the population may lose its diversity, causing premature convergence in the algorithm and resulting in approximations to the sub-optimal solution(s). This paper proposes a Voronoi Tessellations based Simple Optimizer (VTSO) algorithm that utilizes a niche concept of Voronoi Tessellations (VTs) from the field of computational geometry to ensure a well-distributed population throughout the optimization procedure. It proposed an elite sampling mechanism that utilizes Lévy flights to aggressively explore the search space for locating potential optimal region(s), and the Differential Evolution (DE) algorithm to exploit these regions in order to approximate the global optimal solution(s). In addition, a population diameter-based switch is devised which activates itself when the algorithm detects premature convergence in the algorithm. Experiments are conducted on CEC14 and CEC17 benchmark test suit, and the proposed algorithm is compared with the existing state-of-the-art evolutionary algorithms. The results are competitive to recommend the VTSO algorithm as a new efficient and accurate optimizer for handling complex optimization problems. •This paper proposed a computational geometry approach for improving population diversity in evolutionary algorithms.•This includes a novel Voronoi Tessellations-based population generation method.•A levy flight and Differential Evolution assisted approach for updating population in the search space.•In addition, a population diameter-based switch is proposed to check the stagnation or premature convergence.