Human Eye Vision Algorithm (HEVA): A Novel Approach for the Optimization of Combinatorial Problems

In today's optimization paradigm, reducing time to obtain the optimal solution for a combinatorial optimization problem is a significant challenge. The proposed algorithm is based upon the power of human eye vision for minimizing the time needed to achieve the best solution to a combinatorial optimization problem. To improve the attribute position in a particular domain to reach their target in a foggy climate, researchers used the population's visualization ability to see a way to traverse and gain adequate heuristic information. The suggested algorithm has been empirically shown to work on Howard H. Rosen's hypothesis, which was formulated in 1960. When added to the fitness function of Rosen rock using MATLAB 8.5-R2015a, this new optimization algorithm minimizes the cost from 100 to 54.6342.