Stochastic Fractal Search Algorithm Improved with Opposition-Based Learning for Solving the Substitution Box Design Problem

The main component of a cryptographic system that allows us to ensure its strength against attacks, is the substitution box. this can be validated by various metrics, one them being nonlinearity. To end, it essential develop design for boxes guarantee compliance with metric. In work, we implemented hybrid between stochastic fractal search algorithm in conjunction opposition-based learning. This supported sequential model configuration proper parameters configuration. We obtained high nonlinearity comparison other works based on metaheuristics and chaotic schemes. proposed box evaluated using bijectivity, strict avalanche criterion, nonlinearity, linear probability, differential probability bit-independence which demonstrate excellent performance approach.