Abstract

For solving optimization issues, this work provides a modified differential evolution (MDE) technique. The DE parameters, such as scaling factor (F) and crossover ratio (CR), are dynamically modified in this approach to get the global minimum value, which was previously thought to be set for the classic DE algorithm. On seven multi-modal benchmark test functions, the suggested technique's performance is validated. Particle swarm optimization (PSO), cuckoo search algorithm (CSA), lightning search algorithm (LSA), arithmetic optimization algorithm (AOA), black widow optimization algorithm (BWOA), and seagull optimization algorithm (SOA) are also compared in terms of mean, maximum, minimum, and standard deviation of the objective function value. In comparison to previous approaches, the simulation results show that the proposed approach finds the best solutions.

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