Bull optimization algorithm based on genetic operators for continuous optimization problems

In this paper, the researcher proposes a new evolutionary optimization algorithm that depends on genetic operators such as crossover and mutation, referred to as the bull optimization algorithm (BOA). This new optimization algorithm is called the BOA because the best individual is used to produce offspring individuals. The selection algorithm used in the genetic algorithm (GA) is removed from the proposed algorithm. Instead of the selection algorithm, individuals initially produced attempt to achieve better individuals. In the proposed method, crossover operation is always performed by using the best individual. The mutation process is carried out by using individual positions. In other words, individuals are converged to the best individuals by using crossover operation, which aims to get the individual that is the better than the best individual in the mutation stage. The proposed algorithm is tested using 50 large continuous benchmark test functions with different characteristics. The results obtained from the proposed algorithm are compared with those of the GA, particle swarm optimization (PSO), differential evolution (DE), and the artificial bee colony (ABC) algorithm. The BOA, ABC, DE, PSO, and GA provided either optimum results or better results than other optimization algorithms in 42, 38, 34, 25s and 17 benchmark functions, respectively. According to the test results, the proposed BOA provided better results than the optimization algorithms that are most commonly used in solving continuous optimization problems.