A Non-Dominated Sorting Genetic Algorithm with Adaptive Genetic Operator: NSGA-AGO

Unlike single objective optimization algorithm, solving multi-objective problem (MOP) is a complex task. The solution of a multi-objective optimization problem is a set of solutions, called the Pareto-optimal solution set. Multi-objective Optimization Genetic Algorithm (MOGA) is a commonly used evolutionary computation method for solving multi-objective optimization problems. Genetic algorithm has good global search ability in solving nonlinear, global, and other problems, while also possessing advantages such as distributed computing. However, the local optimization ability of genetic algorithm is poor, and there is a problem of getting stuck in the local optimal solution leading to premature convergence. At the same time, the search ability of genetic algorithm is significantly reduced in the later stage of evolution. To solve the above two issues, this paper introduces the adaptive genetic operator and proposes the Non-dominated Sorting Genetic Algorithm with Adaptive Genetic Operator (NSGA-AGO). According to the distribution characteristics of population fitness during the evolution process of genetic algorithms, the crossover and mutation probabilities of individuals in the population can be adaptively adjusted to maintain population diversity and improve the convergence ability of the algorithm. The experimental results show that the improved algorithm improves the convergence speed of the optimization process, reduces the standard deviation of convergence indicators, and has high stability.