Analysis of the influence of population distribution characteristics on swarm intelligence optimization algorithms

Abstract

The distribution characteristic of populations is one of the main characteristics of population. The pattern of distribution characteristics determines the probability law of individual values. At the same time, the initialization of the population lays a foundation for the iterative process of the swarm intelligence optimization algorithm. To reveal the influence of population distribution characteristics on the swarm intelligence optimization algorithm, this paper proposes a variety of search strategies based on the populations with different distribution characteristics and analyzes the influence of population distribution characteristics on optimization process by comparing the test results of the optimization algorithm after implementing these strategies. First, a new population generator is designed that can transform the same initial population into a population with uniform and central peaking distributions. On this basis, the two kinds of populations are applied to the global and local search stages of the optimization process, and four different search strategies are formed. Among them, the global and local search strategies based on a uniformly distributed population are the traditional methods. Finally, the performance of the optimization algorithm using different search strategies is evaluated through 29 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. In addition, the algorithms are applied to solve the TSP problem. For CEC2017 in 100D, only 13 of the 29 test functions achieve the best optimization effect by using the traditional method, while the other 16 test functions achieve better search results by using the other three search strategies. The analysis shows that the population distribution characteristics have a great influence on the population optimization algorithm. The performance of the algorithm with different population distribution combination strategies is statistically superior to the traditional algorithm with a uniform distribution population, as revealed in the test functions ranging from 38.7% to 62.9% for different dimensions. By reconstructing populations with different distribution characteristics, the overall efficiency of the swarm intelligence optimization algorithm can be greatly improved.