| Abstrakt: |
Aiming at the issues of low solution precision and weak local search ability of the basic chicken swarm optimization algorithm, an Adaptive Dynamic Learning Chicken Swarm Optimization algorithm(ADLCSO) is proposed in this paper. The algorithm adaptively updates each rooster's position using a reverse foraging mechanism and adds a non-linear decreasing learning factor to dynamically adjust the update step size of the rooster's position, so as to enhance the ability of the population to jump out of local extremum, and thus to improve the convergence speed and solution precision of the algorithm. In addition, a population similarity index based on the difference of fitness values between individuals is proposed, and then is used to adaptively adjust the position of each hen in order to inhibit the attenuation of population diversity and further improve the solution precision of the algorithm. Through the simulation experiments on 12 classical test functions, the results show that the ADLCSO algorithm is superior to other comparison algorithms in terms of convergence speed, solution precision, stability and the ability to solve high-dimensional problems. [ABSTRACT FROM AUTHOR] |
