Title: Study on the optimisation ability of natural selection mechanism

Authors: Huichao Liu; Fengying Yang

Addresses: College of Information Engineering, Huanghuai University, Zhumadian, HA, China ' College of Information Engineering, Huanghuai University, Zhumadian, HA, China

Abstract: In recent years, evolutionary algorithms have developed rapidly and become an important method for solving complex and nonlinear optimisation problems. Many evolutionary algorithms, such as differential evolution algorithm (DE), artificial bee colony algorithm (ABC) and brainstorming algorithm (BSO), adopt the natural selection principle of 'survival of the fittest' to determine the individuals of new populations. For a long time, researchers regard the selection operator as an important part of maintaining the evolution of the algorithm, and seldom distinguish the optimisation ability of the selection operator. In fact, the natural selection operator also has some abilities to optimise. For this reason, this paper takes DE algorithm as an example to construct different DE variants, and compares the optimisation results of them with the standard DE algorithm. Simulation results show that the new algorithm which only using natural selection can achieve certain optimisation results, meanwhile, DE algorithm which removing its greedy selection operator only has poor performance. This proves that natural selection operator has certain optimisation ability. Theoretical analysis shows that natural selection mechanism can determine a searching baseline during evolution and make exploration and exploitation fuse with each other.

Keywords: algorithm analysis; evolutionary algorithm; differential evolution algorithm; optimisation ability; natural selection mechanism.

DOI: 10.1504/IJIIDS.2019.102332

International Journal of Intelligent Information and Database Systems, 2019 Vol.12 No.1/2, pp.136 - 150

Received: 13 Sep 2018
Accepted: 02 Apr 2019

Published online: 18 Sep 2019 *

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