TY  - EJOU
AU  - Zheng, Li Ming 
AU  - Luo, Jun Ting 

TI  - Handling Stagnation in Differential Evolution Using Elitism Centroid-Based Operations
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 84
IS  - 2
SN  - 1546-2226

AB  - Differential evolution (DE) algorithms are simple and efficient evolutionary algorithms that perform well in various optimization problems. Unfortunately, they inevitably stagnate when differential evolutionary algorithms are used to solve complex problems (e.g., real-world artificial neural network (ANN) training problems). To resolve this issue, this paper proposes a framework based on an efficient elite centroid operator. It continuously monitors the current state of the population. Once stagnation is detected, two dedicated operators, centroid-based mutation (CM) and centroid-based crossover (CX), are executed to replace the classical mutation and binomial crossover operations in DE. CM and CX are centred on the elite centroid composed of multiple elite individuals, constituting a framework consisting of elitism centroid-based operations (CMX) to improve the performance of the individuals who fall into stagnation. In CM, elite centroid provide evolutionary direction for stagnant individuals, and in CX, elite plasmoids address the limitation that stagnant individuals can only obtain limited information about the population. The CMX framework is simple enough to easily incorporate into both classically well-known DEs with constant population sizes and state-of-the-art DEs with varying populations. Numerical experiments on benchmark functions show that the proposed CMX method can significantly enhance the classical DE algorithm and its advanced variants in solving the stagnation problem and improving performance.
KW  - Differential evolution; stagnation; centroid-based mutation and crossover

DO  - 10.32604/cmc.2025.063347