An Ensemble of Evolutionary Algorithms With Both Crisscross Search and Sparrow Search for Processing Inferior Individuals

TL;DR

This paper tackles stagnation in long-term real-parameter single-objective optimization by enhancing the EA4eig ensemble with two secondary algorithms, crisscross search and sparrow search, to process inferior individuals. EA4eigCS applies these secondary operators selectively to worsen individuals based on stagnation signals, thereby diversifying the population without primarily altering the best solutions. Empirical results on the CEC 2021 and 2022 benchmarks show EA4eigCS is competitive with, and often superior to, state-of-the-art methods and the EA4eig baseline, with ablation studies confirming the value of the two secondary operators and their selective application. This work demonstrates the potential of targeted diversification via secondary evolutionary rules to improve long-term optimization performance, and suggests extending ensembles with additional secondary operators in future work.

Abstract

In the field of artificial intelligence, real parameter single objective optimization is an important direction. Both the Differential Evolution (DE) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) demonstrate good performance for real parameter single objective optimization. Nevertheless, there exist other types of evolutionary algorithm for the purpose. In recent years, researchers begin to study long-term search. EA4eig - an ensemble of three DE variants and CMA-ES - performs well for long-term search. In this paper, we introduce two types of evolutionary algorithm proposed recently - crisscross search and sparrow search - into EA4eig as secondary evolutionary algorithms to process inferior individuals. Thus, EA4eigCS is obtained. In our ensemble, the secondary evolutionary algorithms are expected to vary distribution of the population for breaking stagnation. Experimental results show that our EA4eigCS outperforms EA4eig and is competitive when compared with state-of-the-art algorithms. Code and supplementary material are available at:https://anonymous.4open.science/r/EA4eigCS-2A43.

Figures (1)

• Figure 1: Convergence graph of the eight algorithms for seven functions in the CEC 2022 benchmark test suite when $D=20$