A Multi-operator Ensemble LSHADE with Restart and Local Search Mechanisms for Single-objective Optimization

TL;DR

This paper introduces mLSHADE-RL, an advanced ensemble evolutionary algorithm with restart and local search mechanisms, achieving superior results in high-dimensional single-objective optimization tasks.

Contribution

It combines multiple mutation strategies, a restart mechanism, and local search to enhance LSHADE's performance on complex optimization problems.

Findings

Outperforms state-of-the-art algorithms on CEC 2024 benchmark

Effectively avoids local optima through restart mechanism

Achieves high-quality solutions in 30-dimensional problems

Abstract

In recent years, multi-operator and multi-method algorithms have succeeded, encouraging their combination within single frameworks. Despite promising results, there remains room for improvement as only some evolutionary algorithms (EAs) consistently excel across all optimization problems. This paper proposes mLSHADE-RL, an enhanced version of LSHADE-cnEpSin, which is one of the winners of the CEC 2017 competition in real-parameter single-objective optimization. mLSHADE-RL integrates multiple EAs and search operators to improve performance further. Three mutation strategies such as DE/current-to-pbest-weight/1 with archive, DE/current-to-pbest/1 without archive, and DE/current-to-ordpbest-weight/1 are integrated in the original LSHADE-cnEpSin. A restart mechanism is also proposed to overcome the local optima tendency. Additionally, a local search method is applied in the later phase of the evolutionary procedure to enhance the exploitation capability of mLSHADE-RL. mLSHADE-RL is tested on 30 dimensions in the CEC 2024 competition on single objective bound constrained optimization, demonstrating superior performance over other state-of-the-art algorithms in producing high-quality solutions across various optimization scenarios.