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

TL;DR

The paper tackles robust single-objective bound-constrained optimization by proposing mLSHADE-RL, a multi-operator ensemble built on LSHADE-cnEpSin that integrates three mutation strategies, a restart mechanism, and a late-stage SQP local search. The approach dynamically selects among mutation operators, adapts $F$ and $CR$ using sinusoidal ensembles and memory-based updates, and employs a restart scheme with horizontal and vertical crossovers to mitigate stagnation, complemented by a local search in the latter phase. Empirical evaluation on the CEC 2024 30-D single-objective benchmark shows that mLSHADE-RL delivers high-quality solutions and competitive or superior performance relative to state-of-the-art algorithms across diverse problem types, with statistical analyses supporting its robustness. The work advances practical optimization by enhancing exploration-exploitation balance, adaptability, and convergence reliability for real-parameter optimization tasks.

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.