Dropout-Inspired Strategies For Enhanced Exploration In Evolutionary Algorithms

RLDMGO: a reinforcement learning-driven hybrid framework for complex optimization problems

A reinforcement-learning-guided memetic Narwhal Optimization Algorithm for global and engineering optimization

The Narwhal Optimization Algorithm is a recent swarm metaheuristic that, like most population-based optimisers, is prone to premature convergence, is sensitive to random initialisation, and relies on a rigid, schedule-driven exploration–exploitation balance. This paper develops and rigorously evaluates two enhanced variants that address these weaknesses. NWOA-OBL adds opposition-based initialisation and a stagnation-triggered, dynamic-opposition restart that replenishes population diversity, while NWOA-RL replaces the fixed exploration ratio with a Q-learning controller that selects the search behaviour online from the observed progress of the optimisation. Both variants are made memetic through a shared elite local search that supplies the local-refinement drive the original wave-based moves lack. The variants are compared against the baseline algorithm and seven established and recent optimisers on the CEC2017 suite at dimension thirty and the CEC2022 suite at dimensions ten and twenty, on six constrained engineering-design problems, and through parameter-sensitivity and ablation studies, all under a common evaluation budget with thirty independent runs and full nonparametric statistical analysis. Pooled over the benchmark functions, NWOA-RL attains the joint-best mean rank, statistically indistinguishable from the strongest competitor and significantly ahead of the remaining baselines, and reaches near-optimal engineering designs. The ablation identifies the elite local search as the decisive component of the design.

Exploring Elitism Strategies in Nested Tournament Selection for Multi-Objective Genetic Programming

This study systematically investigates multiple elitism mechanisms for NT within tree-based MO Genetic Programming (MOGP), including NSGA-II population replacement, crowding distance, first-objective, and a novel ideal-point strategy, comparing them against non-elitist NT and standard NSGA-II.

Hot off the Press: Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms

Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually performed in a greedy, deterministic manner. In this paper, we analytically present that stochastic population update can be beneficial for the search of MOEAs. Specifically, we prove that the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, for solving two bi-objective problems, OneJumpZeroJump and bi-objective RealRoyalRoad, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed population update method. This work is an attempt to show the benefit of introducing randomness into the population update of MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area. This paper for the Hot-off-the-Press track at GECCO 2025 summarizes the work C. Bian, Y. Zhou, M. Li, and C. Qian. Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms. Artificial Intelligence, 2025, 341: 104308. [5]

MDLPSO-DE: a memory-driven distribution learning hybrid swarm optimizer with application to humanistic learning path optimization

Particle swarm optimization (PSO) tends to suffer from premature convergence and limited adaptability when addressing high-dimensional and complex multimodal optimization problems. To alleviate these issues, a hybrid framework termed MDLPSO-DE is proposed by integrating memory-driven distribution learning with differential evolution and adaptive operator selection. An archive mechanism is employed to retain high-quality historical solutions and provide stable evolutionary guidance. A rank-weighted distribution model constructed from elite individuals is used to enhance exploitation while maintaining population diversity. A differential evolution mutation operator is incorporated into the velocity–position update process to improve global search capability. Furthermore, operator activation probabilities are dynamically adjusted according to their historical contribution, enabling adaptive coordination between exploration and exploitation during different search stages. The proposed method is evaluated on the CEC2017 benchmark suite under multiple dimensional settings. Comparative results indicate that MDLPSO-DE achieves improved optimization accuracy and convergence behavior relative to several representative swarm and evolutionary algorithms. Statistical analysis based on the Friedman test and Holm-corrected Wilcoxon signed-rank tests confirms that the performance advantage of MDLPSO-DE is statistically significant across the majority of benchmark functions and baseline algorithms. Ablation experiments are conducted to examine the contribution of each module. An application-inspired learning path optimization case study is further used to demonstrate the applicability of the proposed method under structured and constrained optimization settings.

Hot off the Press: A Theoretical Perspective on Why Stochastic Population Update Needs an Archive in Evolutionary Multi-objective Optimization

Evolutionary algorithms (EAs) are popular for multi-objective optimization due to their population-based nature. While population updates in multi-objective EAs (MOEAs) are typically greedy and deterministic. However, recent studies have questioned this practice and shown that stochastic population update (SPU), which allows inferior solutions have a chance to be preserved, can help MOEAs jump out of local optima more easily. Nevertheless, SPU risks losing high-quality solutions, potentially requiring a large population. Intuitively, a possible solution to this issue is to introduce an archive that stores the best solutions ever found. This paper theoretically demonstrates that incorporating an archive to store best-found solutions enables smaller populations and enhances SPU-based MOEA performance. Analyzing SMS-EMOA and NSGA-II on the bi-objective OneJumpZeroJump problem, we prove archives reduce expected running time upper bounds (even exponentially). The comparison between SMS-EMOA and NSGA-II also suggests that the (μ + μ) update mode may be more suitable for SPU than the (μ + 1) update mode. We also validate our findings empirically. This paper for the Hot-off-the-Press track at GECCO 2026 sum marizes the work S. Ren, Z. Liang, M. Li, and C. Qian. A Theoretical Perspective on Why Stochastic Population Update Needs an Archive in Evolutionary Multi-objective Optimization. IJCAI, 2025, 8921: 8929. [17]