Jul 2026· Proceedings of the Genetic and Evolutionary Computation Conference Companion· 0 citations· 25 references
TL;DR
This work investigates how balancing the number of decomposed subproblems and ants impacts performance under a fixed computational budget and introduces MOEA/D-AM, a hybrid algorithm that adapts this decomposition strategy for partial classification rule induction.
Abstract
Multi-objective evolutionary algorithms based on decomposition (MOEA/D) have established a dominant optimization paradigm successfully extended using Ant Colony Optimization (ACO). However, a rigid architectural assumption within this hybrid framework is the strict assignment of a single ant to evaluate each scalar subproblem, leaving the optimal distribution of computational resources largely unexplored. To address this, we introduce MOEA/D-AM, a hybrid algorithm that adapts this decomposition strategy for partial classification rule induction. By formulating rule induction to optimize multiple objectives simultaneously, we investigate how balancing the number of decomposed subproblems and ants impacts performance under a fixed computational budget. We evaluated five distinct resource configurations across nine benchmark datasets using two global storage strategies, a Pareto archive and a Memory archive. Our analysis reveals significant behavioral differences between these strategies under varying configurations. The findings highlight that the architectural distribution of subproblems and swarm resources fundamentally dictates the model's predictive capabilities. By tuning this trade-off, practitioners can tailor the rule-based classifier to the specific error costs of their domain.
Task scheduling in multi-core systems is a critical NP-hard optimization problem that significantly impacts system performance and resource utilization. This paper proposes a novel hybrid approach combining Bee Colony Optimization (BCO) and Genetic Algorithm (GA) for efficient task scheduling in multi-core processor systems. The hybrid BCO-GA algorithm leverages the global exploration capabilities of BCO and the exploitation strengths of GA to achieve optimal task-to-core assignments while minimizing makespan and balancing system load. The proposed approach incorporates adaptive parameter tuning, elite preservation strategies, and dynamic population management to enhance convergence speed and solution quality. Experimental evaluation using standard benchmark task graphs demonstrates that the hybrid BCO-GA algorithm achieves an average makespan reduction of 18.7% compared to standalone BCO, 15.3% compared to pure GA, and 23.4% compared to the Heterogeneous Earliest Finish Time (HEFT) algorithm. The results also show improved load balancing with a 21.5% reduction in load imbalance factor and 16.8% enhancement in processor utilization. The proposed hybrid approach demonstrates superior performance in handling various task graph characteristics including different Communication-to-Computation Ratios (CCR), task counts, and dependency structures, making it a robust solution for multi-core task scheduling problems.
Igiri C. G, Victor Peters, Igu Ajumoke Elizabeth· Journal of Artificial Intell...· 0 citations
This first complete mathematical runtime analysis for the MOEA/D using the original weighted-sum decomposition shows that this variant of the algorithm solves the classic OneMinMax benchmark considerably faster than both the MOEA/D with Tchebycheff decomposition and many other classic algorithms such as the NSGA-II, NSGA-III, SMS-EMOA, and SPEA2.
Danyang Zhang, Zerong Zhong, Weijie Zheng et al.· Proceedings of the Genetic a...· 0 citations
A systematic mapping study of multi-objective optimization algorithms, tracing their evolution from classical Pareto-based methods toward AI-driven and hybrid approaches, with software testing as the primary application domain, and outlining a research roadmap for the next generation of multi-objective optimization systems that combine the complementary mathematical strengths of neural function approximation and evolutionary diversity preservation.
In evolutionary algorithms (EAs), static operator configurations often depend on human expertise and may lead to search stagnation. Existing adaptive methods either overlook real-time optimization states or require substantial computational overhead. Recent LLM-based methods support adaptive decision-making; however, applying an LLM to a single population requires the model to resolve conflicting exploration and exploitation objectives, which may reduce the reliability of operator selection. To address this limitation, this paper proposes DEAL, a Dual-Pool Evolutionary Algorithm with LLM-Driven Adaptive Operator Selection. DEAL separates the population into an exploitation pool and an exploration pool, enabling the LLM to pursue different search objectives: refining promising solutions in the exploitation pool and exploring alternative candidate structures in the exploration pool. DEAL further incorporates an offline experience collection module and a structured prompt template that jointly provide operator information, population status, and relevant historical evidence. Benchmark experiments on EUC-2D TSP instances show that DEAL reduces the average optimality gap on most tested categories compared with conventional baselines, suggesting the effectiveness of dual-pool LLM-driven operator scheduling.
Teming Qi, Yishun Liu, Chunhua Yang et al.· 2026 IEEE 27th China Confere...· 0 citations
Findings indicate that NSABC is a competitive and statistically supported alternative to NSGA-III for bi-objective facility location problems involving both economic efficiency and balanced customer distribution.
Muhammed Resul Aydın, Mehmet Emin Aydin· Mathematics· 0 citations
This work investigates the hybridization of a Genetic Algorithm with Pareto Local Search to improve the exploration of non-dominated rulesets and provides a documented baseline for hybrid metaheuristics in rule mining.
Evgueni Blanquart, L. Jourdan, Nadarajen Veerapen· Proceedings of the Genetic a...· 0 citations