Metaheuristic Optimization Algorithms Research

Neueste Paper

• HeurEvo: Agentic Evolution of Hybrid Solver-Augmented Heuristics for Time-Critical Mathematical Optimization

  Feijie Wu, Hugo Barbalho, Konstantina Mellou, Marco Molinaro, Jing Gao, Ishai Menache, Xinzhi Zhang, Sirui Li · 1. Oktober 2026

  Recent advances in agentic heuristic design use AI agents and execution feedback to automate algorithm discovery for challenging optimization problems. In many practical settings, high-quality solutions must be obtained under strict runtime constraints, motivating hybrid approaches that combine prob…

• BiFE: Search-Efficient Discovery of CPU-Only Branching Policies via LLM-based Bi-Fidelity Evolution

  Ce Zhang, Bin Zhang, Zhiwei Xu, Hao Chen, Xinyue Lu, Shanwei Fan, Yingxuan Teng, Guoliang Fan · 30. September 2026

  In branch-and-bound (B&B) for mixed-integer linear programming (MILP), branching variable selection critically impacts efficiency. Existing neural branching policies often require GPU inference, while CPU-efficient symbolic expressions lack the representational capacity for complex logic. Large Lang…

• A practical DIRECT-type algorithm for medium-scale black-box global optimization

  Linas Stripinis, Remigijus Paulavi\v{c}ius · 10. September 2026

  The DIRECT algorithm is a deterministic global optimization method known for its versatility and balanced exploration-exploitation strategy. However, DIRECT-type algorithms are primarily effective for low-dimensional problems and often exhibit slow convergence as dimensionality increases, limiting t…

• Towards Efficient Evaluation of Evolutionary Transfer Optimization: Case Studies on Task-Parameterized Applications

  Yanchen Li, Xiaoming Xue, Kay Chen Tan · 7. September 2026

  As evolutionary transfer optimization (ETO) scales to larger collections of related tasks, problem evaluation can become a major source of runtime growth. This work studies problem-side evaluation scaling in task-parameterized applications and reformulates application-specific serial computations in…

• Rethinking Learnability in Offline Data-driven Optimization

  Chao Qian, Chen-Guang Wang, Rong-Xi Tan, Ke Xue · 2. September 2026

  Black-Box Optimization (BBO) has found broad applications, but evolutionary algorithms and Bayesian optimization face efficiency challenges as real-world BBO problems grow increasingly complex. Data-driven optimization improves the efficiency of BBO algorithms by learning from data. Offline data-dri…

• Coronavirus Optimization Algorithm: A Success-History Adaptive Evolutionary Framework with Archive-Assisted Search and Stagnation Recovery for Global Optimization

  Hari Mohan Pandey · 26. August 2026

  This paper proposes the Coronavirus Optimization Algorithm (COA), a SARS-CoV-2-inspired success-history adaptive evolutionary optimizer for box-constrained continuous global optimization. COA does not model disease transmission; instead, it maps selected coronavirus mechanisms to explicit search ope…

• Mycelial Search: A Graph-Structured Metaheuristic for Continuous Optimisation

  Mohammad Mahdi Dehshibi · 25. August 2026

  Continuous optimisation methods need to balance sharing information and maintaining alternative search directions. In this paper, we introduce Mycelial Search (Myco), a graph-structured metaheuristic designed around active tips, community-weighted flow, adaptive cord plasticity, and anchor-based inj…

• SSPO: Structure-Aware Similarity-Weighted Preference Optimization for Neural Combinatorial Optimization

  Yuanyu Li, Jintao Xu, Zijiang Liu, Yongzhi Qi, Ningxuan Kang, Jianshen Zhang, Wei Qi, Chen Xie, Zuo-Jun Max Shen · 14. August 2026

  Neural combinatorial optimization (NCO) relies on parallel solution sampling for training, yet existing methods fail to fully exploit the rich information latent in a co-sampled solution group. Preference-optimization methods anchor on the single best solution and discard fine-grained quality and st…

• Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks

  Wojciech Zarzecki, Jaros{\l}aw Arabas · 14. August 2026

  Existing global optimization benchmark suites are of a moderate size and are based on a small number of analytical functions that date back even to the 1970s. This causes a risk of biasing the development of global optimization methods. We argue that the tasks related to the black-box adversarial at…

• Adaptive Hybrid Particle Swarm Optimization with Gradient Descent

  Aryan Gurudeo · 13. August 2026

  Gradient injection helps Particle Swarm Optimization (PSO) only when the swarm has identified a basin with smooth local structure, not universally. We propose Adaptive Hybrid PSO (AHPSO), which uses a sigmoid function on swarm diversity to automatically modulate gradient influence: near-zero during …

• Evolving Parallel Algorithm Portfolios via Potential-Aware Instance Generation with LLMs

  Shaofeng Zhang, Shengcai Liu, Zhiyuan Wang, Ke Tang · 10. August 2026

  The Automatic Construction of Portfolios via Large Language Models (LLM-ACP) suffers from poor generalization in practical few-shot scenarios when solving complex combinatorial optimization problems. Instance and algorithm co-evolution frameworks address this by expanding the training dataset with g…

• Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input

  So Nakashima, Tetsuya J. Kobayashi · 4. August 2026

  We investigate Optimization under Input Uncertainty (OIU), in which the input to the objective function, rather than the objective function itself, is subject to uncertainty. OIU appears in manufacturing processes with production tolerance, control of physical systems with actuation noise, Mixture o…

• Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution

  Vojt\v{e}ch Nov\'ak, Ivan Zelinka · 3. August 2026

  Swarm and evolutionary algorithms are usually analyzed as complete procedural systems in which nonlinear selection, replacement, and adaptation obscure simpler structure within candidate generation. This paper introduces an operator--selection factorization that separates objective-independent varia…

• Prioritizing Search Space Regions in the Low Autocorrelation Binary Sequences Problem

  Bla\v{z} P\v{s}eni\v{c}nik, Borko Bo\v{s}kovi\'c, Jan Popi\'c, Janez Brest · 14. Juli 2026

  Low autocorrelation binary sequences problem (LABS) is a hard combinatorial optimization challenge with important applications in communications, signal processing, and satellite navigation. This paper proposes a hybrid search framework that combines Thompson sampling with parallel self-avoiding wal…

• Analysis of Parameter Settings for the Bat Algorithm Using Variance Evolution

  Xin-She Yang, Mehmet Karamanoglu · 30. Juni 2026

  Parameter settings in evolutionary algorithms and metaheuristics are important because such parameter values can influence the performance of algorithms under evaluation. For a given algorithm, there are many different numerical experiments to show that the algorithm can work well in practice; howev…

• Accelerated Stochastic Min-Max Optimization Based on Bias-corrected Momentum

  Haoyuan Cai, Sulaiman A. Alghunaim, Ali H. Sayed · 24. Juni 2026

  Lower-bound analyses for nonconvex strongly-concave minimax optimization problems have shown that stochastic first-order algorithms require at least $\mathcal{O}(\varepsilon^{-4})$ sample complexity to find an $\varepsilon$-stationary point. Some works indicate that this complexity can be improved t…

• Stage-dependent integer-binary encoding in factorization-machine black-box optimization

  Ryo Ogawa, Mayumi Nakano, Yuya Seki, Shu Tanaka · 23. Juni 2026

  Black-box optimization (BBO) deals with problems where objective functions lack explicit analytical forms and are expensive to evaluate. Factorization machine with quadratic-optimization annealing (FMQA) constructs a surrogate model using a factorization machine (FM) and optimizes it with an Ising m…

• Multi-Column RBF Neural Network Using Adaptive and Non-Adaptive Particle Swarm Optimization

  Ammar Hoori, Yuichi Motai · 4. Juni 2026

  The radial basis function neural network (RBFN) trained with a gradient descending algorithm provides an effective fully connected structure in both shallow and deep networks. The error correction (ErrCor), a state-of-the-art gradient-based training method, selects optimal hidden units to improve ac…

• Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization

  Dat Thanh Tran, Van Khu Vu, Yining Ma · 4. Juni 2026

  Neural-guided Ant Colony Optimization (ACO) suffers from a fundamental training-inference misalignment: policies are typically trained to generate static priors (e.g., heatmaps), yet deployed to guide iterative, long-horizon search processes. In this paper, we present DyNACO, a novel framework that …

• From Mean-Field Limits to Semiclassical Concentration: Global Convergence of the Canonical Evolutionary Strategy

  Mat\'ias Neto, Nicol\'as Garay, Luis Mart\'i, Nayat Sanchez-Pi · 1. Juni 2026

  We address the issue of global convergence in stochastic continuous optimization. For that purpose, we formulate the Canonical Evolutionary Strategy (CES) as a controlled mathematical framework to analyze global convergence in evolutionary algorithms via the semiclassical limit of a Schr{\"o}dinger-…

• A Fresh Look at Lamarckian Evolution and the Baldwin Effect

  In\`es Benito, Johannes F. Lutzeyer, Benjamin Doerr · 28. Mai 2026

  Baldwinian and Lamarckian evolution have existed for a long time in evolutionary algorithms (EAs) without ever dominating the academic literature or practical applications. In this work, we use modern empirical and theoretical methods to revisit Lamarckian and Baldwinian evolution and rigorously com…

• Implicit Binarization via Complex Phase Dynamics in Combinatorial Optimization

  Khen Cohen, Mark Glass, Meir Feder, Yaron Oz · 26. Mai 2026

  We introduce a physics-inspired continuous relaxation framework that yields substantially improved solutions for NP-hard combinatorial optimization problems, including Quadratic Unconstrained Binary Optimization (QUBO), binary sparse coding, and planted-solution Ising models. By parameterizing discr…

• Indian Wedding System Optimization (IWSO): A Novel Socially Inspired Metaheuristic with Operational Design and Analysis

  Deepika Saxena, Kishu Gupta, Jitendra Kumar, Jatinder Kumar, Sakshi Patni, Vinaytosh Mishra, Niharika Singh, Ashutosh Kumar Singh · 15. Mai 2026

  This paper presents a novel population-based metaheuristic, Indian Wedding System Optimization (IWSO), inspired by the socio-cultural dynamics of traditional Indian weddings. IWSO models the matchmaking process driven by collaboration among families, candidates, and matchmakers as a guided, selectiv…

• A Family of Quaternion-Valued Differential Evolution Algorithms for Numerical Function Optimization

  Gerardo Altamirano-Gomez, \'Alvaro Gallardo, Carlos Ignacio Hern\'andez Castellanos · 13. Mai 2026

  The numerical optimization of continuous functions is a fundamental task in many scientific and engineering domains, ranging from mechanical design to training of artificial intelligence models. Among the most effective and widely used algorithms for this purpose is Differential Evolution (DE), know…

• Direct From Darwin: Deriving Advanced Optimizers From Evolutionary First Principles

  Daniel Grimmer · 8. Mai 2026

  Evolutionary computation has long promised to deliver both high-performance optimization tools as well as rigorous scientific simulations of Darwinian evolution. However, modern algorithms frequently abandon evolutionary fidelity for physics-inspired heuristics or superficial biological metaphors. T…