ABCO: Adaptive Bacterial Colony Optimisation

• URL: http://arxiv.org/abs/2505.01320v1
• Date: Fri, 02 May 2025 14:48:14 GMT
• Title: ABCO: Adaptive Bacterial Colony Optimisation
• Authors: Barisi Kogam, Yevgeniya Kovalchuk, Mohamed Medhat Gaber,
• Abstract summary: This paper introduces a new optimisation algorithm, called Adaptive Bacterial Colony optimisation (ABCO)<n>ABCO follows three stages--explore, exploit and reproduce--and is adaptable to meet the requirements of its applications.
• Score: 3.031375888004876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a new optimisation algorithm, called Adaptive Bacterial Colony Optimisation (ABCO), modelled after the foraging behaviour of E. coli bacteria. The algorithm follows three stages--explore, exploit and reproduce--and is adaptable to meet the requirements of its applications. The performance of the proposed ABCO algorithm is compared to that of established optimisation algorithms--particle swarm optimisation (PSO) and ant colony optimisation (ACO)--on a set of benchmark functions. Experimental results demonstrate the benefits of the adaptive nature of the proposed algorithm: ABCO runs much faster than PSO and ACO while producing competitive results and outperforms PSO and ACO in a scenario where the running time is not crucial.

Related papers

• Adam assisted Fully informed Particle Swarm Optimization ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA) [1.024113475677323]
  The Quantum Approximate Optimization Algorithm (QAOA) is a prominent variational algorithm used for solving optimization problems such as the Max-Cut problem.<n>A key challenge in QAOA lies in efficiently identifying suitable parameters that lead to high-quality solutions.
  arXiv  Detail & Related papers  (2025-06-07T13:14:41Z)
• A novel algorithm for optimizing bundle adjustment in image sequence alignment [6.322876598831792]
  This paper introduces a novel algorithm for optimizing the Bundle Adjustment (BA) model in the context of image sequence alignment for cryo-electron tomography.<n>Extensive experiments on both synthetic and real-world datasets were conducted to evaluate the algorithm's performance.
  arXiv  Detail & Related papers  (2024-11-10T03:19:33Z)
• Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling [96.47086913559289]
  gradient-based algorithms are widely used in bilevel optimization. We introduce a without-replacement sampling based algorithm which achieves a faster convergence rate. We validate our algorithms over both synthetic and real-world applications.
  arXiv  Detail & Related papers  (2024-11-07T17:05:31Z)
• The Firefighter Algorithm: A Hybrid Metaheuristic for Optimization Problems [3.2432648012273346]
  The Firefighter Optimization (FFO) algorithm is a new hybrid metaheuristic for optimization problems. To evaluate the performance of FFO, extensive experiments were conducted, wherein the FFO was examined against 13 commonly used optimization algorithms. The results demonstrate that FFO achieves comparative performance and, in some scenarios, outperforms commonly adopted optimization algorithms in terms of the obtained fitness, time taken for exaction, and research space covered per unit of time.
  arXiv  Detail & Related papers  (2024-06-01T18:38:59Z)
• Halfway Escape Optimization: A Quantum-Inspired Solution for General Optimization Problems [6.3816899727206895]
  This paper first proposes the Halfway Escape Optimization (HEO) algorithm, a quantum-inspired metaheuristic designed to address general optimization problems. After the introduction to the HEO mechansims, the study presents a comprehensive evaluation of HEO's performance against extensively-used optimization algorithms. The test of HEO in Pressure Vessel Design and Tubular Column Design infers its feasibility and potential in real-time applications.
  arXiv  Detail & Related papers  (2024-05-05T08:43:07Z)
• Ant Colony Sampling with GFlowNets for Combinatorial Optimization [68.84985459701007]
  Generative Flow Ant Colony Sampler (GFACS) is a novel meta-heuristic method that hierarchically combines amortized inference and parallel search.<n>Our method first leverages Generative Flow Networks (GFlowNets) to amortize a emphmulti-modal prior distribution over solution space.<n>This prior is updated via parallel search in the spirit of Ant Colony Optimization (ACO) leading to the posterior distribution that generates near-optimal solutions.
  arXiv  Detail & Related papers  (2024-03-11T16:26:06Z)
• A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization [53.14532968909759]
  We introduce an efficient single-loop primal-dual block-coordinate algorithm called ALEXR.<n>We establish the convergence rates of ALEXR in both convex and strongly convex cases under smoothness and non-smoothness conditions.<n> Experimental results on GDRO and partial Area Under the ROC Curve for cFCCO demonstrate the promising performance of our algorithm.
  arXiv  Detail & Related papers  (2023-12-04T19:00:07Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers [109.52244418498974]
  We propose a novel textscAdmeta (textbfADouble exponential textbfMov averagtextbfE textbfAdaptive and non-adaptive momentum) framework. We provide two implementations, textscAdmetaR and textscAdmetaS, the former based on RAdam and the latter based on SGDM.
  arXiv  Detail & Related papers  (2023-07-02T18:16:06Z)
• Hybrid Evolutionary Optimization Approach for Oilfield Well Control Optimization [0.0]
  Oilfield production optimization is challenging due to subsurface model complexity and associated non-linearity. This paper presents efficacy of two hybrid evolutionary optimization approaches for well control optimization of a waterflooding operation.
  arXiv  Detail & Related papers  (2021-03-29T13:36:51Z)
• Bilevel Optimization: Convergence Analysis and Enhanced Design [63.64636047748605]
  Bilevel optimization is a tool for many machine learning problems. We propose a novel stoc-efficientgradient estimator named stoc-BiO.
  arXiv  Detail & Related papers  (2020-10-15T18:09:48Z)
• BOSH: Bayesian Optimization by Sampling Hierarchically [10.10241176664951]
  We propose a novel BO routine pairing a hierarchical Gaussian process with an information-theoretic framework to generate a growing pool of realizations. We demonstrate that BOSH provides more efficient and higher-precision optimization than standard BO across synthetic benchmarks, simulation optimization, reinforcement learning and hyper- parameter tuning tasks.
  arXiv  Detail & Related papers  (2020-07-02T07:35:49Z)
• Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization [71.03797261151605]
  Adaptivity is an important yet under-studied property in modern optimization theory. Our algorithm is proved to achieve the best-available convergence for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
  arXiv  Detail & Related papers  (2020-02-13T05:42:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.