GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering Challenges and Beyond

• URL: http://arxiv.org/abs/2307.10420v3
• Date: Wed, 16 Oct 2024 21:04:57 GMT
• Title: GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering Challenges and Beyond
• Authors: Rebwar Khalid Hamad, Tarik A. Rashid,
• Abstract summary: The GOOSE algorithm is benchmarked on 19 well-known test functions. The proposed algorithm is tested on 10 modern benchmark functions. The achieved findings attest to the proposed algorithm's superior performance.
• Score: 4.939986309170004
• License:
• Abstract: This study proposes the GOOSE algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other individuals in the flock. The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness dependent optimizer (FDO). In addition, the proposed algorithm is tested on 10 modern benchmark functions, and the gained results are compared with three recent algorithms, such as the dragonfly algorithm, whale optimization algorithm (WOA), and salp swarm algorithm (SSA). Moreover, the GOOSE algorithm is tested on 5 classical benchmark functions, and the obtained results are evaluated with six algorithms, such as fitness dependent optimizer (FDO), FOX optimizer, butterfly optimization algorithm (BOA), whale optimization algorithm, dragonfly algorithm, and chimp optimization algorithm (ChOA). The achieved findings attest to the proposed algorithm's superior performance compared to the other algorithms that were utilized in the current study. The technique is then used to optimize Welded beam design and Economic Load Dispatch Problem, three renowned real-world engineering challenges, and the Pathological IgG Fraction in the Nervous System. The outcomes of the engineering case studies illustrate how well the suggested approach can optimize issues that arise in the real-world.

Related papers

• BMR and BWR: Two simple metaphor-free optimization algorithms for solving real-life non-convex constrained and unconstrained problems [0.5755004576310334]
  Two simple yet powerful optimization algorithms, named the Best-MeanRandom (BMR) and Best-Worst-Random (BWR) algorithms, are developed and presented in this paper.
  arXiv  Detail & Related papers  (2024-07-15T18:11:47Z)
• 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)
• Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization [63.8557841188626]
  The expected improvement (EI) algorithm is one of the most popular strategies for optimization under uncertainty. We propose a variant of EI with a standard incumbent defined via the GP predictive mean. We show that our algorithm converges, and achieves a cumulative regret bound of $mathcal O(gamma_TsqrtT)$.
  arXiv  Detail & Related papers  (2022-03-15T13:17:53Z)
• Duck swarm algorithm: theory, numerical optimization, and applications [6.244015536594532]
  A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm (DSA), is proposed in this study. Two rules are modeled from the finding food and foraging of the duck, which corresponds to the exploration and exploitation phases of the proposed DSA. Results show that DSA is a high-performance optimization method in terms of convergence speed and exploration-exploitation balance.
  arXiv  Detail & Related papers  (2021-12-27T04:53:36Z)
• ANA: Ant Nesting Algorithm for Optimizing Real-World Problems [21.95618652596178]
  A novel intelligent swarm is proposed called ant nesting algorithm (ANA) The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change.
  arXiv  Detail & Related papers  (2021-12-04T08:55:06Z)
• An Accelerated Variance-Reduced Conditional Gradient Sliding Algorithm for First-order and Zeroth-order Optimization [111.24899593052851]
  Conditional gradient algorithm (also known as the Frank-Wolfe algorithm) has recently regained popularity in the machine learning community. ARCS is the first zeroth-order conditional gradient sliding type algorithms solving convex problems in zeroth-order optimization. In first-order optimization, the convergence results of ARCS substantially outperform previous algorithms in terms of the number of gradient query oracle.
  arXiv  Detail & Related papers  (2021-09-18T07:08:11Z)
• Provably Faster Algorithms for Bilevel Optimization [54.83583213812667]
  Bilevel optimization has been widely applied in many important machine learning applications. We propose two new algorithms for bilevel optimization. We show that both algorithms achieve the complexity of $mathcalO(epsilon-1.5)$, which outperforms all existing algorithms by the order of magnitude.
  arXiv  Detail & Related papers  (2021-06-08T21:05:30Z)
• Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information [78.78486761923855]
  In many real world problems, we want to infer some property of an expensive black-box function f, given a budget of T function evaluations. We present a procedure, InfoBAX, that sequentially chooses queries that maximize mutual information with respect to the algorithm's output. On these problems, InfoBAX uses up to 500 times fewer queries to f than required by the original algorithm.
  arXiv  Detail & Related papers  (2021-04-19T17:22:11Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• A survey on dragonfly algorithm and its applications in engineering [29.190512851078218]
  The dragonfly algorithm was developed in 2016. It is one of the algorithms used by researchers to optimize an extensive series of uses and applications in various areas. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems.
  arXiv  Detail & Related papers  (2020-02-19T20:23:26Z)
• 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.