Hybrid ACO-CI Algorithm for Beam Design problems

• URL: http://arxiv.org/abs/2303.16908v1
• Date: Wed, 29 Mar 2023 04:37:14 GMT
• Title: Hybrid ACO-CI Algorithm for Beam Design problems
• Authors: Ishaan R Kale, Mandar S Sapre, Ayush Khedkar, Kaustubh Dhamankar, Abhinav Anand, Aayushi Singh
• Abstract summary: A novel hybrid version of the Ant colony optimization (ACO) method is developed using the sample space reduction technique of the Cohort Intelligence (CI) algorithm. The proposed work could be investigate for real world applications encompassing domains of engineering, and health care problems.
• Score: 0.4397520291340694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A range of complicated real-world problems have inspired the development of several optimization methods. Here, a novel hybrid version of the Ant colony optimization (ACO) method is developed using the sample space reduction technique of the Cohort Intelligence (CI) Algorithm. The algorithm is developed, and accuracy is tested by solving 35 standard benchmark test functions. Furthermore, the constrained version of the algorithm is used to solve two mechanical design problems involving stepped cantilever beams and I-section beams. The effectiveness of the proposed technique of solution is evaluated relative to contemporary algorithmic approaches that are already in use. The results show that our proposed hybrid ACO-CI algorithm will take lesser number of iterations to produce the desired output which means lesser computational time. For the minimization of weight of stepped cantilever beam and deflection in I-section beam a proposed hybrid ACO-CI algorithm yielded best results when compared to other existing algorithms. The proposed work could be investigate for variegated real world applications encompassing domains of engineering, combinatorial and health care problems.

Related papers

• Performance Evaluation of Evolutionary Algorithms for Analog Integrated Circuit Design Optimisation [0.0]
  An automated sizing approach for analog circuits is presented in this paper. A targeted search of the search space has been implemented using a particle generation function and a repair-bounds function. The algorithms are tuned and modified to converge to a better optimal solution.
  arXiv  Detail & Related papers  (2023-10-19T03:26:36Z)
• Solution to Advanced Manufacturing Process Problems using Cohort Intelligence Algorithm with Improved Constraint Handling Approaches [0.07989135005592125]
  Cohort Intelligence (CI) algorithm is a socio inspired optimization technique which is successfully applied for solving several unconstrained & constrained real-world problems from the domains such as design, manufacturing, supply chain, healthcare, etc.
  arXiv  Detail & Related papers  (2023-10-16T05:40:23Z)
• Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates [49.84541884653309]
  A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms. Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability. We propose a method for accelerating cutting-plane algorithms via reinforcement learning.
  arXiv  Detail & Related papers  (2023-07-17T20:11:56Z)
• Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler [22.08617448389877]
  This paper introduces a setup for benchmarking algorithms for submodular optimization problems. The focus is on the development of iterative search algorithms with the implementation provided and integrated into IOHprofiler. We present a range of submodular optimization problems that have been integrated into IOHprofiler and show how the setup can be used for analyzing and comparing iterative search algorithms in various settings.
  arXiv  Detail & Related papers  (2023-02-02T23:36:23Z)
• OptABC: an Optimal Hyperparameter Tuning Approach for Machine Learning Algorithms [1.6114012813668934]
  OptABC is proposed to help ABC algorithm in faster convergence toward a near-optimum solution. OptABC integrates artificial bee colony algorithm, K-Means clustering, greedy algorithm, and opposition-based learning strategy. Experimental results demonstrate the effectiveness of OptABC compared to existing approaches in the literature.
  arXiv  Detail & Related papers  (2021-12-15T22:33:39Z)
• Amortized Implicit Differentiation for Stochastic Bilevel Optimization [53.12363770169761]
  We study a class of algorithms for solving bilevel optimization problems in both deterministic and deterministic settings. We exploit a warm-start strategy to amortize the estimation of the exact gradient. By using this framework, our analysis shows these algorithms to match the computational complexity of methods that have access to an unbiased estimate of the gradient.
  arXiv  Detail & Related papers  (2021-11-29T15:10:09Z)
• An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem [49.04543375851723]
  Vehicle routing problem (VRP) is a typical discrete optimization problem. Many studies consider learning-based optimization algorithms to solve VRP. This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.
  arXiv  Detail & Related papers  (2021-07-15T02:13:03Z)
• A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems [54.61091936472494]
  We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
  arXiv  Detail & Related papers  (2021-03-10T03:16:12Z)
• CSCF: a chaotic sine cosine firefly Algorithm for practical application problems [0.0]
  Several optimization algorithms namely firefly algorithm, sine cosine algorithm, particle swarm optimization algorithm have few drawbacks such as computational complexity, convergence speed etc. This paper develops a novel Chaotic Sine Cosine Firefly (CSCF) algorithm with numerous variants to solve optimization problems.
  arXiv  Detail & Related papers  (2020-11-20T08:54:28Z)
• IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method [64.15649345392822]
  We introduce a framework for designing primal methods under the decentralized optimization setting where local functions are smooth and strongly convex. Our approach consists of approximately solving a sequence of sub-problems induced by the accelerated augmented Lagrangian method. When coupled with accelerated gradient descent, our framework yields a novel primal algorithm whose convergence rate is optimal and matched by recently derived lower bounds.
  arXiv  Detail & Related papers  (2020-06-11T18:49:06Z)
• 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.