Hybrid Firefly-Genetic Algorithm for Single and Multi-dimensional 0-1 Knapsack Problems

• URL: http://arxiv.org/abs/2501.14775v1
• Date: Tue, 31 Dec 2024 08:03:15 GMT
• Title: Hybrid Firefly-Genetic Algorithm for Single and Multi-dimensional 0-1 Knapsack Problems
• Authors: Aswathi Malanthara, Ishaan R Kale,
• Abstract summary: The paper addresses the challenges faced by algorithms, such as the Firefly Algorithm (FA) and the Genetic Algorithm (GA) The hybrid algorithm is validated by solving unconstrained benchmark functions and constrained optimization problems. The proposed algorithm delivers improved solution accuracy and computational efficiency compared to conventional optimization algorithm.
• Score: 0.0
• License:
• Abstract: This paper addresses the challenges faced by algorithms, such as the Firefly Algorithm (FA) and the Genetic Algorithm (GA), in constrained optimization problems. While both algorithms perform well for unconstrained problems, their effectiveness diminishes when constraints are introduced due to limitations in exploration, exploitation, and constraint handling. To overcome these challenges, a hybrid FAGA algorithm is proposed, combining the strengths of both algorithms. The hybrid algorithm is validated by solving unconstrained benchmark functions and constrained optimization problems, including design engineering problems and combinatorial problems such as the 0-1 Knapsack Problem. The proposed algorithm delivers improved solution accuracy and computational efficiency compared to conventional optimization algorithm. This paper outlines the development and structure of the hybrid algorithm and demonstrates its effectiveness in handling complex optimization problems.

Related papers

• Benchmarking Randomized Optimization Algorithms on Binary, Permutation, and Combinatorial Problem Landscapes [8.337399973715396]
  We evaluate the performance of four randomized optimization algorithms: Randomized Hill Climbing (RHC), Simulated Annealing (SA), Genetic Algorithms (GA), and MIMIC. We compare these algorithms using a set of benchmark fitness functions that highlight the specific challenges and requirements of each problem category. Our study analyzes each algorithm's effectiveness based on key performance metrics, including solution quality, convergence speed, computational cost, and robustness.
  arXiv  Detail & Related papers  (2025-01-21T23:13:01Z)
• 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)
• 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)
• Hybrid ACO-CI Algorithm for Beam Design problems [0.4397520291340694]
  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.
  arXiv  Detail & Related papers  (2023-03-29T04:37:14Z)
• A socio-physics based hybrid metaheuristic for solving complex non-convex constrained optimization problems [0.19662978733004596]
  It is necessary to critically validate the proposed constrained optimization techniques. The search is different as it involves a large number of linear constraints and non-type inequality. The first CI-based algorithm incorporates a self-adaptive penalty approach. The second algorithm combines CI-SAPF with the referred properties of the future.
  arXiv  Detail & Related papers  (2022-09-02T07:46:46Z)
• A Constrained Optimization Approach to Bilevel Optimization with Multiple Inner Minima [49.320758794766185]
  We propose a new approach, which convert the bilevel problem to an equivalent constrained optimization, and then the primal-dual algorithm can be used to solve the problem. Such an approach enjoys a few advantages including (a) addresses the multiple inner minima challenge; (b) fully first-order efficiency without Jacobian computations.
  arXiv  Detail & Related papers  (2022-03-01T18:20:01Z)
• 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)
• 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)
• 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)
• 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)
• Quantum approximate algorithm for NP optimization problems with constraints [12.294570891467604]
  In this paper, we formalize different constraint types to equalities, linear inequalities, and arbitrary form. Based on this, we propose constraint-encoding schemes well-fitting into the QAOA framework for solving NP optimization problems. The implemented algorithms demonstrate the effectiveness and efficiency of the proposed scheme.
  arXiv  Detail & Related papers  (2020-02-01T04:45:41Z)

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.