BMR and BWR: Two simple metaphor-free optimization algorithms for solving real-life non-convex constrained and unconstrained problems
- URL: http://arxiv.org/abs/2407.11149v1
- Date: Mon, 15 Jul 2024 18:11:47 GMT
- Title: BMR and BWR: Two simple metaphor-free optimization algorithms for solving real-life non-convex constrained and unconstrained problems
- Authors: Ravipudi Venkata Rao, Ravikumar shah,
- Abstract summary: This paper presents two simple yet powerful optimization algorithms named Best-Mean-Random (BMR) and Best-Worst-Randam (BWR)
The BMR algorithm is based on the best, mean random solutions of the population generated for solving the problem.
The BWR algorithm is based on the best, worst, and random solutions.
- Score: 0.5755004576310334
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents two simple yet powerful optimization algorithms named Best-Mean-Random (BMR) and Best-Worst-Randam (BWR) algorithms to handle both constrained and unconstrained optimization problems. These algorithms are free of metaphors and algorithm-specific parameters. The BMR algorithm is based on the best, mean, and random solutions of the population generated for solving a given problem; and the BWR algorithm is based on the best, worst, and random solutions. The performances of the proposed two algorithms are investigated by implementing them on 26 real-life non-convex constrained optimization problems given in the Congress on Evolutionary Computation (CEC) 2020 competition and comparisons are made with those of the other prominent optimization algorithms. Furthermore, computational experiments are conducted on 30 unconstrained standard benchmark optimization problems including 5 recently developed benchmark problems having distinct characteristics. The results proved the better competitiveness and superiority of the proposed simple algorithms. The optimization research community may gain an advantage by adapting these algorithms to solve various constrained and unconstrained real-life optimization problems across various scientific and engineering disciplines.
Related papers
- Dynamic Incremental Optimization for Best Subset Selection [15.8362578568708]
Best subset selection is considered the gold standard for many learning problems.
An efficient subset-dual algorithm is developed based on the primal and dual problem structures.
arXiv Detail & Related papers (2024-02-04T02:26:40Z) - GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering
Challenges and Beyond [1.1802674324027231]
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.
arXiv Detail & Related papers (2023-07-19T19:14:25Z) - 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) - 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) - 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) - Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex
Decentralized Optimization Over Time-Varying Networks [79.16773494166644]
We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network.
We design two optimal algorithms that attain these lower bounds.
We corroborate the theoretical efficiency of these algorithms by performing an experimental comparison with existing state-of-the-art methods.
arXiv Detail & Related papers (2021-06-08T15:54:44Z) - PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective
Optimization Problems [0.0]
The proposed algorithm is based on solving a set of surrogate problems defined by models of the real one.
Our algorithm also performs a meta-search for optimal surrogate models and navigation strategies for the optimization landscape.
arXiv Detail & Related papers (2021-03-19T11:18:03Z) - Optimizing Optimizers: Regret-optimal gradient descent algorithms [9.89901717499058]
We study the existence, uniqueness and consistency of regret-optimal algorithms.
By providing first-order optimality conditions for the control problem, we show that regret-optimal algorithms must satisfy a specific structure in their dynamics.
We present fast numerical methods for approximating them, generating optimization algorithms which directly optimize their long-term regret.
arXiv Detail & Related papers (2020-12-31T19:13:53Z) - Convergence of adaptive algorithms for weakly convex constrained
optimization [59.36386973876765]
We prove the $mathcaltilde O(t-1/4)$ rate of convergence for the norm of the gradient of Moreau envelope.
Our analysis works with mini-batch size of $1$, constant first and second order moment parameters, and possibly smooth optimization domains.
arXiv Detail & Related papers (2020-06-11T17:43:19Z) - 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) - Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms.
We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
arXiv Detail & Related papers (2020-01-29T09:40:58Z)
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.