Enhancing Optimization Through Innovation: The Multi-Strategy Improved Black Widow Optimization Algorithm (MSBWOA)

• URL: http://arxiv.org/abs/2312.13395v1
• Date: Wed, 20 Dec 2023 19:55:36 GMT
• Title: Enhancing Optimization Through Innovation: The Multi-Strategy Improved Black Widow Optimization Algorithm (MSBWOA)
• Authors: Xin Xu
• Abstract summary: This paper introduces a Multi-Strategy Improved Black Widow Optimization Algorithm (MSBWOA) It is designed to enhance the performance of the standard Black Widow Algorithm (BW) in solving complex optimization problems. It integrates four key strategies: initializing the population using Tent chaotic mapping to enhance diversity and initial exploratory capability; implementing mutation optimization on the least fit individuals to maintain dynamic population and prevent premature convergence; and adding a random perturbation strategy to enhance the algorithm's ability to escape local optima.
• Score: 11.450701963760817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a Multi-Strategy Improved Black Widow Optimization Algorithm (MSBWOA), designed to enhance the performance of the standard Black Widow Algorithm (BW) in solving complex optimization problems. The proposed algorithm integrates four key strategies: initializing the population using Tent chaotic mapping to enhance diversity and initial exploratory capability; implementing mutation optimization on the least fit individuals to maintain dynamic population and prevent premature convergence; incorporating a non-linear inertia weight to balance global exploration and local exploitation; and adding a random perturbation strategy to enhance the algorithm's ability to escape local optima. Evaluated through a series of standard test functions, the MSBWOA demonstrates significant performance improvements in various dimensions, particularly in convergence speed and solution quality. Experimental results show that compared to the traditional BW algorithm and other existing optimization methods, the MSBWOA exhibits better stability and efficiency in handling a variety of optimization problems. These findings validate the effectiveness of the proposed strategies and offer a new solution approach for complex optimization challenges.

Related papers

• A Nonlinear African Vulture Optimization Algorithm Combining Henon Chaotic Mapping Theory and Reverse Learning Competition Strategy [9.252838762325927]
  The Henon chaotic mapping theory and elite population strategy are proposed to improve the randomness and diversity of the vulture's initial population. The reverse learning competition strategy is designed to expand the discovery fields for the optimal solution. The proposed HWEAVOA is ranked first in all test functions, which is superior to the comparison algorithms in convergence speed, optimization ability, and solution stability.
  arXiv  Detail & Related papers  (2024-03-22T01:20:45Z)
• Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis [5.6787965501364335]
  Black-box optimization problems are common in many real-world applications. These problems require optimization through input-output interactions without access to internal workings. Two widely used gradient-free optimization techniques are employed to address such challenges. This paper aims to elucidate the similarities and differences in the utilization of model uncertainty between these two methods.
  arXiv  Detail & Related papers  (2024-03-21T13:59:19Z)
• Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization [50.38518771642365]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form. This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method. A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
  arXiv  Detail & Related papers  (2023-12-28T23:15:18Z)
• 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)
• Advancements in Optimization: Adaptive Differential Evolution with Diversification Strategy [0.0]
  The study employs single-objective optimization in a two-dimensional space and runs ADEDS on each of the benchmark functions with multiple iterations. ADEDS consistently outperforms standard DE for a variety of optimization challenges, including functions with numerous local optima, plate-shaped, valley-shaped, stretched-shaped, and noisy functions.
  arXiv  Detail & Related papers  (2023-10-02T10:05:41Z)
• An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization [78.36413169647408]
  We study the effectiveness of various ZO optimization methods for optimizing molecular objectives. We show the advantages of ZO sign-based gradient descent (ZO-signGD) We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
  arXiv  Detail & Related papers  (2022-10-27T01:58:10Z)
• Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics [89.24951036534168]
  Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model.
  arXiv  Detail & Related papers  (2022-10-23T22:21:10Z)
• High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series [1.9371782627708491]
  We propose a new high dimensional Bayesian Optimization algorithm combining Recurrent neural networks. The proposed RNN-BO algorithm can solve the optimal control problems in the lower dimension space. We also discuss the impacts of different numbers of the RNN layers and training epochs on the trade-off between solution quality and related computational efforts.
  arXiv  Detail & Related papers  (2022-01-01T08:40:17Z)
• A novel multiobjective evolutionary algorithm based on decomposition and multi-reference points strategy [14.102326122777475]
  Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been regarded as a significantly promising approach for solving multiobjective optimization problems (MOPs) We propose an improved MOEA/D algorithm by virtue of the well-known Pascoletti-Serafini scalarization method and a new strategy of multi-reference points.
  arXiv  Detail & Related papers  (2021-10-27T02:07:08Z)
• EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization [68.8204255655161]
  EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm. Results prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms.
  arXiv  Detail & Related papers  (2020-07-09T10:19:22Z)
• 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.