Fig Tree-Wasp Symbiotic Coevolutionary Optimization Algorithm

• URL: http://arxiv.org/abs/2503.09340v1
• Date: Wed, 12 Mar 2025 12:35:33 GMT
• Title: Fig Tree-Wasp Symbiotic Coevolutionary Optimization Algorithm
• Authors: Anand J Kulkarni, Isha Purnapatre, Apoorva S Shastri,
• Abstract summary: Fig Tree-Wasp Symbiotic Coevolutionary (FWSC) algorithm is proposed.<n>It models the symbiotic coevolutionary relationship between fig trees and wasps.<n>The algorithm is successfully tested on a variety of test problems.
• Score: 0.08571111167616165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The nature inspired algorithms are becoming popular due to their simplicity and wider applicability. In the recent past several such algorithms have been developed. They are mainly bio-inspired, swarm based, physics based and socio-inspired; however, the domain based on symbiotic relation between creatures is still to be explored. A novel metaheuristic optimization algorithm referred to as Fig Tree-Wasp Symbiotic Coevolutionary (FWSC) algorithm is proposed. It models the symbiotic coevolutionary relationship between fig trees and wasps. More specifically, the mating of wasps, pollinating the figs, searching for new trees for pollination and wind effect drifting of wasps are modeled in the algorithm. These phenomena help in balancing the two important aspects of exploring the search space efficiently as well as exploit the promising regions. The algorithm is successfully tested on a variety of test problems. The results are compared with existing methods and algorithms. The Wilcoxon Signed Rank Test and Friedman Test are applied for the statistical validation of the algorithm performance. The algorithm is also further applied to solve the real-world engineering problems. The performance of the FWSC underscored that the algorithm can be applied to wider variety of real-world problems.

Related papers

• LiteSearch: Efficacious Tree Search for LLM [70.29796112457662]
  This study introduces a novel guided tree search algorithm with dynamic node selection and node-level exploration budget. Experiments conducted on the GSM8K and TabMWP datasets demonstrate that our approach enjoys significantly lower computational costs compared to baseline methods.
  arXiv  Detail & Related papers  (2024-06-29T05:14:04Z)
• Nature Inspired Evolutionary Swarm Optimizers for Biomedical Image and Signal Processing -- A Systematic Review [0.0]
  The paper reviews 28 latest peer-reviewed relevant articles and 26 nature-inspired algorithms. It segregates them into thoroughly explored, lesser explored and unexplored categories intending to help readers understand the reliability and exploration stage of each of these algorithms.
  arXiv  Detail & Related papers  (2023-10-02T04:52:46Z)
• HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection [75.84584400866254]
  We propose a new algorithm selector leveraging special forests, combining the strengths of both approaches while alleviating their weaknesses. HARRIS' decisions are based on a forest model, whose trees are created based on optimized on a hybrid ranking and regression loss function.
  arXiv  Detail & Related papers  (2022-10-31T14:06:11Z)
• 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)
• Waypoint Planning Networks [66.72790309889432]
  We propose a hybrid algorithm based on LSTMs with a local kernel - a classic algorithm such as A*, and a global kernel using a learned algorithm. We compare WPN against A*, as well as related works including motion planning networks (MPNet) and value networks (VIN) It is shown that WPN's search space is considerably less than A*, while being able to generate near optimal results.
  arXiv  Detail & Related papers  (2021-05-01T18:02:01Z)
• Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithms [69.87173070473717]
  We investigate the properties of two diversity search algorithms, the Novelty Search and the Goal Exploration Process algorithms. The relation to MP algorithms reveals that the smoothness, or lack of smoothness of the mapping between the policy parameter space and the outcome space plays a key role in the search efficiency.
  arXiv  Detail & Related papers  (2021-04-10T13:52:27Z)
• A bi-level encoding scheme for the clustered shortest-path tree problem in multifactorial optimization [1.471992435706872]
  The Clustered Shortest-Path Tree Problem (CluSPT) plays an important role in various types of optimization problems in real-life. Recently, some Multifactorial Evolutionary Algorithm (MFEA) have been introduced to deal with the CluSPT. This paper describes a MFEA-based approach to solve the CluSPT.
  arXiv  Detail & Related papers  (2021-02-12T13:36:07Z)
• Nature-Inspired Optimization Algorithms: Research Direction and Survey [0.0]
  Nature-inspired algorithms are commonly used for solving the various optimization problems. We classify nature-inspired algorithms as natural evolution based, swarm intelligence based, biological based, science based and others. The purpose of this review is to present an exhaustive analysis of various nature-inspired algorithms based on its source of inspiration, basic operators, control parameters, features, variants and area of application where these algorithms have been successfully applied.
  arXiv  Detail & Related papers  (2021-02-08T06:03:36Z)
• 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)
• Efficient Computation of Expectations under Spanning Tree Distributions [67.71280539312536]
  We propose unified algorithms for the important cases of first-order expectations and second-order expectations in edge-factored, non-projective spanning-tree models. Our algorithms exploit a fundamental connection between gradients and expectations, which allows us to derive efficient algorithms.
  arXiv  Detail & Related papers  (2020-08-29T14:58:26Z)
• A Heuristic Based on Randomized Greedy Algorithms for the Clustered Shortest-Path Tree Problem [2.099922236065961]
  This paper introduces a new algorithm that combines the major features of RGAs and Shortest Path Tree Algorithm (SPTA) to deal with the Clustered Shortest-Path Tree Problem (CluSPT) To evaluate the performance of the proposed algorithm, Euclidean benchmarks are selected.
  arXiv  Detail & Related papers  (2020-05-05T08:34:58Z)

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.