Snail Homing and Mating Search Algorithm: A Novel Bio-Inspired Metaheuristic Algorithm

• URL: http://arxiv.org/abs/2310.04020v1
• Date: Fri, 6 Oct 2023 05:18:48 GMT
• Title: Snail Homing and Mating Search Algorithm: A Novel Bio-Inspired Metaheuristic Algorithm
• Authors: Anand J Kulkarni, Ishaan R Kale, Apoorva Shastri, Aayush Khandekar
• Abstract summary: The proposed SHMS algorithm is investigated by solving several unimodal and multimodal functions. The real-world application of SHMS algorithm is successfully demonstrated in the engineering design domain.
• Score: 0.855200588098612
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, a novel Snail Homing and Mating Search (SHMS) algorithm is proposed. It is inspired from the biological behaviour of the snails. Snails continuously travels to find food and a mate, leaving behind a trail of mucus that serves as a guide for their return. Snails tend to navigate by following the available trails on the ground and responding to cues from nearby shelter homes. The proposed SHMS algorithm is investigated by solving several unimodal and multimodal functions. The solutions are validated using standard statistical tests such as two-sided and pairwise signed rank Wilcoxon test and Friedman rank test. The solution obtained from the SHMS algorithm exhibited superior robustness as well as search space exploration capabilities within the less computational cost. The real-world application of SHMS algorithm is successfully demonstrated in the engineering design domain by solving three cases of design and economic optimization shell and tube heat exchanger problem. The objective function value and other statistical results obtained using SHMS algorithm are compared with other well-known metaheuristic algorithms.

Related papers

• ORS: A novel Olive Ridley Survival inspired Meta-heuristic Optimization Algorithm [2.4343652794054487]
  Olive Ridley Survival (ORS) is proposed which is inspired from survival challenges faced by hatchlings of Olive Ridley sea turtle. ORS has two major phases: hatchlings survival through environmental factors and impact of movement trajectory on its survival. To validate the algorithm, fourteen mathematical benchmark functions from standard CEC test suites are evaluated and statistically tested.
  arXiv  Detail & Related papers  (2024-09-13T21:48:05Z)
• In Search of Excellence: SHOA as a Competitive Shrike Optimization Algorithm for Multimodal Problems [4.939986309170004]
  The main inspiration for the proposed algorithm is taken from the swarming behaviours of shrike birds in nature. Two parts of optimization exploration and exploitation are designed by modelling shrike breeding and searching for foods. This paper is a mathematical model for the SHOA to perform optimization.
  arXiv  Detail & Related papers  (2024-07-09T11:19:37Z)
• 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)
• A Three-Phase Artificial Orcas Algorithm for Continuous and Discrete Problems [0.17188280334580192]
  A new swarm intelligence algorithm based on orca behaviors is proposed for problem solving. The originality of the proposal is that for the first time a meta-heuristic simulates simultaneously several behaviors of just one animal species.
  arXiv  Detail & Related papers  (2023-02-17T12:54:37Z)
• Linearized Wasserstein dimensionality reduction with approximation guarantees [65.16758672591365]
  LOT Wassmap is a computationally feasible algorithm to uncover low-dimensional structures in the Wasserstein space. We show that LOT Wassmap attains correct embeddings and that the quality improves with increased sample size. We also show how LOT Wassmap significantly reduces the computational cost when compared to algorithms that depend on pairwise distance computations.
  arXiv  Detail & Related papers  (2023-02-14T22:12:16Z)
• Learning Sampling Policy for Faster Derivative Free Optimization [100.27518340593284]
  We propose a new reinforcement learning based ZO algorithm (ZO-RL) with learning the sampling policy for generating the perturbations in ZO optimization instead of using random sampling. Our results show that our ZO-RL algorithm can effectively reduce the variances of ZO gradient by learning a sampling policy, and converge faster than existing ZO algorithms in different scenarios.
  arXiv  Detail & Related papers  (2021-04-09T14:50:59Z)
• AutoSpace: Neural Architecture Search with Less Human Interference [84.42680793945007]
  Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction. We propose a novel differentiable evolutionary framework named AutoSpace, which evolves the search space to an optimal one. With the learned search space, the performance of recent NAS algorithms can be improved significantly compared with using previously manually designed spaces.
  arXiv  Detail & Related papers  (2021-03-22T13:28:56Z)
• 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)
• Evolutionary Algorithm and Multifactorial Evolutionary Algorithm on Clustered Shortest-Path Tree problem [2.578242050187029]
  Clustered Shortest-Path Tree Problem (CluSPT) is an NP-hard problem. To enhance the performance of the search process, two approaches are proposed.
  arXiv  Detail & Related papers  (2020-10-19T08:37:18Z)
• A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation [71.31905141672529]
  We study the widely adopted ancestral sampling algorithms for auto-regressive language models. We identify three key properties that are shared among them: entropy reduction, order preservation, and slope preservation. We find that the set of sampling algorithms that satisfies these properties performs on par with the existing sampling algorithms.
  arXiv  Detail & Related papers  (2020-09-15T17:28:42Z)

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.