In Search of Excellence: SHOA as a Competitive Shrike Optimization Algorithm for Multimodal Problems

• URL: http://arxiv.org/abs/2407.06219v1
• Date: Tue, 9 Jul 2024 11:19:37 GMT
• Title: In Search of Excellence: SHOA as a Competitive Shrike Optimization Algorithm for Multimodal Problems
• Authors: Hanan K. AbdulKarim, Tarik A. Rashid,
• Abstract summary: 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.
• Score: 4.939986309170004
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, a swarm intelligence optimization algorithm is proposed as the Shrike Optimization Algorithm (SHOA). Many creatures living in a group and surviving for the next generation randomly search for food; they follow the best one in the swarm, called swarm intelligence. Swarm-based algorithms are designed to mimic creatures' behaviours, but in multimodal problem competition, they cannot find optimal solutions in some difficult cases. The main inspiration for the proposed algorithm is taken from the swarming behaviours of shrike birds in nature. The shrike birds are migrating from their territory to survive. However, the SHOA mimics the surviving behaviour of shrike birds for living, adaptation, and breeding. Two parts of optimization exploration and exploitation are designed by modelling shrike breeding and searching for foods to feed nestlings until they get ready to fly and live independently. This paper is a mathematical model for the SHOA to perform optimization. The SHOA benchmarked 19 well-known mathematical test functions, 10 from CEC-2019, and 12 from CEC-2022 most recent test functions, a total of 41 competitive mathematical test functions benchmarked and four real-world engineering problems with different conditions, both constrained and unconstrained. The statistical results obtained from the Wilcoxon sum ranking and Fridman test show that SHOA has a significant statistical superiority in handling the test benchmarks compared to competitor algorithms in multi-modal problems. The results for engineering optimization problems show the SHOA outperforms other nature-inspired algorithms in many cases.

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)
• Optimistic Active Exploration of Dynamical Systems [52.91573056896633]
  We develop an algorithm for active exploration called OPAX. We show how OPAX can be reduced to an optimal control problem that can be solved at each episode. Our experiments show that OPAX is not only theoretically sound but also performs well for zero-shot planning on novel downstream tasks.
  arXiv  Detail & Related papers  (2023-06-21T16:26:59Z)
• 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)
• Introductory Studies of Swarm Intelligence Techniques [1.2930503923129208]
  Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.
  arXiv  Detail & Related papers  (2022-09-26T16:29:55Z)
• Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization [73.96101108943986]
  A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. We present a Zooming Memory-Based Initialization algorithm that builds on conventional Bayesian optimization principles.
  arXiv  Detail & Related papers  (2022-08-26T23:57:41Z)
• Non-Elitist Selection Can Improve the Performance of Irace [0.8258451067861933]
  We study two alternative selection methods for tuning ant colony optimization algorithms for traveling salesperson problems and the quadratic assignment problem. The experimental results show improvement on the tested benchmarks compared to the default selection of irace. In addition, the obtained results indicate that non-elitist can obtain diverse algorithm configurations, which encourages us to explore a wider range of solutions to understand the behavior of algorithms.
  arXiv  Detail & Related papers  (2022-03-17T10:34:30Z)
• Duck swarm algorithm: theory, numerical optimization, and applications [6.244015536594532]
  A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm (DSA), is proposed in this study. Two rules are modeled from the finding food and foraging of the duck, which corresponds to the exploration and exploitation phases of the proposed DSA. Results show that DSA is a high-performance optimization method in terms of convergence speed and exploration-exploitation balance.
  arXiv  Detail & Related papers  (2021-12-27T04:53:36Z)
• The Archerfish Hunting Optimizer: a novel metaheuristic algorithm for global optimization [0.8315801422499861]
  Global optimization solves real-world problems numerically or analytically by minimizing their objective functions. We propose a global metahistic algorithm based on the Archerfish Hunting (AHO)
  arXiv  Detail & Related papers  (2021-02-03T16:22:31Z)
• 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)
• AdaLead: A simple and robust adaptive greedy search algorithm for sequence design [55.41644538483948]
  We develop an easy-to-directed, scalable, and robust evolutionary greedy algorithm (AdaLead) AdaLead is a remarkably strong benchmark that out-competes more complex state of the art approaches in a variety of biologically motivated sequence design challenges.
  arXiv  Detail & Related papers  (2020-10-05T16:40:38Z)
• Self-Directed Online Machine Learning for Topology Optimization [58.920693413667216]
  Self-directed Online Learning Optimization integrates Deep Neural Network (DNN) with Finite Element Method (FEM) calculations. Our algorithm was tested by four types of problems including compliance minimization, fluid-structure optimization, heat transfer enhancement and truss optimization. It reduced the computational time by 2 5 orders of magnitude compared with directly using methods, and outperformed all state-of-the-art algorithms tested in our experiments.
  arXiv  Detail & Related papers  (2020-02-04T20:00:28Z)

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.