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Chase, Pounce, and Escape Optimization Algorithm

Adel Sabry Eesa*

Computer Science Department, Faculty of Science, University of Zakho, Duhok City, 42001, Iraq

* Corresponding Author: Adel Sabry Eesa. Email: email

(This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud/Fog/Edge)

Intelligent Automation & Soft Computing 2024, 39(4), 697-723. https://doi.org/10.32604/iasc.2024.053192

Abstract

While many metaheuristic optimization algorithms strive to address optimization challenges, they often grapple with the delicate balance between exploration and exploitation, leading to issues such as premature convergence, sensitivity to parameter settings, and difficulty in maintaining population diversity. In response to these challenges, this study introduces the Chase, Pounce, and Escape (CPE) algorithm, drawing inspiration from predator-prey dynamics. Unlike traditional optimization approaches, the CPE algorithm divides the population into two groups, each independently exploring the search space to efficiently navigate complex problem domains and avoid local optima. By incorporating a unique search mechanism that integrates both the average of the best solution and the current solution, the CPE algorithm demonstrates superior convergence properties. Additionally, the inclusion of a pouncing process facilitates rapid movement towards optimal solutions. Through comprehensive evaluations across various optimization scenarios, including standard test functions, Congress on Evolutionary Computation (CEC)-2017 benchmarks, and real-world engineering challenges, the effectiveness of the CPE algorithm is demonstrated. Results consistently highlight the algorithm’s performance, surpassing that of other well-known optimization techniques, and achieving remarkable outcomes in terms of mean, best, and standard deviation values across different problem domains, underscoring its robustness and versatility.

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Cite This Article

APA Style
Eesa, A.S. (2024). Chase, pounce, and escape optimization algorithm. Intelligent Automation & Soft Computing, 39(4), 697-723. https://doi.org/10.32604/iasc.2024.053192
Vancouver Style
Eesa AS. Chase, pounce, and escape optimization algorithm. Intell Automat Soft Comput . 2024;39(4):697-723 https://doi.org/10.32604/iasc.2024.053192
IEEE Style
A.S. Eesa, “Chase, Pounce, and Escape Optimization Algorithm,” Intell. Automat. Soft Comput. , vol. 39, no. 4, pp. 697-723, 2024. https://doi.org/10.32604/iasc.2024.053192



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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